How this knowledge base works

Moodbook — Internal Docs Portal (knowledge database)

A single searchable internal documentation site for the Moodbook team, hosted on the shop's own domain: https://moodbook.gr/pages/team-docs (unlisted — share by link). Grouped sidebar TOC + live full-text search. Fully self-contained.

⭐ Standing rule

Every new custom app/feature we build for the e-shop is also added here. When a new tool ships, adding its docs to this portal is part of "done" — this keeps the team self-serve instead of depending on one person.

Files

• build_docs_portal.py — the generator. Reads a curated DOCS list of .md files from across D:\Shopify Coding and emits:
• docs-portal.html — standalone (open locally / offline).
• <theme>/templates/page.team-docs.liquid + <theme>/snippets/team-docs-c*.liquid — the Shopify page (content chunked so each Liquid file stays < ~85 KB, under Shopify's ~100 KB per-file limit). All HTML is wrapped in {% raw %} so the docs' own Liquid examples render literally.
• update_docs.bat — double-click to regenerate → git commit → git pull → git push. Deploy is via the Shopify GitHub integration (the live theme moodbook-theme/main #199828406604 on moodstaff.myshopify.com auto-deploys from the GitHub main branch), so a git push IS the deploy — do NOT shopify theme push.

Add a new app's docs (the whole workflow)

1. Write the tool's doc(s) as .md (README / manual / handoff).
2. Open build_docs_portal.py → add an entry to the DOCS list: ("Group title", [("Display title", "relative/path/to/DOC.md"), ...]) (a path may be a glob, e.g. ".../Module *.md"; missing files are skipped).
3. Double-click update_docs.bat. Done — it's live within ~1 minute.

One-time setup (already done)

A Shopify Page with handle team-docs, assigned the team-docs theme template.

Requirements

pip install markdown (the bat installs it on first run).

Live Preview — README (τεχνικό)

In-house Live Preview — production pipeline (Phase 2)

Batch tooling that turns personalized orders into print-ready covers, replacing the Cloudlift app's server export. The storefront (snippets/live-preview.liquid + assets/live-preview.js) captures each personalization into properties[_preview] (a JSON descriptor) using artwork-relative % geometry. These scripts consume that same descriptor, so what the customer saw == what prints.

Shopify order ──▶ order_to_md.py ──▶ 1 MD per cart item ──▶ stamp.py ──▶ print PDF
                                     (front-matter +        (base variant PDF
                                      ```json descriptor)    from Illustrator)

Requirements

python -m pip install pymupdf pillow "fonttools[woff]" brotli

Fonts are already solved — do not go looking for the originals. Neither PyMuPDF nor Pillow reads .woff2, but WOFF2 is only a compression wrapper, so woff2_to_sfnt.py unwraps the storefront files in woff2/ back into usable TTF/OTF in print-fonts/. All 11 faces are committed and verified to carry Greek (Α α ά ώ ς ϊ ΐ). fonts.json maps each metafield family to its file, and stamp.py / mockup_png.py pick the right one per layer from the descriptor — no --font needed.

Scripts

fetch_orders.py — Shopify → orders.json

python fetch_orders.py --shop moodstaff.myshopify.com --unfulfilled --out out/queue.json

• Admin GraphQL, read_orders. Auth via $SHOPIFY_SHOP/$SHOPIFY_TOKEN or the nearest gitignored export-token.txt. The token is never printed.
• Guards the _preview name collision. Cloudlift writes a property also called _preview holding a PNG URL; ours holds a JSON descriptor. A line item counts as ours only when _preview parses as JSON with a non-empty layers.
• --unfulfilled is the print queue; --name "#7653" pulls one order.

order_to_md.py — order → descriptors

python order_to_md.py --order order.json --out-dir out/ [--covers covers.json]

• order.json — a Shopify Admin API order (or a list). Line items without a _preview property are skipped (non-personalized).
• --covers — optional { "<variantId>": "covers/x.pdf" } map. Falls back to the convention covers/<product>-<variantId>.pdf.
• Emits one self-contained .md per personalized line item: readable front-matter (order, variant, base_pdf, customer_text) + the exact descriptor as a ```json block.

stamp.py — descriptor → stamped PDF

python stamp.py --descriptor out/order_1001_item_1111.md --out final.pdf [--verify] [--png proof.png]

• Reads a .md (json fence + base_pdf from front-matter) or a plain .json (then pass --base).
• Stamps each text layer at its % box, auto-shrinking to fit width (mirrors the browser fitText), with the descriptor's color/weight/alignment/uppercase.
• Draws line by line at explicit baselines on EVERY path, advance = size × leading. Recorded lines are drawn verbatim; without them it wraps the text itself (the same greedy break-word rule as mockup_png.py). insert_textbox is not used at all — it silently ignores lineheight on faces with compressed metrics (Cavafy asked 1.15, got ~0.77 — order #7675).
• Picks the font per layer from the descriptor's font.family via fonts.json. An unknown family falls back loudly and names the substitute; with no usable fallback it refuses rather than print the wrong typeface. --font forces one file for every layer, as a deliberate override.

run_batch.py / print_queue.bat — the whole print run, one command

print_queue.bat                       double-click: today's unfulfilled queue
python run_batch.py --order "#7653"   one order

The single source for every cover, personalised or not. Each mapped line item leaves ready for the printer, so nobody opens Illustrator to fetch a plain cover:

covers.production.json maps each variantId to both variations. A variant with only a custom file blocks non-personalised orders rather than guessing.

⚠ A Cloudlift line item must never be handed a standard cover. It carries personalisation this pipeline cannot reproduce (its _preview is a rendered PNG URL, not a descriptor), so a standard cover would silently drop the customer's text. Those are listed separately in the manifest as manual work.

Output: one PDF per line item, a mockup PNG per personalised item, ALL_COVERS.pdf with every cover merged in manifest order and repeated per quantity — that is the file you print — and MANIFEST.md.

Every personalised item goes through two gates, and there are three outcomes, not two:

The third class is the point. A cover nobody could check is not a cover that failed, and a cover that failed is not one nobody could check — collapsing the two would put «η εκτύπωση δεν συμφωνεί με ό,τι είδε ο πελάτης» in front of the owner for a DNS blip. The closing line says how many were actually compared against the customer's screen, right under how many are ready, because a green result with fewer checks is a worse result.

--no-gate skips the photograph gate; everything is then reported ΧΩΡΙΣ ΠΛΗΡΗ ΕΛΕΓΧΟ, never as passed.

accents.py / text_fixes.py — the customer's own spelling

python accents_selftest.py
python accents.py "Ο γαμος μας"                   # ad hoc, one string
python text_fixes.py "#7721" --word "Στελιος=Στέλιος"
python text_fixes.py "#7670" --as-typed
python text_fixes.py --list

Not a gate on the print. The other three ask whether the cover matches what the customer saw; this asks whether what they saw is what they meant, and only the owner can answer that — so a flagged item comes out ✋ ΠΕΡΙΜΕΝΕΙ ΑΠΟΦΑΣΗ, stamped as .HOLD.pdf, out of ALL_COVERS.pdf, and never ΜΠΛΟΚΑΡΙΣΜΕΝΟ.

One rule, no dictionary: a Greek word of two or more syllables carries exactly one tone. Syllables are counted as maximal runs of vowels, which is a lower bound — two runs is always two syllables, «Μαρια» has two runs and three syllables — so the counter can miss a flag and can never invent one. ALL-CAPS is skipped (Greek drops tones in capitals), as are Latin, digits and emoji.

⚠ The lexicon cannot help and has been checked. All three tables in MOODBOOK_DETECTOR_LINT/lexicon/data/ are stored DE-ACCENTED, so they can say a word exists and never where its tone goes. Nothing here proposes a spelling: the cover carries a person's name.

A decision is a word substitution applied to content AND to every recorded line, so lines stays true and the photograph gate still runs — see text_fixes.py for why a replacement text would re-wrap and reopen #7673. text_fixes.json is gitignored; it holds customers' names.

print_gate.py — the photograph against the print

python print_gate_selftest.py                     # 14 cases + a 152-render sweep
python run_batch_selftest.py                      # the WIRING, against a live witness
python print_gate.py --descriptor d.json --pdf out.pdf --base-pdf cover.pdf \
    --image-url "<the order's _lp_image>"

⚠ Run both. print_gate_selftest.py proves the gate answers correctly; run_batch_selftest.py proves run_batch.py asks it and listens to the answer. Both real defects of this work lived in the second one and were invisible to the first — see HANDOFF.md → Shipped 2026-08-25, second pass. The only check that can contradict layers[].lines. check_orders.py asks whether the transcript describes a layout that could have existed and check_match.py asks whether the PDF and the mockup flow the same way — but when there ARE recorded lines both renderers read them, so a wrong transcript comes out green from both. This one subtracts the cover from the stored capture, subtracts the base artwork from the stamped PDF, and compares the two through one operator (ink.py): which line boxes hold text, and how tall the block of ink is.

⚠ Only an m=dom capture is a witness. m=desc was drawn FROM the descriptor, so its agreeing with it proves nothing — those come back ΧΩΡΙΣ ΠΛΗΡΗ ΕΛΕΓΧΟ. A missing m= is desc; every order before 2026-08-24 is one.

⚠ _lp_image carries the customer's text inside ?v=<base64 descriptor>. Base64 is not redaction. Nothing the gate prints may contain it — only the object key — and a fixture holds it to that, because run_batch.py writes this output straight into MANIFEST.md.

mockup_png.py — descriptor → customer-facing PNG

python mockup_png.py --descriptor out/order_1001_item_1111.md --out out/mockup.png [--width 700]

• Renders what the CUSTOMER saw (text on the square cover image), not the print artwork. Local by default: no hosting, no writes.
• Close, not pixel-identical — Pillow metrics vs the browser's. The pixel-accurate reference stays the _preview_link viewer.
• --upload --order-id <id> pushes it to Shopify Files and points an order metafield of type file_reference at it (that type is what makes the Admin render a thumbnail). Needs write_files/write_orders. A frozen copy — fix the geometry later and the PNG silently keeps the old render.

woff2_to_sfnt.py — storefront WOFF2 → printable TTF/OTF

python woff2_to_sfnt.py --in woff2 --out print-fonts

• Run it after adding a font to woff2/, then add the family to fonts.json.
• Reports Greek coverage per face, so a subsetted upload is visible instead of dropping characters at print time.
• --verify re-extracts the stamped text and prints its measured % center vs the descriptor box center (dx/dy should be ~0). --png writes a raster proof.

Descriptor schema (one per cart item)

{
  "v": 1, "product": "our_wedding", "variantId": 456,
  "base": "https://cdn.shopify.com/.../our_wedding-snow.png",
  "renderedBase": "https://cdn.shopify.com/.../our_wedding-snow.png",
  "layers": [
    { "id": "title", "content": "Στη Μαρία\nκαι στον Γιώργο\nμε αγάπη",
      "lines": ["Στη Μαρία", "και στον Γιώργο", "με αγάπη"],
      "multiline": true,
      "box": { "x": 12, "y": 35, "w": 76, "h": 34 },
      "font": { "family": "Astir", "renderedFamily": "Astir, serif",
                "url": "https://cdn.shopify.com/.../Astir.woff2",
                "sizePct": 60, "weight": 700, "leading": 1.15,
                "color": "#3a2a1a", "align": "center", "transform": "none" } }
  ]
}

lines — the browser's own line breaks (the reason all three agree)

layers[].lines is the list of VISUAL lines the customer actually saw, captured at add-to-cart by walking the overlay's text node with a Range and noting where the character's top edge changes. Print and the mockup draw those lines verbatim instead of re-wrapping.

This exists because the three renderers measure text differently and were disagreeing on real orders: the same dedication came out 5 lines in print and 6 in the mockup, and insert_textbox silently ignored lineheight on faces with compressed metrics (Cavafy asked 1.15, got 0.77, and the lines collided). With lines recorded, no downstream engine makes a flow decision at all — and because stamp.py then draws line by line at explicit baselines, the advance is exactly size × leading, i.e. the value tuned per font in the box-tool.

Capture is skipped while the chosen webfont is still loading, since breaks measured against the fallback face would be wrong (via faceLoaded(), which asks the document.fonts registry for a face at status loaded — not document.fonts.check(), which returns true for a family that was never declared).

⚠️ lines is a hint, not evidence. On order 7673 the recorded lines (["Nef & Dim","","“","Our w","edding”"]) did not match what the customer saw — the same product on a desktop and two phones all rendered “Our wedding” on one line.

lines and font.sizePct are captured together and fail together. On 7673 the recorded size was 22.4% of a 276 pt box = 61.8 pt, at which “Our wedding” measures 352 pt against a 304 pt box — 16% too wide — and five lines need 355 pt of a 276 pt box. No single layout satisfies both, because fitText() had bailed on a box with no size and left the previous fit's sizePct beside breaks measured in an unfitted DOM.

Since 2026-08-21 the storefront refuses to record lines when the fit did not run, or when a break splits a word that would have fitted alone. Downstream, for orders taken before that, the break is repaired rather than dropped — repairLines() in live-preview-cart.js, lines_guard.py in stamp.py, mockup_png.py and order_to_md.py — because dropping leaves the renderer to re-wrap at the same broken sizePct, which on 7673 gives “Our / wedding”, a third layout nobody saw. Repair lands at 52.5 pt against the customer's own 51.2 pt. The print log says ! LINES …, the .md carries a lines_warning, and the order-preview page shows a yellow strip and draws what will print. Left alone: a break inside a word too long to fit any line, and a break after a dash or a slash. See HANDOFF.md → Things that will bite you.

Descriptors without lines (anything captured before this, or refused by the guard) still work — both renderers fall back to their own wrapping, and check_match.py flags them if they diverge. Since 2026-08-24 stamp.py's fallback also draws its own wrap at explicit baselines (advance = size × leading) instead of insert_textbox, so the leading survives on every face: on #7675 the spacing drift went from 33% to 0.0%, and what can still block is a genuine break-point disagreement between the two engines — which is exactly the gate's job.

A BLANK line in the text (typed or recorded) is invisible to PDF text extraction but takes a full line box in both renderers, so the gate compares only the text-bearing lines and divides each measured gap by the line boxes it spans — otherwise a healthy blank read as double leading and blocked real dedications. A genuinely collapsed leading still blocks, blank or not; check_match_selftest.py proves all four directions.

renderedBase and font.renderedFamily are what the browser actually painted — heroImg.currentSrc and getComputedStyle(overlayText).fontFamily — recorded beside base and font.family, which are only what it was told to paint. Nothing renders from them; the order-preview page compares them and warns. The worst failure in this feature renders perfectly: if the personalised cover 404s the hero keeps showing the gallery photo and every health check still passes, so renderedBase is the only record that the customer's name went onto generic artwork. Compare by file, not by string — Liquid emits protocol-relative URLs and currentSrc always resolves to an absolute one.

box and font.sizePct are % of the artwork/page — the single source of truth shared by the Illustrator artboard, the browser preview, and this stamping step. font.sizePct is the FINAL fitted size the customer saw (autofit fill or their stepped size), so print reproduces it exactly. content may contain \n; stamp.py wraps, aligns (font.align), two-axis auto-fits, and vertically centres — matching the storefront overlay exactly. base (cover URL) and font.url (font file) make the descriptor self-contained so the cart page and the order-preview viewer can re-render it without any product context; stamp.py ignores them (it uses the base PDF + a local font file).

Cart & order preview (no image hosting)

The personalised cover is never rasterised or saved — it is composed ad-hoc (base cover image + text overlay) from the descriptor everywhere it appears:

• Cart / drawer / add-to-cart notification — assets/live-preview-cart.js (loaded globally in layout/theme.liquid) overlays the text on the base cover for each line item; click a preview to zoom. Personalised items are detected by the _preview / _preview_base line item properties.
• Order admin verification — each item carries a hidden _preview_link = {shop}/pages/cover-preview?view=cover-preview&d=<base64 descriptor>. The merchant clicks it in the Shopify order and templates/page.cover-preview.liquid re-renders the exact zoomable cover on the shop's own domain. One cover-preview Page total (create it once: a Page whose handle is cover-preview) — it is reused per order via the ?d= query param, so nothing is created per order and there is no storage. The view= param picks the template, so assigning it to the Page in the admin is optional; sections/main-page.liquid bounces older links that lack the param.
• Health monitoring — assets/live-preview-health.js watches a real customer's session and reports when the preview silently stops working (overlay missing or collapsed, artwork not loaded or not matching the descriptor, descriptor empty or unparseable, text undrawn, webfont fell back) plus the engage → add-to-cart funnel, to dataLayer and Clarity tags. It never sends the customer's text.
• Line item properties captured per personalised item: Κείμενο (visible text), _lp_preview (descriptor — _preview before 2026-08-20, a name Cloudlift also uses for a PNG URL, which made the admin render ours as a broken image; readers take both), _preview_base (cover URL), _custom_product = Yes (flag to hide cash-on-delivery etc.), _preview_link (the viewer URL). The _ prefix hides a property from the customer but keeps it on the order / in the admin.

Sample fixtures (checked in)

sample_base.pdf, sample_item.json, sample_order.json, covers.json — run the full chain to self-check:

python order_to_md.py --order sample_order.json --out-dir out --covers covers.json
python stamp.py --descriptor out/order_1001_item_1111.md --out out/final.pdf --verify --png out/final.png

Expected: dx=-0.0 dy=+0.0 and «ΔΗΜΗΤΡΗΣ» centered on the cover.

Authoring config — the print PDF is the coordinate system

The box lives in two frames at once: % of the storefront image for the browser overlay, and points on the print artwork for stamp.py. They are not related by any formula — the storefront image is a square scene with the book sitting inside it, the print PDF is a full wrap (back cover + spine + front cover). A percentage on one means nothing on the other.

The old tool derived boxPt = box% × artworkPt anyway, and defaulted artworkPt to A5 portrait (420×595). On the real 350×250 mm wrap that put the text at x 88→273 mm — starting inside the back cover, crossing the spine, ending mid-front. Every configured product carried that.

box-tool.html — align once, place once

Three stacked layers, the way you would do it in Illustrator:

1. Load the print PDF. It is the back layer and it defines the coordinate system; its true page size becomes artworkPt, read from the file.
2. Load the storefront image on top and press 1 · Ευθυγράμμιση. Drag and scale it (opacity slider, arrow keys = 1 pt, Shift = 10 pt) until the fixed landmarks coincide — logo, small logo right, «HELLO PAPER PERSON». They line up exactly because the mockup was made from this PDF.
3. Press 2 · Τοποθέτηση and place the box once. Both frames fall out of that single placement: boxPt directly, box % through the alignment.

The alignment is saved as calib (where the mockup sits on the artwork, in points), so the product can be reopened and nudged without eyeballing it again.

Cross-check: placing the box on the front panel of a 350×250 wrap yields box = 24.3 / 23.6 / 51.3, against the 25.3 / 22.4 / 52.8 that was authored independently by dragging on the storefront image. The percentage side was always right; only the points side was broken.

One product metafield (paste the tool output)

Create a product metafield definition once: key custom.live_preview, type JSON. Then on each product, paste the whole JSON the tool produced into that field. That's it — one field, one paste. The snippet reads box (%) for the live overlay and boxPt+artworkPt (points) for exact print. No metaobject needed.

Example value (what the tool outputs):

{ "box": {"x":25,"y":50,"w":50,"h":10},
  "artworkPt": {"w":420,"h":595}, "boxPt": {"x":105,"y":298,"w":210,"h":60},
  "font": {"family":"Astir","sizePct":70,"color":"#3a2a1a","align":"center"},
  "fonts": [ {"label":"Χειρόγραφη","family":"Astir","url":"https://cdn.shopify.com/.../Astir.woff2"} ],
  "size": {"min":40,"max":100,"step":8} }

size (optional) overrides the block's per-product size bounds — see below.

Storefront controls (theme block settings)

On the product's Live preview block (Theme editor → Product information), beyond position/colour/fonts:

• Autofit (autofit_fill, default on): the text grows to the largest size that fits the box on both axes. The customer's text always fills the box.
• Size +/− buttons (show_size_stepper): optional small stepper the customer uses to enlarge/shrink the text, bounded by Min / Max (size_min / size_max, % of box height) in steps of size_step. Hybrid with autofit: default = fill, − down to min, + back to fill; it never overflows. The chosen size is written to the descriptor as font.sizePct, so stamp.py prints it exactly (no script change).
• Labels (font_label / size_label / align_label) shown above each control.
• Tooltips (label_tip / font_tip / size_tip / align_tip): optional (i) help text next to a label; blank hides it.

The legacy "Customer font choices" (font_options) block setting was removed — customer fonts now come only from the custom.live_preview fonts array above.

box (%) is relative to the storefront cover image — load that exact image in the tool. fonts (optional, per product) is the customer font dropdown: upload each font file to Shopify Files, then add a Label | Family | URL line per font in the tool's "Customer fonts" box. The storefront loads them via @font-face and the first one is the default.

fonts_to_woff2.py — make fonts web-friendly

Convert your fonts (TTF/OTF/TTC/WOFF) to compact, web-optimised WOFF2 before uploading to Shopify Files. Requires pip install "fonttools[woff]" brotli.

python fonts_to_woff2.py --in "C:/path/to/fonts"        # a folder
python fonts_to_woff2.py --in cover.otf --out web-fonts # a single file

It writes .woff2 files and prints the family name to use for each, e.g. Astir | Astir | <paste URL> — the exact line to paste into the tool's Customer fonts box (just fill in the Shopify Files URL after uploading the .woff2). (Type1/.pfb and other non-sfnt formats need FontForge first.)

Store-side setup still required (manual)

1. Base PDFs — export one cover PDF per color variant from Illustrator; the artboard aspect ratio must equal the storefront base image's.
2. Per-variant base images — upload each variant's preview mockup to Shopify Files and set the variant metafield custom.preview_base_image (or leave the block's single base_image for one-variant products).
3. Font licensing — the files are in the repo and the pipeline works; what is still open is whether each family's licence covers print as well as web embedding. Converting WOFF2 back to OTF changes nothing legally: same font, same licence. Confirm per family, Astir included.
4. ~~Order export~~ — done, see fetch_orders.py above.
5. Inline thumbnail on the order line item — not achievable without a server. Cloudlift manages it because it renders the PNG and hosts it before add-to-cart, so the URL is already in the line item property; the Admin then shows it inline. Line item properties are immutable once the order exists, so nothing the batch produces can get back onto the line. Reaching parity means a client-side canvas render plus an upload endpoint (app proxy) — i.e. re-introducing hosting. The no-server options are the _preview_link viewer (one click, never stale) and mockup_png.py --upload, which puts a thumbnail on the order's Metafields card after the batch runs. ```

Live Preview — Technical handoff

In-house Live Preview — Technical Handoff

Status: LIVE in production since 2026-08-19. Everything below the "Historical" line predates the cutover and is kept for context only — where it disagrees with this section, this section wins.

Working in parallel

Several sessions work on this tool at once, each in its own worktree under .claude/worktrees/. Nothing here has ever been a merge conflict — main serialises the commits — but a stale worktree is a real hazard, and it fails silently. Before you touch anything under tools/live-preview/:

git fetch && git merge --ff-only main

A worktree that predates a shipped fix hands you the pre-fix file. Edit it, commit, merge, and the fix is gone with nothing to warn you: on 2026-08-24 two idle worktrees were still holding a stamp.py from before b6a3243 — the commit that stopped the print stamp trusting insert_textbox when an order carries no recorded lines. Re-fixing that by hand would have been the second time.

A merged branch is 0 ahead of main. One line separates finished work from work still sitting in someone's tree:

git rev-list --left-right --count HEAD...main

This section is the register. If your session is not in it, add it.

Register — 2026-08-24

All four worked the same directory in the same afternoon and none of them collided, because each landed on main before the next started. That ordering is the whole mechanism — it is not luck, and it stops working the moment two sessions hold uncommitted edits to the same file at once.

It was tested the same afternoon: two sessions edited this file within minutes of each other, one adding this section and one adding its own row. It cost nothing, because both had committed and git merge-tree --write-tree main <branch> answered the question before either of us guessed at it. That command is the cheap check — it resolves the merge in memory and touches no working tree, so it is safe to run against a branch someone else is actively using.

Where things stand

The in-house block is enabled on the shared product.moodbooks-redesigned template and the Cloudlift app block is disabled — one commit, because both write properties[_preview] and two live systems would collide. The Cloudlift app embed is a separate theme-settings toggle and was disabled by hand from the theme editor; cloudlift now returns 0 matches on a live PDP.

13 products live, 39 variants, each with custom.live_preview (geometry + fonts), custom_preview_base_image and custom_preview_text_color.

Shipped since the cutover (PR #7, rebase-merged)

• The order's _preview_link now carries ?view=cover-preview, so it resolves regardless of which template the Page is assigned; sections/main-page.liquid bounces links that predate it.
• The descriptor property was renamed _lp_preview; every reader still accepts _preview for older orders. This was the broken image in the order admin.
• Autofit starts at 85% of the per-font ceiling (sizeStart), not 100%, so a short dedication no longer lands enormous with + already disabled.
• The −/+ and alignment buttons are 44×44 with a 2px border.
• assets/live-preview-health.js, the customer notice, the cart hold, and the Google Form → Apps Script → GitHub issue chain (see Monitoring below).
• The faceLoaded() guard replaced document.fonts.check().

Verified by 42 assertions across four Playwright suites (headless Chromium at /opt/pw-browsers/chromium-*/chrome-linux/chrome; note that moodbook.gr, cdn.shopify.com and docs.google.com are unreachable from that sandbox, so harnesses must be fully local — self-hosted fonts included, or every measurement silently runs against a serif fallback).

Shipped 2026-08-21 — lines stops being taken on trust

Everything here came out of order 7673 and is described in full under Things that will bite you → the lines caveat.

• reflow() re-captures the descriptor. It re-fitted the text on every resize and hero load and never re-recorded, so an order could carry line breaks from a layout the customer no longer had.
• overflow-wrap: anywhere → break-word on .lp-overlay-text, .lp-cart-text, .lp-mini-text. anywhere is what permits w|edding.
• fitText() now reports whether it ran, and no lines are recorded when it did not. This is the root cause: the fit bails on a box with no size, and the descriptor was pairing those unfitted breaks with a sizePct from an earlier fit — two layouts in one record, which is why 7673's numbers are impossible.
• A break that splits a word which would have fitted a line of its own is refused at capture by wrapsAreCredible(), which measures it.
• Downstream the same break is repaired, not discarded — repairLines() in live-preview-cart.js and lines_guard.py in the print pipeline — because on an order already taken the sizePct failed alongside the lines, so re-wrapping produces a third layout nobody saw. Measured on 7673: repair lands at 52.5 pt against the customer's own 51.2 pt.
• The descriptor records renderedBase and font.renderedFamily — what the browser painted, beside what it was told to paint.
• The order-preview page warns, in Greek, when the recorded lines were repaired, when the cover on screen was not the cover recorded, or when the webfont that the lines were measured against was not the one asked for — and it draws the cover on exactly the recorded number of lines, so it cannot warn about a break and then show one.
• Recorded lines render under white-space: pre everywhere, so no renderer can quietly turn a transcript into one line more.
• Order 7673 now prints Nef & Dim / blank line / “Our wedding”, verified by stamping its own descriptor onto its own artwork in Marilia.

Verified by 48 assertions in tools/live-preview/harness/ (Chromium against a fully local harness with the real Astir face self-hosted) plus the print pipeline run end to end on order 7673's own descriptor, onto its own artwork, in Marilia — the resulting PDF reads Nef & Dim / blank / “Our wedding”. The pre-fix reflow() is kept in the suite as a negative control, so the regression cannot come back unnoticed.

Shipped 2026-08-23 — a blank cover now asks one question

Came out of the three abandoned checkouts of 2026-08-22, analysed end to end from Shopify, Clarity session recordings and GA4. Two of those three customers had engaged with the personaliser and typed; the third never touched it and reached the buy button with all five properties empty.

• assets/live-preview-confirm.js, plus a <dialog> in snippets/live-preview.liquid. When a personalisable product is about to enter the cart with the text field untouched, it asks once: «Σίγουρα δεν θέλεις να τυπώσουμε στο εξώφυλλο το δικό σου μήνυμα; Είναι ΔΩΡΕΑΝ». «ΝΑΙ θέλω» scrolls to the field and focuses it; «ΟΧΙ δεν θέλω» adds. The wording is the shop owner's own, approved 2026-08-23 and pinned verbatim by an assertion — do not reword it without asking him.
• Asked once per page, whichever way they answer, and a variant change does not re-ask. An earlier version re-asked after «ΝΑΙ θέλω» if the field was still empty; the owner read that as a button refusing to work, and he is right.
• The hook is submit in the capture phase — the single choke point for the main button, the Enter key, and the mobile sticky bar, which adds by .click()ing that same button. The sticky script is not touched at all. Dawn's ProductForm listens on bubble, so this runs first and can stop it.
• Stopping is deliberately narrow. preventDefault + stopPropagation + stopImmediatePropagation fire ONLY on the path where the dialog opens, and answered is set before the re-submit — so every submit that actually adds to the cart passes through untouched. That is what keeps a later capture hook (tools/cover-capture) able to run on the same event without coordination.
• Not a broken-order guard. An empty descriptor prints the variant's standard cover (320b5d2); nothing downstream fails. This is only about a customer who never learned the cover could carry their words.
• Restyled the same evening (8e06941) after a photo from a real handset showed a flat grey box in the browser's own font, two identical buttons, and a row that had wrapped by accident. It now carries the theme's Neutra Text, #cb1e1e on «ΝΑΙ θέλω» — the same red as add-to-cart — a grid that stacks by decision on a phone rather than reflowing, and labels centred on both axes by the button box instead of by the UA default, which loses to any theme rule and does not centre a label that has wrapped to two lines.

The rate is small, and it is written down as small. Counting only line items where this block actually rendered, and only after the cutover landed on main: 2 of 11. An earlier pass reported "4 of 13" — it counted from midnight on 2026-08-19 and swept in Cloudlift items. The durable argument is the geometry, not the count: the sticky bar is on screen from first paint while the field sits at 909px against an 812px viewport, confirmed by the owner on his own handset. See tools/abandoned-checkouts/README.md for both ways to get this number wrong.

#7674 is the strongest of the two, for a reason worth keeping. Its beacon fired overlay-missing at add-to-cart — and the overlay is mounted lazily by activate(), whose only four call sites sit inside onInput(), onFontChange(), setAlign() and onSizeStep(). So no overlay is not an absence to be explained away: it is positive proof the customer never typed, never changed the face, never touched alignment and never touched size. That customer was then shown a false "preview did not load" and had add-to-cart disabled under them — a separate defect, fixed in f712c74 — which is why the population it proves is real but the sale it cost is not attributable to this.

Verified by 28 assertions in tools/live-preview/harness/confirm.html (fully local, runs straight against the asset, no build step), plus an end-to-end pass on the live storefront reading cart.js before and after: empty field → dialog opens, 0 → 0 items; «ΟΧΙ δεν θέλω» → 0 → 1; a second empty add → no dialog, 1 → 2. Deploy verified by md5 against themes(roles:[MAIN]){ files }, both files written within one second of each other.

Shipped 2026-08-24 — print stops trusting insert_textbox even without lines

Came out of order 7675 (Cavafy). Its descriptor carried lines: null — the webfont had not loaded at add-to-cart, so the capture refused to record breaks, exactly as designed — which sent stamp.py down its insert_textbox fallback. That call silently ignores lineheight= on faces with compressed metrics: Cavafy asked 1.15 and got ~0.77, the printed lines sat 9.9% of the box height apart where the mockup (Pillow, which obeys leading) had 14.8%, and check_match.py blocked the run at 33% drift. The cover was fixed by hand — print_runs/20260824_1408/MANIFEST.md → «Διορθώθηκε χειροκίνητα».

stamp.py now wraps the text ITSELF on the no-lines path — the same greedy break-word rule as mockup_png.py, re-derived at every candidate size the way the browser's fitText does — and then draws exactly like the recorded-lines path: line by line at explicit baselines, advance = size × leading. insert_textbox is gone from the file, so the leading survives on every face and the two renderers can only part company where their METRICS disagree — the residue the gate exists to judge.

Verified by re-running the whole 2026-08-24 batch from its archived orders: all ten previously-green personalised covers still ✅ — including the two no-lines ones the old code passed (7680 Astir, 7681 Royal) — the checked-in fixtures still land dx/dy≈0, and 7675 itself now measures 0.0% spacing drift. It still blocks, on ONE break point (PyMuPDF ends line 1 «πιο», Pillow «την»): two engines wrapping a long text differently is a human's call by design, and that order is already printed. The blank-line residue this paragraph used to record is closed — next section.

Shipped 2026-08-24 — a blank line is space, not leading

The spacing check compared each measured gap between consecutive PDF lines against the mockup's declared per-line step_ratio. A blank line takes a full line box in BOTH renderers but leaves no glyphs, so pdf_lines() cannot see it and the measured gap across one reads as double the step: any descriptor whose text held a blank line false-blocked at the gate. Probed with "Αγαπημένη μου\n\nΜαρία" (no lines, Astir): both renderers drew the identical 3-line layout, stamp --verify landed dy=+0.0, and the gate blocked at 93%. The same arithmetic bit recorded lines holding a blank — a 7673-shaped descriptor (["Nef & Dim", "", "“Our wedding”"]) blocked identically. It predates the insert_textbox removal (a replica of that path blocked the same probe at 66%).

check_match.py now divides each measured gap by the number of line boxes it spans — read off the layout's text_lines, whose blanks mark exactly where the unseen boxes sit, and only when the visible-line counts agree (when they don't, check 1 is already blocking and the mapping means nothing). The division does not soften the gate: a genuinely collapsed leading shrinks the per-box quotient the same way it shrinks a plain gap, so Cavafy's 0.77 still blocks with or without a blank in the text.

check_match_selftest.py holds the fixture in all four directions — blank healthy PASSES, blank collapsed BLOCKS, plain collapsed BLOCKS (the #7675 shape), plain healthy PASSES — built at exact synthetic baselines so no font metric blurs what is measured. Against the pre-fix gate it fails the first case at exactly 100% drift, which is the 2× arithmetic by name.

Verified: the probe and the 7673-shaped descriptor both ✅ (3.3% and 0.0% drift), the sample chain still lands dx/dy = 0.0, and the archived 2026-08-24 batch re-run returns 18/18 identical verdicts — 7675 still blocks, now only on the break-point disagreement that is a human's call by design.

Shipped 2026-08-25 — the print gate: the photograph gets a vote

print_gate.py, ink.py, print_gate_selftest.py, wired into run_batch.py. Built from PRINT_GATE_SPEC.md and the adversarial review beside it.

What it does. Subtract renderedBase from the stored capture; subtract the base artwork PDF from the stamped PDF; crop both to the box; rasterise the print side to the photo crop's pixel height; then ask both sides the same two questions through the same code — which line boxes hold text, and how tall the block of ink is. Neither side's answer is compared against lines; they are compared against each other, which is the only way a wrong transcript can lose.

Three things the spec got wrong, found by measuring:

1. Contiguous ink bands are not the line count. Confirmed independently: three lines of ordinary Greek into our_wedding's real box, 150 dpi, threshold 28 — Cavafy, Nadiri, Roboto and Memories return 4 bands for 3 healthy lines, split by a 1.44–2.40 pt gap under an accent or above a descender. Caps returns 3/3 on all eight. The spec's suggested repairs both make it worse: a minimum band height does not close a 2 pt gap, and smoothing closes the REAL gaps first (21 of 96 combinations wrong, against 11 raw). What works is the slot grid — .lp-overlay is a flex box centring one block of T line boxes of sizePct/100 × leading, so the grid has no free parameter. A gap that splits a line still lands in that line's slot.
2. Occupancy alone is not enough, and it fails in the direction that ships a wrong cover. A layout with one line FEWER than the transcript records is centred inside the longer grid and still touches every slot — the pattern matches and it sails through, on 7 of 8 faces. Its block of ink is exactly one pitch shorter, which nothing else in the pipeline can be, so the second question is a length and needs no threshold at all.
3. stamp.py's shrink valve breaks a shared ruler. It starts at the descriptor's sizePct and shrinks until PyMuPDF's metrics fit — measured 4.0% at worst on healthy items, 33% when the recorded lines are stale. Laying the descriptor's pitch over the print then reports a line-count disagreement that is really a size difference. Each side's grid is now built at the size that side actually used (the print's read off get_text("dict") spans), and the size ratio is reported.

Calibration, and it is a floor. 8 faces × 14 healthy line shapes = 112 renders, against 8 × 5 defective = 40, with the photo side drawn by Pillow and the print side stamped by stamp.py itself — two engines and a JPEG q0.92 hop, the same asymmetry the real gate faces.

worst healthy block drift   0.056 line     threshold 0.45
least defective drift       0.903 line     -> 8x the noise, half the signal
worst blank-slot spill      3.86% of peak  -> ink.SLOT_SHARE 0.10
two engines on one font     4.1% on width  -> check_orders LINE_OVERFLOW 0.50

Zero healthy covers blocked, zero defective missed. These are desktop libraries standing in for iOS Safari's text shaper — re-measure when there is more than one real m=dom witness. Today there is exactly one, #7688.

Two more checks came out of the review, and both are free:

• The calib cross-check (print_gate.py, every order, no photo, no network). The descriptor carries box % AND boxPt; calib lives only in the repo. Recompute one from the other and you test that the config file still matches the metafield the descriptor came from. Noise floor 0.0495 pp across all 52 values of the 13 shipped configs — that residual is rounding, because box-tool.html DERIVES box from boxPt through calib. Signal: the +100 pt boxPt.x injection that check_match.py passed with a green tick measures 15.188 pp. Threshold 1.0 pp.
• The single-line width check (check_orders.py). Its loop was range(len(grp) - 1) — only ever pairs — so one recorded line that could never have fitted the box was never asked about. Margin 50%, and it is wide on purpose: two desktop libraries reading the SAME font file disagree by up to 4.1% on the width of the same Greek string, and the customer's browser is a third shaper.

And one live bug the review found in run_batch.py: when mockup_png.py failed, ok_png went false, good stayed True, and the cover went into ALL_COVERS.pdf ungated and counted in «N εξώφυλλα έτοιμα». There are now three outcomes — ✅ / ❌ ΔΕΝ ΤΑΙΡΙΑΖΕΙ / ⚠ ΧΩΡΙΣ ΠΛΗΡΗ ΕΛΕΓΧΟ — only the middle one may rename a PDF, the third has its own manifest section, and the closing line says how many covers were actually compared against the customer's screen.

⚠ _lp_image is the customer's text. It carries ?v=<base64 of the whole descriptor> and the descriptor carries content; base64 is not redaction, and run_batch.py writes the gate's stdout verbatim into MANIFEST.md. Everything the gate emits goes through redact(), only the object key is ever printed, and a fixture asserts no output line holds a long base64 run.

Shipped 2026-08-25, second pass — where the bugs actually were

The gate's own fixtures were green through both of these. Neither was in the gate. Both were in the seam around it, and both collapsed the three outcomes back into two — silently, in the direction that prints an unchecked cover or blocks a healthy one.

1. --no-png switched off the photograph gate. The two gates were written under one elif args.no_png:, so a flag whose documented job is "skip the mockup PNG and the match check" also disabled a check that needs no mockup. Six covers printed, one of them the poisoned #7685 shape, and the run said nothing. Found the first time the gate was given a reachable witness: the sandbox reaches no cover host, so fetch_image had only ever taken its failure branch and run_batch's «συγκρίθηκαν» counter had never once been above zero. No fixture had exercised a flag combination.

2. A Greek console turned "unchecked" into "blocked". cp1253 cannot encode «⚠» or «❌», so print_gate.py raised UnicodeEncodeError on its own headline and Python exited 1 — which run_batch.py reads as BLOCKED. On any console not switched to UTF-8, every order without a witness would have come out «η εκτύπωση δεν συμφωνεί με ό,τι είδε ο πελάτης», with a traceback, in front of the owner — which is most of the queue today, and precisely the failure the three-outcome contract exists to prevent. print_queue.bat sets chcp 65001 and PYTHONIOENCODING=utf-8 and would have hidden it forever; the README documents python run_batch.py --order … directly, which does not. The exit code no longer depends on whether printing worked, and _console_never_dies() now guards run_batch.py, check_orders.py and check_match.py too — all three print Greek and all three had the same exposure.

The lesson, and it is the one worth carrying: the checks were tested, the wiring was not. run_batch_selftest.py closes that — it drives the real run_batch.py as a subprocess against a witness served from a thread-local HTTP server, which is the only way to reach that seam from a sandbox that resolves neither moodbook.gr nor cdn.shopify.com. 16 assertions: the six witness outcomes, the renaming, the counts, both flag combinations, both encodings, and the customer's text staying out of the manifest. Verified to FAIL against the pre-fix code before being kept.

Two smaller things from the same pass:

• ok now means every check ran, not "the witness agreed". It read elif gated:, so a product with no calib reported exit 0 with its geometry check silently missing. A product whose metafield carries no boxPt can no longer read ✅ until someone adds it — which is the point.
• A descriptor with more than one layer says so. stamp.py stamps every layer and this gate reads the first. No shipped product has a second one, so it is named rather than fixed — judging it needs a grid design with no product to test against.

Shipped 2026-08-31 — the gate meets 31 witnesses and four of its numbers were wrong

The gate shipped 2026-08-25 calibrated against fixtures: 8 faces x 14 line shapes drawn by Pillow and stamped by MuPDF. BLOCK_PITCH_TOL's comment said so and asked for a re-cut "when there is more than one real witness".

On 2026-08-31 the unfulfilled queue held 41 personalised line items, 31 with an m=dom witness. The gate blocked 10 of 41. Nine were its own measurement.

Four constants, each re-cut against the 31:

• ink.SPAN_FLOOR = 0.02 — a row joins the ink block only above 2% of the busiest row. Worst stray measured: 1.6% (#7698, 2 pixels against 125). A real line's densest row cannot fall under SLOT_SHARE = 10%.
• print_gate.INK_VISIBLE_SHARE = 0.05 — under 5% of the print's ink, the photograph is not evidence and the item is ΧΩΡΙΣ ΠΛΗΡΗ ΕΛΕΓΧΟ, never ΜΠΛΟΚΑΡΙΣΜΕΝΟ. Measured spread on the 31: healthy 97.0%–126.6%, #7710 0.02%. There is no middle.
• print_gate.ALIGN_TOL = 0.25 — past a quarter line the per-line PATTERN is not comparable and the OFFSET is the finding. Measured: 28 of 31 sit between −0.11 and +0.02; two sit at +0.46 and +0.47.
• check_match.pdf_lines now returns the baseline (span["origin"]), not the bbox top. Containment still uses the bbox — that question is about ink.

⚠ The order the checks are APPENDED is load-bearing, because blocked[0] is the headline. A missed wrap makes the block a line taller, which moves its centre half a line, which moves every line across a slot boundary — it trips all three. Only the first is the defect; the rest are its shadow. The order is lines → block → align, and both orderings that put align earlier were caught by print_gate_selftest.py renaming a known defect: "one wrap the transcript missed" became VERTICAL_OFFSET, and #7685's poisoned transcript became BLOCK_HEIGHT. Both still blocked; both would have sent the reader looking in the wrong place.

After: 4 blocked of 41, and all four are real.

• #7697 pes-mou-pappou — no transcript, and the two engines wrap «Κωνσταντίνος Δημητρακάκης» differently (1 line against 2). Nobody recorded what the customer saw, so nobody can settle it but a human.
• #7716, #7718 our_wedding — the print stamps the text half a line higher than the customer's screen (+0.46, +0.47). Line count right, height right, position wrong. Both transcripts carry more blank line boxes than visible lines, which is the thread to pull: 🔴 root cause still unknown.
• #7720 pregnancy_journal — the print's ink block is 0.98 of a line taller than the photograph's, with occupancy matching on both sides. That is precisely the shape the block-height check was written for.

The selftests are the regression guard and all four pass: 12/12, 4/4, 18/18, and the 152-render sweep at 0 healthy blocked / 0 defective missed.

Shipped 2026-08-31 — the tone check, and the fourth outcome it needed

Greek customers leave the tone off a name constantly, and until now nothing looked. accents.py looks; it never fixes.

The rule needs no dictionary, and no dictionary could have done it. Modern Greek monotonic orthography: a word of two or more syllables carries exactly one tone. What is on these covers is FIRST NAMES AND SURNAMES — the one class of word no lexicon covers — so «Νεφελη» has to be flaggable without anyone having heard of Νεφέλη.

⚠ And the lexicon we own could not have placed the tone anyway. MOODBOOK_DETECTOR_LINT/lexicon/data/ holds 200.000 Greek forms, and all three tables are stored DE-ACCENTED — freq.tsv.gz has «νεφελη», lemma.tsv.gz has «ααρων», neighbours.tsv.gz has «αβαειο». They can say a word exists; they cannot say where its tone goes. Checked on 2026-08-31 — do not go looking there again. (Their data is CC BY-SA: internal use carries no obligation, copying it into a repo does.)

Syllables are counted as MAXIMAL RUNS OF VOWELS, which is a lower bound and that is the point. Two runs are separated by a consonant and a consonant is never a nucleus, so two runs is always two syllables; the reverse fails — «Μαρια» has two runs and three syllables. Under-counting can only lose a flag. It can never invent one, and an invented one is what would cost the shop: the owner is being asked to look at covers before printing, and a check that cries about «μια» or «ΝΕΦΕΛΗ» teaches him to skip the list.

one run, silent μια για δυο πια ναι και ποιος μου μας ALL-CAPS, silent ΝΕΦΕΛΗ ΚΑΙ ΔΗΜΟΣ (Greek drops tones in capitals) two runs, flagged Νεφελη Μαρια Δημος Νικος γαμος αυτη ετσι

Measured on the live queue of 2026-08-31: 5 of 39 line items, all five real — «Νικος», «Δημητρα», «Στελιος», «Ειρηνη», «γαμος», one swear word, and one plain typo («λσλυτερη») that the rule caught for free.

A FOURTH outcome, and it had to be a fourth. «ΜΠΛΟΚΑΡΙΣΤΗΚΕ» means the print disagrees with the screen; these covers agree with the screen perfectly and it is the SCREEN that needs a decision. Filing them under the same word would teach the owner that a block is sometimes nothing, which is the one thing the gate cannot afford. So: ✋ ΠΕΡΙΜΕΝΕΙ ΑΠΟΦΑΣΗ, stamped, kept as .HOLD.pdf, out of ALL_COVERS.pdf — drawn on purpose, because the decision is about how a name looks on a cover and he should be able to open it and look.

The decision is a WORD SUBSTITUTION, never a replacement text — text_fixes.py. Handing the queue new text throws lines away, stamp.py re-wraps, and the cover can come out with breaks nobody has seen: that is order #7673, the defect this whole pipeline exists to prevent. Substituting word for word inside BOTH content and every recorded line keeps the transcript true. Verified on the live queue: #7721 corrected («Στελιος»→«Στέλιος», «Ειρηνη»→«Ειρήνη») still passes print_gate against the OLD photograph and comes out ✅, because a tone mark does not move the layout.

⚠ An approval is not a missing check. The first wiring appended the approval to notes, which is the list that demotes an item to ΧΩΡΙΣ ΠΛΗΡΗ ΕΛΕΓΧΟ — so a corrected cover that the photograph gate had compared and passed was reported as one nobody could check, and the run's "unchecked" count went up by one for a cover that was more verified than before. remarks is now a separate list: it prints, it reaches the manifest, and it never touches the verdict.

Half a correction still holds the cover. «Νίκος & Δημητρα» is worse than «Νικος & Δημητρα» — the first looks decided.

text_fixes.json is gitignored: it holds customers' names verbatim and is a working note beside the print queue, not a record.

Tests: accents_selftest.py, and the suite is now five — 12/12, 4/4, 18/18, the 152-render sweep, and this one.

Shipped 2026-08-31 — the queue stops printing orders that are not orders, and #7710's colour is named

Three of the covers in that day's run belonged to CANCELLED orders. #7669,

7670 and #7688 were VOIDED, cancelled AND archived, and had been coming out of

print_queue.bat as covers to print for days — #7669 and #7670 even as ✅. fetch_orders.py --unfulfilled now appends status:open, which excludes cancelled and archived in one word, at the API, before anything is drawn.

⚠ PENDING IS NOT «UNPAID» IN THIS SHOP, and a paid-only filter is a trap. Greek IRIS and bank transfer sit in PENDING until the money lands. On the 2026-08-31 queue that was 4 orders of 48 — and three of them were orders the owner had that same hour asked to print. A literal paid-only rule would have removed them without a word, which is the one failure a print queue must never have: nobody looks for what is missing. So the money splits three ways:

VOIDED / REFUNDED / PARTIALLY_REFUNDED / EXPIRED   dropped, named
PENDING / AUTHORIZED / PARTIALLY_PAID              WITHHELD, named,
                                                   and `--include-pending`
PAID                                               printed

#7710 — the cover was painted in another variant's colour

The order says #d9c7c5 (Steel Teal's custom.preview_text_color). The photograph was painted in #606161 — Champagne Pink's, another variant of the same product, left behind when the customer moved between colours. The print is correct; the customer's screen was not.

What made it invisible: the descriptor records renderedFamily — the face the browser actually resolved, beside the one we asked for — and has done since the 7673 work. It records no renderedColor. So a colour that diverged from state.font.color left no trace in the order at all, and naming it took a photograph, a variant-colour table and a median-of-ink measurement.

Three changes, and the first is the one that matters:

• renderedColor now rides beside renderedFamily. Every order from here carries what the browser actually painted. check_orders.py can see this before anything is printed; no photograph needed.
• The colour is set !important, exactly as font-family already was and for the reason recorded there — a theme rule on gallery descendants beats a plain inline value.
• reflow() re-resolves the variant's colour. It runs on the new hero's load, which is the one path that fires after Dawn re-renders the gallery without a render() — so a colour resolved before the form input caught up stayed on the cover permanently. COLOUR ONLY: applyVariantVisual() can swap the hero, and swapping it from the hero's own load handler is a loop.

And so the gate says it out loud: ink.dominant_ink() measures the colour the photograph was actually painted in, so the ungated message is now «η παραγγελία λέει #d9c7c5, ο browser ζωγράφισε #606161» instead of «δεν κρίθηκε».

⚠ The 31 descriptors were checked and they are not the problem. The descriptor inside _lp_image's ?v= (written at capture) and the one in _lp_preview (written at add-to-cart) were compared field by field across all 31 witnessed line items: variantId, base, renderedBase, colour, family, sizePct, align, leading, line count, character count, box — zero differences. The capture and the order agree. What diverged was the PAINT and the state, and only a renderedColor will ever show that in the order itself.

A correction may not move the layout

text_fixes.layout_risk() counts what a substitution did to the shape: line count, per-line character count, total length, word count. And run_batch.py asks check_orders for the verdict BEFORE and AFTER the fix — if it got worse, the cover is blocked as «Η ΔΙΟΡΘΩΣΗ ΧΑΛΑΕΙ ΤΗ ΔΙΑΤΑΞΗ». A tone is the same character count in every face we ship, which is why substitution inside the recorded lines is safe; «Νικος → Νικόλαος» is not, and a line already wrapped to the edge of the box would then overflow. A cover that is correctly spelled and wrongly set is worse than the one it replaced, because nobody re-reads a cover they just fixed.

--accept-gate

The owner looked at #7716 and #7718 — half a line high — and judged them cosmetic. There was no way to say so, and the covers were unprintable from the queue. text_fixes.py "#7716" --accept-gate "<γιατί>" prints over a block; the reason is required, and every reason it overrode is printed, reaches the manifest and is counted. It never silences a check.

Running the storefront harness on Windows

tools/ios-safari/suite-chrome.mjs. The harness needs a page that is actually visible — every embedded pane here reports document.visibilityState: 'hidden' and the suite's fits run behind requestAnimationFrame, which a hidden page never services, so it hangs at «running…» forever. Playwright's headless page reports visible. channel: 'chrome' uses the Chrome already installed, so nothing is downloaded. 82/82 green with the changes above. (The harness README still says 48; the suite has grown.)

Shipped 2026-08-31 — #7697 and #7720, and the repair rewrap is for

Both were blocked and the owner asked for both to be printed as they stood. The browser was asked first — simulate-orders.mjs --engines chromium, Chrome 151 via channel:'chrome', four widths — and it reversed which one was broken.

#7697 was never wrong. «Κωνσταντίνος Δημητρακάκης» comes out on ONE line at desktop, narrow, phone and Android alike, which is what stamp.py prints. The block came from check_match.py: mockup_png.py (Pillow) wraps it to two, and with no transcript there was nothing to break the tie. The tie-breaker is the browser, and it sides with the print. --accept-gate, with that as the reason.

#7720 was wrong, and «as it stands» would have printed it wrong.

transcript   5 lines, breaking after «this», «to», «the», «beautiful»
Chrome ×4    4 lines, breaking after «journal», «treasure», «the», «ahead!»
photograph   ink 3.90 lines tall against the print's 4.88 — one line SHORT

Two witnesses that have never met — the customer's own capture and a fresh browser run at the order's own size — agree with each other and against the record. The transcript is stale; this is the #7673 family.

So text_fixes.py --rewrap: throw the recorded lines away and let the printer wrap the text itself. ⚠ It needs EVIDENCE and the docstring says so. layers[].lines is the whole design and discarding it is how you get a THIRD layout nobody has seen — in a broken transcript sizePct is usually broken with it (52.5pt against the screen's 51.2pt on #7673). It is safe only when something other than the transcript says what the screen showed, and says it twice.

The proof it worked is the gate itself: #7720 went from BLOCK_HEIGHT 0.98 to ✅. Re-wrapped, the print reproduces the photograph. Nothing else in the run moved.

⚠ A deliberate rewrap always lands the transcript check on ΧΩΡΙΣ — that is what it IS — so run_batch.py stops reading that transition as damage when the decision is rewrap. ΠΡΟΣΟΧΗ still blocks: an impossible layout is impossible however it got there.

The queue is now 38 ready, 0 blocked, 0 waiting — 27 compared against the customer's own photograph, 7 ΧΩΡΙΣ ΠΛΗΡΗ ΕΛΕΓΧΟ (6 of them orders too old to carry a picture, plus #7710 whose picture was painted in the wrong colour).

Shipped 2026-09-03 — COD, an order range, the final sigma, and a raster older than its own text

--min-order / --max-order. Shopify's order query has no range operator for name, so the number is filtered client-side. It is a STRING in the API («#7700») and comparing those as strings puts #999 after #7700 — hence int(), and a name that is not a number is NAMED rather than assumed in or out.

COD is PENDING forever by design. Cash on delivery is not money that failed to arrive; it arrives when the courier hands the parcel over, and the parcel cannot exist until the cover is printed — withholding it deadlocks the order. So a PENDING order on a COD gateway is printable. ⚠ No COD order has ever reached this queue, and that is deliberate: live-preview.js sets properties[_custom_product] on every personalised item precisely so the checkout can hide cash on delivery for them. Measured over the 35 unfulfilled orders from #7700: shopify_payments, IRIS, Bank Deposit, PayPal, Revolut, Viva — not one COD. The branch is there for the day that setting changes; it is not a path anyone has seen work.

A Greek word cannot end in σ. That position is ς, always, with no exception in the modern language — the same shape of rule as the tone, no dictionary, and it cannot be wrong. Order #7736 typed «Ο γαμος μασ»: the tone check caught the «γαμος» and walked straight past the «μασ», one run away from a hardback that said so. FINAL_SIGMA.

⚠ THE RASTER AND THE DESCRIPTOR ARE NOT TAKEN ATOMICALLY

Order #7735 blocked on three checks at once. The photograph shows

Ο γάμος μας / Ελένη κ, / Θάνος          three lines

and the order's own content — identical in _lp_preview AND in the ?v= attached to the image itself — is

Ο γάμος μας / (blank) / Ελένη και Θάνος

which is what stamp.py prints and what Chrome produces at all four widths. The picture is of an EARLIER moment than the text it was uploaded with.

This is a second member of the family #7710 opened. There the paint held a stale COLOUR while the descriptor was current; here the paint holds stale TEXT while the descriptor is current. The 2026-08-31 audit that compared ?v= against _lp_preview across 31 line items and found zero differences was answering a narrower question than it looked: both of those are written from state, so they agree by construction. What can lag is the third thing — paintOverlay()'s raster of the DOM.

🔴 Open: make the capture atomic. The raster must be taken from the same moment as the descriptor it ships with, or carry its own timestamp so the gate can refuse a witness older than the text. Until then a stale witness reads as a layout disagreement, which is a lie about the cover, and the only way out is --accept-gate with a measurement — which is what #7735 got.

text_fixes.json no longer dies with the worktree

It is gitignored, which makes it invisible to git AND to git worktree — so a recycled worktree took every decision the owner had recorded in it. On 2026-09-03 that was seven approvals, three of them corrections already applied to covers, saved only because the old directory had not been deleted yet. git rev-parse --git-common-dir answers with the MAIN repository's .git from inside any worktree, so its parent is the checkout that outlives them; the file now lives there. MOODBOOK_TEXT_FIXES overrides it.

Shipped 2026-09-03 — the one check that is not our own code marking its own homework

Every gate in this pipeline compares two things WE produced. check_orders reads the descriptor our code wrote. check_match compares two renderers that both read layers[].lines. Even print_gate's photograph is rasterised by our paintOverlay(). A defect upstream of all of them — the code writing down the wrong text — would come out green everywhere, and #7735 is what made that concrete: three gates fired at once and none of them could say WHICH artefact was stale.

There is a second path, and it owes us nothing. The visible properties[«Γράψε το κείμενο…»] is an ordinary textarea inside the product form. Shopify receives it whether or not live-preview.js ran, parsed anything, or agreed with itself. The descriptor is written BY that code. So:

descriptor.content  ==  the visible field   ⟹  the text about to print IS
                                                what the customer submitted

not because anyone read the JavaScript and believed it, but because two things that had to disagree did not.

Measured before wiring it, over 120 days: 73 personalised line items, ZERO disagreements — #7735 included. That is what settled which of that order's two artefacts was stale: the picture, not the text. The owner's question — «I don't remember which of the two the print comes from» — has a one-word answer (the DESCRIPTOR) and now a standing proof rather than an assurance.

check_orders.check_typed(), wired into run_batch.py as GATE 0a, blocking. Nothing downstream can recover from printing the wrong words.

⚠ ASKED BEFORE ANY CORRECTION IS APPLIED. An approved fix changes the descriptor on purpose, so after it the two paths are MEANT to differ; asking afterwards would block every corrected cover for doing what it was told.

⚠ Whitespace is not the question. The textarea keeps the customer's CRLF and trailing spaces; content comes from textarea.value, which the HTML spec normalises to LF. Comparing raw reports every order broken. Both sides collapse whitespace runs first — a difference that survives that is a difference in what will be printed.

⚠ The message carries counts, never the text. A fixture asserts it: the customer's words must not reach a log to explain that the customer's words are wrong.

Shipped 2026-09-03 — the picture now says what it drew

Order #7735 shipped a photograph of «Ελένη κ,» attached to a descriptor that said «Ελένη και Θάνος». Three gates fired at once and not one of them could say WHICH artefact was stale; it took a browser re-run and a side-by-side render to find out. The reason is one line of missing bookkeeping.

runCapture() takes sig — the descriptor, out of state. paintOverlay() then reads overlayText.textContent — out of the DOM. Two different moments, and nothing compared them. The existing guards both compare sig with the CURRENT state (sendCapture's first line, and sealCapture at add-to-cart), so a raster of an EARLIER moment passes every one of them: the snapshot is current, the state is current, and the picture is not.

The raster now certifies its own content. paintOverlay() hangs the exact string it serialised on the resolved image (_lpText, _lpAt), and sendCapture() refuses to publish when it is not the text in the snapshot — clears the property, forgets the upload, and schedules another cycle. A picture of the wrong moment is worse than no picture, because print would trust it. Both sides are overlayText.textContent, so the comparison is exact string equality and no whitespace or transform rule can weaken it.

And the URL carries the SHAPE, for the orders already placed: &p=<chars>.<lines>&t=<epoch> — characters and lines, never the words, the same discipline the health monitor keeps. print_gate.check_witness_fresh() compares it with the descriptor and returns UNGATED / WITNESS_STALE, never blocked: the cover is almost certainly fine, it is the WITNESS that is inadmissible. Orders before today carry no p= and come back None — the check cannot run on them, and says so by staying silent.

⚠ A trap paid for twice on the way in. check_typed compares the descriptor's content with the visible textarea, and content is overlayText.textContent — so on a product set to text-transform: uppercase it is ALREADY uppercased. Comparing raw would block every order of such a product. Uppercasing the customer's side is not enough either: CSS uppercase on Greek drops the tone («γάμος» → ΓΑΜΟΣ) while Python's str.upper() keeps it (ΓΆΜΟΣ), so the fix is to strip tones from both sides and compare letters — which is all the transform leaves to compare. Fixture: «ΚΕΦΑΛΑΙΑ δεν είναι διαφορά».

Harness 82/82 with the storefront change. Selftests 5/5.

Shipped 2026-09-03 — the half-line was ours: a trailing Enter takes no room

Three covers had come out of the gate as VERTICAL_OFFSET — #7716, #7718, #7736 — and the finding was real but nobody knew whose fault it was. It is ours, and the cause is one keystroke.

A customer who presses Enter after the last word leaves an empty final paragraph. The browser gives it NO HEIGHT. .lp-overlay centres the block with align-items:center, so the ink lands where three lines would put it, not four. Both of our renderers counted it.

Measured on #7736 — «Ο γαμος μασ \nΕυαγγελία & Σταύρο \n» — by stamping the same descriptor twice into its own box and reading the ink centre as a fraction of the box:

counting the trailing blank      39.5%
dropping it                      50.5%
the customer's own photograph    50.4%     ← the browser

Not an argument about CSS: the customer's capture and the corrected stamp agree to one part in a thousand.

Nine per cent of the queue ends in an Enter (3 of 32 line items on 2026-09-03) — and all three were the covers that had produced an offset or a layout dispute. There is no other member of that family; it explains the whole class.

lines_guard.drop_trailing_blanks(), used by stamp.py, mockup_png.py and print_gate.py — the two renderers so they draw the same boxes, and the gate so its ruler counts the boxes that were drawn. ⚠ TRAILING ONLY. A blank line typed BETWEEN two lines is space the customer asked for; «Ο γάμος μας», blank, the names is the commonest layout this shop prints.

⚠ THE FIXTURE WAS WRONG WITH THE CODE, WHICH IS WHY THE SUITE STAYED GREEN. print_gate_selftest.draw_photo() stands in for the browser and it counted the trailing blank too, so all three of our renderers agreed with each other and none of them agreed with the thing being imitated. Changing a fixture to make a suite pass is normally the worst move available; it is right here only because the measurement above says which behaviour the browser has. The docstring carries the numbers so nobody has to take that on trust.

And the sweep's reported blank-slot spill went 3.05% → 100% on the first green run, because the metric was still walking the ORIGINAL line list and reading the bottom pad as a blank slot. Meaningless, harmless to the verdict — and ink.SLOT_SHARE's comment invites a future reader to re-cut the threshold from exactly that number. Fixed; it reports 3.02% again.

Result on the live queue: 0 blocked, and the covers compared against the customer's photograph rose from 22 to 24.

Printing, day to day

Double-click print_queue.bat. One dated folder appears under print_runs/ holding, per personalised line item, a print PDF and a mockup PNG; plus ALL_COVERS.pdf — every approved cover merged in manifest order and repeated per quantity, which is the file you actually send — and MANIFEST.md.

Routing is by line item: personalised → text stamped on the variant's custom artwork; not personalised → the variant's standard artwork copied in; a legacy Cloudlift item → reported as manual work and never given a standard cover, because that would silently drop the customer's text.

Nothing is written to Shopify and nothing is fulfilled. Re-running is safe.

The files that hold the state

Scripts

fetch_orders.py (orders → JSON) · order_to_md.py (→ descriptors) · stamp.py (→ print PDF) · mockup_png.py (→ customer-facing PNG) · check_orders.py + check_orders_selftest.py (could this transcript have existed?) · check_match.py + check_match_selftest.py (does the PDF flow like the mockup?) · print_gate.py + ink.py + print_gate_selftest.py (does the PDF flow like the PHOTOGRAPH?) · run_batch.py (all of the above, one command) + run_batch_selftest.py (does run_batch ASK them, and listen? — that is where both real bugs were) · accents.py + text_fixes.py + accents_selftest.py (did the customer leave the tone off a name, and what did the owner decide about it?) · woff2_to_sfnt.py (storefront WOFF2 → printable TTF/OTF) · box-tool.html (author a product's geometry).

The three checks are not interchangeable and none of them subsumes another:

⚠ The fourth is not a gate on the PRINT. The first three ask whether the cover matches what the customer saw; this one asks whether what the customer saw is what they meant, which only the owner can answer. It holds the cover (✋ ΠΕΡΙΜΕΝΕΙ ΑΠΟΦΑΣΗ) and never blocks it.

Things that will bite you

A narrower screen prints the text slightly LARGER

sizePct is the rendered size as a fraction of the box height, so a layout that scales uniformly should record the same percentage at any screen width. It does not. Measured under WebKit on 2026-08-25, desktop and iPhone 15 identically:

box 486px -> 152px  (3.2x narrower)
sizePct  24.8% -> 25.2%   (+1.6%, and it goes UP)

The cause is the fit's tolerance, which is ONE PIXEL, absolute:

el.scrollHeight <= maxH + 1 && el.scrollWidth <= maxW + 1

One pixel is a larger share of a small box than of a big one, so the smaller box permits proportionally more overflow and the search lands proportionally higher. Predicted from the box heights alone: +1.01%. Measured: +1.61%. Same sign, same order; the rest is bisection granularity and integer scrollHeight.

What it means in practice: the size that goes to print depends slightly on the width of the screen the customer ordered from. Two customers typing identical text on a phone and a laptop get covers ~1.6% apart. Below every gate's tolerance and nobody has ever noticed, so this is a note and not an alarm — but it is the same absolute-tolerance defect the audit flagged across stamp.py (+0.5pt) and mockup_png.py (+1px), and if those are ever made proportional, make this one proportional too or the three will drift apart in a new direction.

Found by the iOS session while settling whether reflow() re-records under WebKit. It does — see the fixture note in harness/suite.js section 1.

When the margin is the size of what you left out, measure again

Both of this file's tolerance findings came within a hair of being reported backwards, and for the same reason.

The transcript check — "would these two recorded lines have fitted on one line?" — nearly condemned order #7687. Joined, the two lines measure 8.28 em against a box of 8.35: comfortably impossible, report it. But joining them dropped the SPACE the wrap had consumed. The real string is 8.58 em, it genuinely did not fit, and the break was honest. The omission was 0.30 em against a margin of 0.07.

The same shape produced the sizePct anomaly above: a tolerance of one absolute pixel, against a box whose height made that pixel worth about the size of the effect being measured.

The rule this leaves: when the quantity you might have omitted is the same order as the margin you are judging by, a confident number is at its least trustworthy. Both of those checks felt decisive and both were within one forgotten term of being wrong in the direction that costs the shop — telling the owner a healthy order is broken is as expensive as missing a broken one, because he stops trusting the check. Re-derive from the raw text, not from the intermediate you built.

It happened a third time on 2026-08-25, and this time it reached the shop. check_orders.py's join test was if w <= box_em — no margin at all — and it reported #7678/the_honeymoon to the owner as broken: «χωρούσαν μαζί (11.07 σε κουτί 11.13)». That margin is 0.54%. The string is «The Honeymoon Chapter» in Royal, and the same string in the same font file measures

PyMuPDF (what check_orders uses)   11.0678 em  -> fits 11.13  -> ΠΡΟΣΟΧΗ
Pillow                             11.1354 em  -> does NOT    -> honest

so the verdict was decided by which library did the arithmetic. The browser is a third shaper, and the storefront fit hands it another +1px one-sided — about 0.21% more slack on a 486px box. Measured spread between the two libraries on one font file: 1.70% on that Latin string, 4.10% on Greek. Every one of those is larger than the margin the verdict rested on.

A join must now fit with 5% to spare before it is called impossible, and the band underneath is ΟΡΙΑΚΟ — printed, counted, and deliberately kept out of «ΜΗΝ τις τυπώσεις». It costs nothing against the real defect: #7685's join fits with 51% to spare, ten times the threshold.

Note what the pattern now is. Both of this file's other tolerance findings were caught before they shipped; this one was not, and the reason is that the check with no margin looked the most rigorous. <= reads as exact. It is only exact about the number you computed, never about the thing you are asking.

«Δεν καταγράφηκαν γραμμές» is not «this cover is wrong»

The same 2026-08-25 run showed 5 of 17 line items with no transcript at all, and check_orders.py closed with one sentence over both classes: «ΜΗΝ τις τυπώσεις — θα βγάλει το ίδιο λάθος». For a missing transcript there is no mistake to repeat; there is only a cover nobody watched. A third of the queue was being stopped for a reason that did not apply to it, which is the same currency as a false positive — the owner does work he did not need to do, twice, and then stops reading.

The two classes are now reported separately and given different advice: a broken transcript needs a corrected cover before printing, a missing one needs a look at the preview link. And these are exactly the items print_gate.py can still judge when they carry a picture — a rasterised overlay needs no transcript, so an order whose transcript came back null is the one most worth having a witness for.

The stored cover is a witness only when m=dom says so

properties[_lp_image] carries ?v=<descriptor>&m=<mode>, and the mode is the whole point of the file:

• m=dom — the browser rasterised the live overlay through <foreignObject> with the face inlined. It re-derived the wrapping from the text and the computed CSS and never saw lines, so it CAN contradict the descriptor. Only this one may be used to judge anything.
• m=desc — the old path: canvas fillText over layer.lines. It agrees with the descriptor by construction, so agreement proves nothing. It is corroboration, never evidence.

Treat a missing m= as desc. Every order before 2026-08-24 is desc, and every degraded capture since is too — no face, no CORS, an old Safari, a fetch past its ceiling. The downgrade is silent and correct, which is exactly why lp_capture now rides on every health event: a shop that has quietly stopped getting witnesses looks identical to one that has them.

The trap this replaced: order #7685's descriptor recorded a break that was arithmetically impossible at the size recorded beside it — «ο γάμος μας» needed 5.18 of the box's 9.93 em — and the stored image agreed with it, because the image was drawn from it. Three of the eleven line items carrying a transcript since June describe a layout no screen could have shown.

Since 2026-08-25 print_gate.py reads m= and is wired into run_batch.py, so the picture finally has a gate behind it — but only for the orders that carry one. Everything else comes out ΧΩΡΙΣ ΠΛΗΡΗ ΕΛΕΓΧΟ, which is a third outcome and not a pass. Read the count, not the colour.

• Variant renames issue NEW variant ids. Giving four single-variant products a real colour name changed all four ids and orphaned their entries in covers.production.json. After any variant edit, re-check the map.
• form.id is not the form's id. A form exposes its named controls as its own properties, and the product form carries <input name="id"> for the variant — so form.id is that input element, not the string. The first version of the confirmation guard compared a string to an element, never matched, and did nothing on any product. It read correctly in review and was inert in the browser; only the harness caught it. Use form.getAttribute('id'), and reach for HTMLFormElement.prototype when calling a method a control could shadow.
• theme.updatedAt is not a deploy indicator. It read three weeks stale while files were updating the same minute. Verify a deploy with theme(id:…){ files(filenames:[…]){ body { … content } } }.
• The overlay lives inside theme markup, so anything it sets can be out-specified. Two rules already had to be forced: font-family (a theme !important) and display:flex (Dawn's .media > :not(...) at 0,3,0, which flattened the flex box and pinned the customer's text to the top of the box). Suspect the cascade first when the overlay looks wrong but the JS values read correctly.
• The text must not outrun its artwork. The overlay is DOM and redraws in a frame; the cover behind it is a network fetch. Writing the new URL straight onto the visible <img> left the customer's name hanging over a blank frame for as long as the download took — measured 2.8s on a deliberately slow cover, and the reason a variant change looked like "text first, picture later". swapHero() decodes off-screen, hides the overlay while the artwork is in flight, and gives the text back only once the VISIBLE element has the frame too (waiting on the preload alone still left ~350ms). warmBases() preloads every variant cover on focus, so the usual case is a cache hit and the swap is one frame.
• _preview is a shared property name. Cloudlift's holds a PNG URL, ours a JSON descriptor. Everything downstream distinguishes them by parsing.

• The keyboard moves what the customer sees, not what fixed is anchored to. An on-screen keyboard shrinks the VISUAL viewport; position: fixed and getBoundingClientRect both speak the LAYOUT viewport, which does not change. So the pinned mobile preview sat ~200px above the visible band and only showed up once the keyboard was dismissed. Everything that has to land where the eye is must go through visibleBand() (visualViewport offsetTop/height), and the keyboard opening arrives as a visualViewport resize — not a window resize and not a scroll.

• A harness cannot test frame callbacks. The Browser pane reports document.visibilityState === 'hidden', so requestAnimationFrame and IntersectionObserver never fire there — silence from them is the environment, not your code. The sticky mobile preview therefore decides visibility with two getBoundingClientRect calls on a time-throttled scroll handler: no frame callback, testable, and it also survives a backgrounded tab in the wild.

• document.fonts.check() does not check what you think. It answers "can this text be rendered", and returns TRUE for a family that was never declared, because a fallback always can — verified on a bare page: check('40px "Astir"') is true with zero registered faces. The descriptor's guard used it to decide whether the captured line breaks were trustworthy, so the guard never fired: whenever a webfont failed to load, the fallback's line breaks were recorded and print drew them verbatim. Ask document.fonts for a face with that family at status loaded instead. Note the two safe defaults point opposite ways: when the answer is unknowable, live-preview.js says NOT loaded (record null, let print re-wrap) and live-preview-health.js says loaded (never raise a false alarm).

• ⚠️ lines and sizePct are captured together, and they fail together. This is what printed order 7673, and the second half of that sentence is the part that cost an extra round: the first fix dropped the bad lines and kept the sizePct beside them, which produced a different wrong cover.

The customer typed Nef & Dim ⏎ ⏎ “Our wedding”; the descriptor recorded ["Nef & Dim", "", "“", "Our w", "edding”"] at sizePct 22.4, and print drew those five lines verbatim. Stelios re-ran the product on a desktop and two phones: all three showed “Our wedding” on one line.

The mechanism, measured on the order itself. Its box is 304 × 276 pt and the face is Marilia. sizePct is a % of box HEIGHT, so 22.4 means 61.8 pt — and at 61.8 pt “Our wedding” measures 352 pt against a 304 pt box, 16% too wide to ever fit. Worse, five lines at that size need 355 pt of a 276 pt box. No layout can satisfy both numbers, so they did not come from one layout: fitText() returns early when the box has no width or height, leaving _effSizePct at the value from an EARLIER fit while updateDescriptor() went on to record breaks from a DOM that had never been fitted at all. A collapsed or mid-load box also explains the shape of those breaks — a lone “ on its own line is what a very narrow box does, and overflow-wrap: anywhere is what let it split w|edding. That is why no width in Chromium ever reproduced it: the box was not narrow, it was unfitted.

Four things now stand between that and a printed cover: - fitText() reports whether it ran (_fitOk), and no lines are recorded when it did not. This is the root cause and the only one of the four that prevents the bad pairing from being created. - reflow() re-captures the descriptor. It re-fitted on every resize and hero load and never re-recorded, so a rotation, a keyboard dismissal or a slow cover finally decoding left an order carrying an older layout. Proven: the overlay went 486 px → 152 px, visibly re-wrapping, and the stored lines came back byte-identical. Kept in the suite as a control. - overflow-wrap: anywhere → break-word on .lp-overlay-text, .lp-cart-text, .lp-mini-text. anywhere is what permits w|edding. - A break that splits a word which would have fitted alone is refused at capture (wrapsAreCredible() — it measures the word against the box).

Downstream, REPAIR — never discard. For orders already taken there is no box to measure, and dropping lines leaves the renderer to re-wrap at the sizePct that failed alongside them: on 7673 that gives “Our / wedding”, which nobody saw either. Rejoining the impossible breaks keeps what the capture got right and hands the consequence to the shrink-to-fit loop each renderer already has. Measured: repair lands at 52.5 pt against the 51.2 pt the customer's own screen used — three lines, Nef & Dim / blank / “Our wedding”, within 2.5%. repairLines() in live-preview-cart.js (cart, drawer, notification, order viewer) and lines_guard.py in stamp.py, mockup_png.py, order_to_md.py. Only lines that no longer spell out the text are dropped — there is nothing to repair there. A break after a dash or a slash is left alone; any engine would take it.

The consequence is still a design one: lines was introduced so the three renderers could not disagree, and it does that job well — but it makes the browser the single source of truth for something we never verify. The standing recommendation is unchanged: capture an actual image at checkout (see Open) and demote lines to a hint.

• ⚠️ pre-wrap lets a transcript grow a line. The cart renderer's fit accepts a line up to 1px over the box — a sensible tolerance — and under white-space: pre-wrap that 1px is enough for the browser to break it. So after repairLines() had already put “Our wedding” back together, the order viewer measured 278px against a 277px box, accepted it, and then wrapped it into two lines again: the page warned about a break and drew one. Recorded lines are a transcript — applyText() now puts them under white-space: pre, which cannot wrap, the same rule stamp.py and mockup_png.py follow by drawing them verbatim. Raw text (no recorded lines) is meant to wrap and keeps pre-wrap. Worth remembering as a shape: a fit tolerance plus a wrapping mode is a silent line-count change, and the line count is the whole contract here.

• ⚠️ Eight of the eleven catalogue faces paint outside their line box. ascender - descender per face: Memories 1.367, Astir 1.349, Roboto 1.319, Marilia 1.250, Royal 1.250, Nadiri 1.200, LittleFont 1.200, Holy 1.159 — all above the line-height: 1.15 the overlay uses. The glyphs paint outside their line boxes, and .lp-overlay / .lp-cart-overlay are overflow: hidden, so when the text block fills the box the last line's descender is guillotined. That is why the g of wedding was cut in 7673's order preview: the wrong 4-line layout filled the box. Print is not affected — stamp.py draws at explicit baselines and never clips — so no cover was ever at risk; only the previews lie, in the alarming direction. With sizeStart at 85% the block normally uses ~85% of the box and the descender has room; it is reachable when a customer presses + all the way to the fill. Not fixed — see Open, and note that any fix which reserves the room inside the box makes every preview (and therefore every print, since sizePct travels) slightly smaller.

• What was asked for and what was painted are different facts. The descriptor used to record only the first: font.family from this.state.font and base from config — the instructions, not the result. An order could not be checked against what the browser actually did. It now also carries renderedBase (heroImg.currentSrc) and font.renderedFamily (getComputedStyle(overlayText).fontFamily), and the order viewer compares them: a personalised cover that 404'd shows up as «ο πελάτης έβλεπε ΑΛΛΟ εξώφυλλο», a webfont that never arrived as «οι γραμμές μετρήθηκαν σε λάθος όψη». Nothing renders from either — they are evidence, and they cost ~40 bytes in a descriptor that measures ~460. Compare them by file, not by string: Liquid emits protocol-relative URLs and currentSrc always resolves to an absolute one, so a raw comparison flags every order, and a warning that is always on is a warning nobody reads.

• A listener wired inside the condition it is waiting for never runs. The sticky mobile copy attached its scroll and visualViewport handlers inside ensureMini(), which only runs once the copy is wanted — and it is only wanted once the hero has scrolled away. Nothing was listening for the scroll that would have asked, so the copy appeared only when some unrelated resize happened to call in: on a real phone the address bar collapsing does exactly that, which is why it looked like it worked. On a viewport that does not, it never ran at all — reproduced headless, where the old code shows nothing. watchMini() is now wired from updateMini(), before the gate.

• ⚠️ A layout rule that reads its own output flickers. paintMini() widens the sticky mobile card when the letters would fall under the readability floor, and it decided that from the card's CURRENT width — one it had set itself on the previous pass. That is not idempotent, and the failure is sharp rather than gradual: the rescue is clamped to cap, so a card needing more than the cap lands exactly ON it, cardW < cap turns false, the release branch fires, and the next pass widens it again. Reproduced on the live PDP of «Πες μου Παππού» at 375px with a 137-character dedication: 165 → 203 → 165 → 203, a 38px swing on every keystroke and every scroll tick. A short dedication converges on a fixed point instead of flipping, which is why it looked intermittent rather than broken. The decision is now always made from the card's NATURAL width, and the width is rounded to whole pixels because clientWidth is an integer and a fractional width makes the next pass measure something different from what it set. General shape: any adaptive rule must be a pure function of inputs it does not itself write.

• Normalising the box is not normalising the text. The mini crops so the box fills a fixed share of the card, which reads like it equalises everything. It does not: the text is a %% of the box HEIGHT while the card is sized by WIDTH, so a flat box yields a short card and tiny letters. Measured across the catalogue the copy ran 8.9px on Πες μου against 20.9px on the wedding book. There is now a floor: below MIN_TEXT_PX the crop tightens (bounded by FILL_MAX, so the customer's own text is never cut), and if the box is so flat that even that is not enough, the card is allowed to grow wider — it is a short strip, so the width is cheap. Both are one-directional: nothing that already read well gets smaller.

• ⚠️ The monitor treated an ordinary sale as a breakage. The overlay is created LAZILY, on the customer's first interaction — deliberately, so a cold PDP keeps the native gallery as its LCP element. But audit() checked for the overlay before it checked whether there was anything to render, so a customer buying one of these books WITHOUT personalising it produced overlay-missing, which is fatal: they were shown «η προεπισκόπηση δεν φόρτωσε σωστά» and had add-to-cart disabled at the moment of buying, on a shop that was working perfectly. Order #7674 is the sale it happened to — the beacon fired at 18:40Z, the order landed at 18:47Z with the personalisation field empty. Reproduced live on our_wedding before the fix. audit() now returns early when there is no overlay AND no text: that is not a broken page, it is an untouched one. A genuine breakage — text present, overlay gone or collapsed — still reports, still shows the notice, still holds the cart; there are assertions for both directions.

Two lessons worth more than the fix. A monitor's false positives cost real sales, so its threshold deserves the same scrutiny as the thing it watches. And this had been live since the monitor shipped: it stayed invisible only because the forwarding trigger was missing, so the alarm had nowhere to ring. Connecting a monitor is what tells you whether the monitor is right.

• The worst failures render perfectly. If the personalised cover 404s, swapHero() never paints it — it reveals the overlay anyway and the hero keeps showing the GALLERY photo. The customer's name then sits on generic artwork: no exception, naturalWidth healthy, overlay mounted, descriptor valid, text drawn at the right size. Every plausible health check passes. Only comparing what is ON SCREEN against what the descriptor claims as base catches it, and that comparison has to wait for the swap to settle or it invents faults mid-flight. This is why live-preview-health.js asserts success instead of watching for errors — the same shape as the two failures that reached customers.

• A shared property name is not just a parsing problem. Cloudlift's app writes a line item property also called _preview, holding a rendered PNG URL, so the order admin renders whatever sits there as an <img>. Our JSON descriptor under the same name therefore came out as a broken image on every personalised order — the print pipeline had always coped (it sniffs for JSON with a layers array), which is exactly why the admin-side damage went unnoticed for weeks. Renamed to _lp_preview, which is ours alone. Readers still accept _preview for orders that predate the rename, and only those need the JSON sniffing.

• An alternate template is inert until something selects it. page.cover-preview had shipped for weeks, yet every _preview_link opened the ordinary shop page: the Cover preview Page still carried the default template, so Shopify rendered sections/main-page.liquid — the page title over an empty body — and dropped the ?d= descriptor without a word. Nothing in the theme can detect that, because Liquid cannot read query parameters at all (request exposes the path, not the query string), so the viewer could not even report its own absence. The fix is to select the template from the URL: ?view=cover-preview overrides whatever the admin assigned. The storefront now writes it into every link, and main-page.liquid bounces links that predate it — guarded on view= being absent, or a genuinely missing template would redirect forever.

• Anything pinned to the PDP has to stand down when the cart panel opens. The panel is Dawn's <cart-notification>, which lives inside the header section and hangs from it — so it is below z-index: 3 and anything above that paints over it. The mobile sticky add-to-cart bar (#moodbook-mobile-sticky-atc, a custom_liquid block in templates/product.moodbooks-redesigned.json) sits at z-index: 9999 and nothing took it away, so it covered the panel's own «Συνέχεια» / «Προβολή καλαθιού» and swallowed their taps — the customer aiming for the cart hit Προσθήκη and bought a second copy. It read as intermittent because it only bites once the panel is tall enough to reach the bar's band: on a 390×780 screen the bar owns everything below 713px, so a 414px panel is fine and a 714px one is not. The bar now carries the same body:has(#cart-notification.active) rule as the Reads-Inside floating button, plus a MutationObserver for browsers without :has(). The Live Preview mini card does NOT have this problem and must not be "fixed": its z-index: 2 is deliberate and keeps it under the header — verified, not assumed.

• Dawn hides empty elements. base.css carries div:empty, p:empty, h1:empty … { display: none }. A harness that spaces a page out with <div style="height:900px"> gets a page 129px tall and every scroll test lies. Put something inside.

• Liquid emits protocol-relative URLs (//cdn.shopify.com/...). Browsers resolve them, Python does not.

• Blank lines leave no glyphs, so PDF text extraction cannot see them even though both renderers give them space. check_match.py compares only the text-bearing lines and divides each measured gap by the line boxes it spans; anything NEW that measures a PDF must likewise remember the boxes it cannot see, or a healthy blank reads as double leading (it blocked real dedications until 2026-08-24).

Monitoring — how a breakage reaches a human

Two failures reached customers before anyone noticed, and neither threw an exception: the order's cover-preview link opened an empty page for weeks, and the descriptor rendered as a broken image in every order admin. Both would have been invisible to a conventional error tracker. So the chain below does not watch for errors — it asserts, after every customer interaction, that the things which MUST be true are true, and reports the ones that are not.

assets/live-preview-health.js   asserts success on the PDP, per customer
        │  navigator.sendBeacon (faults only, no personal data)
        ▼
Google Form  ──► responses Sheet  ──► tools/live-preview/fault-to-github.gs
                                              │  onFormSubmit trigger
                                              ▼
                                GitHub issue, label live-preview-fault
                                (repeats become comments on the open issue)

✅ Verified end to end on 2026-08-21, after the trigger was installed. A fault posted to the Form became a GitHub issue in 3 seconds, and a second occurrence of the same fault+product became a comment on that issue rather than a new one, in about 20 — so the comment count really is the severity. The storefront half was verified live in the same pass: the monitor detects a collapsed overlay, holds add-to-cart, shows the Greek notice with role="alert", and the beacon carries no customer text.

It was NOT connected before that: the installable trigger (setup step 4) had never been added, so three real faults sat in the Sheet and raised nothing. The trap is worth keeping in mind whenever this is touched again — ** selfTest() runs inside the script, so it proves the token, the permissions and the repository name, and says nothing about whether anything calls onFormSubmit.** Green selfTest plus a silent GitHub is indistinguishable from a shop with no faults. Same shape as document.fonts.check(). The only end-to-end test is: submit the form's pre-filled link, watch for the issue.

onFormSubmit fires on NEW submissions only, so the three rows that predate the trigger will never be forwarded. They were resolved by hand: self-test and artwork-mismatch were this session's own testing, and the real one — overlay-missing at add-to-cart on our_wedding/Snow, 18:40Z — was checked afterwards on that exact variant and found healthy (overlay mounted and sized, artwork matching the descriptor, both audits clean). It fell within minutes of a deploy: main IS the live theme, the integration writes the files one at a time, and a customer loading the page across that window can get a mismatched pair of assets. A deploy can therefore raise its own fault report — and hold a real customer's add-to-cart while it does. Worth knowing before reading the next one as a defect, and worth preferring quiet hours for a deploy that touches several files at once.

Things to know before changing any of it:

• The token is never in the repository, never in the theme, and never in a chat. A storefront cannot post to GitHub directly — that would publish a token to every visitor — which is the entire reason the Form sits in the middle: the ingress needs no secret, and the secret lives server-side in the shop's own Google account.
• The beacon carries faults only, never the funnel. Engagement and abandonment go to GA4/Clarity, where cookie consent governs them; a fault report carries no identifier and no typed text, so it must keep working for a customer who declined analytics — their broken sale counts the same.

⚠ That was the design, and for five days it was not the behaviour. report() called dataLayer.push and nothing else, which reaches GA4 only through a GTM container forwarding it — and this shop has none: gcm-integration-script.liquid defines gtag as the consent-mode wrapper, and Shopify's own tag reads gtag, not arbitrary dataLayer rows. Measured 2026-08-23: GA4 held fourteen event names since the cutover and not one was lp_*, while the sticky bar's two were there because its trackEvent() calls gtag as well as pushing. Every engaged / submitted / abandoned this file had ever recorded went nowhere. report() now calls gtag('event', …) first and keeps the push for a container that may exist one day. Six assertions cover it, including that the customer's text is in none of it.

The shape is the one this whole system exists to catch, turned on itself: the sending half was green throughout and said nothing about whether anything was listening — the same as selfTest() with no form trigger, and the same as document.fonts.check(). And it stayed invisible because nobody misses numbers they have never seen. The FAULT beacon was never affected; it posts to the Form directly and is verified end to end. - The customer's typed text is a personal name and is never transmitted — only its length and line count. There are tests asserting this; keep them. - font-fallback is deliberately non-fatal. The print is still correct when a webfont fails; only the preview looks wrong. It reports without holding the cart. - A fault NEVER holds add-to-cart any more. It used to: setHalted() showed the notice and disabled [name="add"], on the stated trade of losing a sale rather than taking an order nobody can print. The premise was false. The personalisation textarea is an ordinary form field (name="properties[…]", form=<product form>), so the customer's words reach the order by the browser's own doing even if every line of this feature fails; and the geometry, font and artwork are ours, in product-configs/*.json and covers.production.json. There is no order here that nobody can print — only orders that need a rebuild, which descriptor_rebuild.py now does, flagged in the batch as ΑΝΑΚΑΤΑΣΚΕΥΑΣΜΕΝΑ for a human to look at.

So of the twelve faults that used to block a sale, eight described a preview that looked wrong on an order that would have printed perfectly, and four described a missing descriptor that is now rebuildable. setNotice() informs and gets out of the way; it still re-enables any button an older version of the file disabled, so a page left open across the deploy recovers on its next audit.

• The monitor repairs before it complains, and never speaks on the first audit. A fault at t+2500 is usually a hero still decoding or a layout that has not settled; saying so to someone mid-flow creates the problem it is describing. One lp:recover event per session asks the element to re-mount and re-render, and only what survives that, at t+8000 or at add-to-cart, is shown to the customer. Everything is still reported to the beacon either way — we want to know about faults we chose not to mention.

And the notice takes itself down. Audits run at fixed moments, so once it was up nothing was watching any more: a customer could rotate the phone, scroll, or simply let the cover finish loading, and still be told the preview was broken while it sat there working. A 3-second re-check runs only while the notice is showing and clears it the moment it stops being true. A message that outlives its problem is its own problem. - One issue per fault per product, not per customer, so the comment count on a thread is the severity. Close the issue when the fix ships.

Open

Live Preview — in priority order. Items 2–7 of the previous list shipped on 2026-08-21; what is left is the one that was always the real answer, plus the things that were never about lines.

0. ✅ ANSWERED 2026-08-31 — the m=dom yield is not the problem, and iOS is not the problem. This item said "exactly one witness exists (#7688)" and feared that iOS Safari would yield desc, in which case the gate would cover a permanent minority. Measured on the unfulfilled queue of 2026-08-31: 41 personalised line items, 31 with a witness, and all 31 are m=dom. Not one m=desc. The foreignObject raster succeeds on what customers actually use, one of them confirmed on mobile Safari.

What is left of this item is smaller and different. Of the 10 line items with no usable witness: 3 predate the capture code (#7669, #7670, #7695 — the last one's CART LINE was built 2026-08-23, before c3315c2 reached main at 13:42 on 08-24, so its input element did not exist yet), 2 are line items where nothing was typed at all (#7727's «Πες μου Μπαμπά» / «Πες μου Μαμά» — empty text property, empty _lp_preview), and 5 carry _lp_image present but empty. Three states, not two, and the third is the only one that is a fault. ⚠ run_batch.py reports all of them with the same sentence, "κάθε παραγγελία πριν τις 24/08", which is false for the empty ones.

🔴 Still owed: why the 5 are empty. lp_capture (dom/desc/none) rides on every health event and would answer it with real volume — 35 events are already collected — but it was never registered as a GA4 custom dimension, so GA4 discards it. That is an Admin change, no code.

Also still owed: re-derive PRINT_GATE_SPEC.md §10's counts. fetch_orders.py now works from a machine that can reach the shop, so the blocker is gone.

1. 🔴 Capture a real image of the hero at add-to-cart, and make it the single source of truth. Everything downstream still re-derives the cover from a descriptor, which means the customer's screen is authoritative for wrapping but is still never actually recorded. The guards shipped for 7673 catch a capture that is implausible; they cannot catch one that is merely wrong in a plausible way, and renderedBase/renderedFamily prove what was painted without proving how it flowed. The cover is served same-origin (moodbook.gr/cdn/…), so the canvas is untainted and toBlob() works; what is missing is somewhere to put the blob. Plan: a Cloudflare Worker + R2 bucket exposing one authenticated PUT, returning a URL stored as a line item property alongside the descriptor. Print reads the image, the descriptor stays for machine-readable geometry. Precedent: this is how Cloudlift behaved, and it is why its orders never produced a dispute even when its own line items disagreed with its preview. The open question is where to start, and the answer is the Worker — it is the only piece with no local stand-in, it can be built and tested against a bucket before a single line of theme code changes, and until it exists the storefront half has nowhere to PUT to.

The Worker is LIVE since 2026-08-23 at https://moodbook-cover-capture.stelios-d58.workers.dev, bucket moodbook-covers (EEUR), covers deleted after 30 days, 10 writes/min per IP. 38 assertions, plus end-to-end verification against the deployed service: a real PNG round-tripped byte-identical, a foreign origin got 403, a GIF declared as PNG got 415, the eleventh write in a minute got 429. The storefront half SHIPPED in c3315c2 and has been uploading ever since. This paragraph said it was not written until 2026-08-24, while captureCanvas() and runCapture() were live in assets/live-preview.js and orders were carrying properties[_lp_image]. Do not trust a status line here over the code again — the reader of this file is usually about to change that code.

⚠ Do NOT wire print to the captured image. The sentence below still recommends it. Until the capture has a shrink loop and a clip it is corroboration only, never a print master: it was drawing every cover in the PRINT SPREAD's coordinates on top of the STOREFRONT PHOTOGRAPH — 100% of captures, every product, since it shipped — because boxPt/artworkPt and box % are bridged by calib, which never travels in the descriptor. Fixed 2026-08-24; the frame is now asserted by the harness against the overlay's own rect rather than by recomputing the implementation's formula.

Writing it found a hole in the plan above: "one authenticated PUT" cannot exist. The caller is the customer's browser, and no secret ever reaches the storefront — the same wall that put a Google Form in front of the GitHub forwarder. The endpoint is therefore credential-less and defends by shape (the Worker names the object, Origin allowlist, magic bytes over Content-Type, a ceiling on bytes read), with the real backstop being an R2 lifecycle rule plus a rate-limiting rule — neither of which wrangler deploy sets up. What is left is not code: a Cloudflare account, a DNS record, and an answer to how long the shop keeps a picture with a customer's name in it. See tools/cover-capture/README.md → Retention and What this costs. 2. Optional: move the fault watch to a Routine created from claude.ai with the Google Drive connector attached, so a session can read the Sheet directly; then delete the session-bound Routine trig_017i9DmaGiqan4oWn3mqyupg. The connectors parameter is not available to this organisation from inside a session, which is why the GitHub forwarder exists at all.

3. ⏳ Three measurements owed, from the 2026-08-22 abandoned-checkout work. Scripts and the traps around them: tools/abandoned-checkouts/. - Does the 1-hour recovery email now reach non-subscribers? It never did. Its «Send marketing email» step was locked to only subscribed customers while the 24-hour one was set to All customers — so every first-time visitor, who is always NOT_SUBSCRIBED because the customer record is created in the same second as the checkout, skipped the free reminder and was handed the 10% discount a day later instead. The owner changed the setting on 2026-08-23. Success looks like sends at 1.1h to NOT_SUBSCRIBED customers; re-run check_recovery_emails.py. - Does an UNSUBSCRIBED customer still receive nothing now that the audience is All customers? One opted out at 2026-08-23T12:44:59Z, ten minutes before abandoning; their 24-hour email fell due 2026-08-24T12:55Z. If it went, that is a compliance problem and the setting goes back. - Does the dialog work? lp_submitted in sessions with no lp_engaged, measured per session — not as a difference of event totals, which cannot tell you whether the one who submitted was one of the two who engaged. Needs ga_session_id as a dimension, or a segment in an Exploration. Give it two weeks: the funnel telemetry only started reaching GA4 on 2026-08-23 (16233e1), having pushed to dataLayer alone since the cutover. Keep lp_abandoned separate — engaged then left is a different population from never engaged, and wants a different answer.

Closed 2026-08-23 — the clipped descenders, and why the metric lied

The item that sat here as #2 is shipped. What it described was not what was happening, which is why two synthetic reproductions of it measured the wrong thing and why it stayed open through three sessions.

It ranked the faces by ascender - descender against a line-height of 1.15. Both halves of that are wrong. The overlay does not use 1.15 — live-preview.js sets line-height per face from font_sets.json, and those run 0.65 (Qute) to 1.30 (Memories). And a face's declared box is where the designer said glyphs MAY go, not where a given string's glyphs DO go; only the second can be clipped.

Measured in Chromium with all eleven faces self-hosted, each at its own leading, against real Greek customer strings — actualBoundingBox*, not the font metric:

So: the worst face is Marilia, not Memories. The overrun is not always a descender — Cavafy clips at the TOP, and it is the face the old rule called perfectly safe. The largest overhang of all is horizontal, Marilia's capital Ν opening on a swash 0.67 em before the origin. And Astir — the face whose cut g opened this whole question — barely overruns at all, which is the strongest evidence that the g in 7673 was the wrapping bug and not this one.

inkBleed() therefore measures the ink of the actual string, in the actual face, at the actual size, on every fit (0.14 ms), and applyInkBleed() grows the clip with clip-path: inset(), which takes NEGATIVE values. Both surfaces that clip have it: the PDP overlay and the cart/order-preview overlay.

⚠ The clip may grow; the box may not. The first attempt padded the overlay instead. Padding changes the content box, so every caller that sets overlay.style.width silently got a narrower box, and «Our wedding» came back as «Our weddin / g» — the 7673 shape, reintroduced by a fix for a cosmetic clip. Four existing assertions caught it within one run. clip-path leaves the geometry untouched, which is the whole reason to prefer it; overflow goes to visible because it would clip first, so a browser too old for clip-path shows the text uncropped rather than cropped — the better of the two failures.

Verified by 58 assertions (48 existing, 10 new) in tools/live-preview/harness/. The new ones assert that the box is byte-identical with a bleed applied, that the content box the text wraps inside is unchanged, that every measured overhang is covered to the pixel, and that the clip is dropped when the field is cleared. The 7673 text is not re-driven across 28 widths here — the bleed applies on every fit, so the existing loop already runs with it live.

Three more cover an OPTIONAL hook, [data-lp-link-visible], filled with the same viewer URL as _preview_link. Shopify hides _-prefixed properties, so the cart is the customer's last sight of their own cover; a property whose name has no underscore reaches the checkout and the order confirmation. live-preview.js fills the element and never requires it — the input, and above all its NAME, is customer-facing copy and belongs in live-preview.liquid for Stelios to approve.

⚠ Nothing uses it, and the case for it was withdrawn. It came out of the abandoned-checkout analysis: a customer who went from checkout back to the cart three times on 2026-08-22 and then left, read as someone checking that what she typed had survived. The gaps between those pages were 11.5s and 39s — too fast to have looked at anything, so a slow checkout fits better, and that session withdrew the reading. Stelios then chose to wait for the real captured image rather than ship a link. The hook stays because the mechanism is still right for the capture: when tools/cover-capture produces an image URL, the element goes into live-preview.liquid in one line and this half already works. Do not re-document it as an abandoned-cart fix.

The WebKit suspicion is retired. It was never WebKit: fitText() had bailed on a box with no size, so 7673's breaks came from a DOM nobody had sized, paired with a sizePct from an earlier fit. Chromium reproduces nothing because there was nothing engine-specific to reproduce. Do not spend another session on it.

Closed on 2026-08-21 — kept here because the next session will look for them: fitText() refuses to pair an unfitted capture with an older size · reflow() re-captures the descriptor · overflow-wrap is break-word everywhere · the mid-word guard runs at capture, and downstream the break is repaired rather than discarded · the descriptor records the rendered font and artwork · the order viewer warns and draws what will print · order 7673 prints Nef & Dim / blank / “Our wedding” · the docs portal is regenerated from build_docs_portal.py · the sticky mobile card holds one width across repaints · an order with no personalisation gets its standard cover instead of being flagged as manual work · the fault monitor is connected and proven end to end.

Print / production (unchanged):

• Bleed: TrimBox = BleedBox = MediaBox on all 80 artworks — zero declared bleed. A question for the printer.
• mockup_png.py --upload written but untested (needs write_files/write_orders).
• Mixed colour models in the artwork: 13 spot /Separation, 3 /DeviceRGB, no CMYK.
• Font licensing: print vs web embedding, per family.

Historical — pre-cutover notes

Status as of 2026-07-07. This is the context to resume work in a new session.

What it is

An in-house replacement for the Cloudlift "Live Product Options" app on the moodbook.gr PDP. The customer types a personalization (name/dedication), which is drawn live on the hero gallery image in the selected variant's colours, and captured into the order. A local pipeline turns each order into a print-ready PDF. Goal: full control, no subscription, and it removes Cloudlift (the documented CLS/LCP root cause).

Verified working on the live PoC product our_wedding (wedding planner).

Branch & deploy

• Merged into main on 2026-07-08. All the Live Preview + cart/order preview
• docs-portal work is on main (repo github.com/saristidis/moodbook-theme).
• Deploy = the Shopify GitHub integration. The LIVE theme moodbook-theme/main (#199828406604) on store moodstaff.myshopify.com auto-deploys from the GitHub branch main. So deploying = git push origin main — do NOT use shopify theme push (it bypasses the integration and causes drift). The theme editor commits its own changes back as "Update from Shopify…" commits, so always git pull before you push. The dev theme #201616654668 is unpublished.
• Non-theme repo paths (moodbook-design-system/, tools/) are ignored by the integration ("… was ignored" warnings) — expected and harmless.
• Pending admin steps on the live theme (one-off, per page/product): 1. Create the Cover preview Page (handle cover-preview). Assigning the cover-preview template to it is optional since 2026-08-20 — the link carries view=cover-preview and sections/main-page.liquid bounces older links — but assigning it saves the extra redirect. 2. Add the "Live preview (in-house)" block to the product in the live theme editor (the code is deployed but the block is not placed, so the PDP is unchanged for customers until you add it).
• Verified live 2026-07-08: moodbook.gr/pages/team-docs serves the docs portal; the GitHub integration reported "16 succeeded, 0 failed, Theme updated!".

Knowledge database (standing convention)

Every new custom app/feature we build for the e-shop must also be added to the internal knowledge database — the searchable team docs portal (moodbook.gr/pages/team-docs). To do it: add the new tool's .md doc(s) to the DOCS list in D:\Shopify Coding\DOCS_PORTAL\build_docs_portal.py, then run D:\Shopify Coding\DOCS_PORTAL\update_docs.bat (regenerate → commit → push → the integration deploys). Treat this as part of "done" for any new e-shop app.

Files

Theme (storefront): - assets/live-preview.js — the <live-preview> custom element. Lazy-inits on first interaction; draws the text overlay on the hero image; per-variant base swap + preset colour; multi-line wrap; autofit-fill + optional −/+ size stepper (hybrid); the untouched size lands at sizeStart%% OF THE FILL (default 85, the size_start block setting or size.start in the metaobject) rather than on the fill itself — short text used to stop growing only at the font's own ceiling and arrive at full size, which reads as enormous and left + already disabled; alignment icon buttons; full-width font dropdown (each option in its own font); sets font-family with !important (beats theme rules). Captures the line item properties below. - snippets/live-preview.liquid — renders the block: reads config metafields, builds the config JSON (data-config), the controls (labels use the shop's .form__label; blank label ⇒ hidden), @font-face for custom fonts, and the debug readout. This is where all the Liquid config-reading lives. - sections/main-product.liquid — has the live_preview block type (a when case + a schema entry). Add the block to a product template via the theme editor. - assets/live-preview-cart.js — renders the personalised cover wherever a line item shows (cart page, drawer, add-to-cart notification) by overlaying the text on the base <img> from the descriptor; click-to-zoom modal; shrink-to-fit. Loaded globally in layout/theme.liquid (the drawer/notification inject HTML via the Section Rendering API, which doesn't run inline <script>s). - assets/live-preview-health.js — the monitor. A PURE OBSERVER (reads the DOM, never calls into <live-preview>, every path wrapped) that asserts success rather than catching exceptions: overlay mounted and not collapsed, artwork decoded, descriptor written and parseable, text drawn and sized, the real webfont in use, and the artwork on screen matching the descriptor's base. Audits at 2.5s and 8s after engagement and again at add-to-cart, and reports lp_engaged / lp_broken / lp_submitted / lp_abandoned to dataLayer (GA4) and clarity() tags, plus an optional health_beacon_url. Never sends the customer's text — length and line count only; the text is a personal name. On a FATAL fault it also shows the customer the fault_message notice and holds add-to-cart, because someone who cannot see their cover is one click from an order we cannot fulfil, and the refund costs more than the sale. Reversible, and it never re-enables a button it did not disable (that button may be sold out). font-fallback is deliberately not fatal: the descriptor now records lines: null in that case, so print re-wraps and the order is still correct. The beacon carries faults only, never the funnel. Behaviour is analytics and cookie consent governs it; a fault report carries no personal data and no identifier and exists so a broken shop reaches a human, which must not go quiet precisely when the customer declined analytics. makeSender() understands a Google Form's PRE-FILLED link specially — it reads the entry.N field id out of the URL and rewrites /viewform to /formResponse — so the whole backend is a form whose responses land in a Sheet, with one setting and nothing to look up. Any other URL gets the same payload as plain JSON. - tools/live-preview/fault-to-github.gs — Apps Script bound to the responses Sheet. On each form submit it opens a GitHub issue for the fault, or comments on the open one if that fault is already known for that product, so the comment count IS the number of customers hit. Why GitHub and not the Sheet: Drive is a claude.ai connector and connectors do not pass to the fresh sessions a durable routine fires, while GitHub comes with the environment; and an issue notifies by email on its own, so the alarm stops depending on anything being scheduled at all. The token is a fine-grained PAT limited to Issues:write on this repository and lives in Script Properties — never in the storefront, which is why the Form is the ingress in the first place: it needs no secret. - snippets/live-preview-cart.liquid — cart line item media for personalised items; wired into sections/main-cart-items.liquid, snippets/cart-drawer.liquid, sections/cart-notification-product.liquid. - templates/page.cover-preview.liquid — standalone (layout none, noindex) viewer that re-renders a cover from ?d=<base64 descriptor>; the order's _preview_link points here so the merchant can verify before printing. Setup: create a Page with handle cover-preview (one page total, reused per order via the ?d= param — nothing is created per order). The template selects itself via view=; assigning it in the admin is optional.

Captured line item properties (per personalised item): - Κείμενο (visible) — the customer's text. - _lp_preview (hidden) — the JSON descriptor; the print/render source of truth. Also carries base (cover URL) and font.url (selected font file) so it is self-contained, plus the FINAL fitted sizePct. Was _preview until 2026-08-20 — see the Cloudlift collision below; every reader takes both names, so orders placed before the rename still print. - _preview_base (hidden) — base cover URL, so the cart shows the base <img> in Liquid natively (works without JS). - _custom_product = Yes (hidden) — flag to hide cash-on-delivery etc. for custom items; on the order + checkable in Liquid (item.properties['_custom_product']). - _preview_link (hidden) — {shop}/pages/cover-preview?view=cover-preview&d=…, clickable in the order admin to view the zoomable cover. (Underscore = hidden from the customer, shown to the merchant in admin.)

Tooling (tools/live-preview/): - box-tool.html — standalone drag-to-place tool. Load the base cover image, drag the box, add customer fonts; outputs the JSON to paste into the metafield. - fonts_to_woff2.py — converts TTF/OTF/TTC/WOFF → WOFF2 and prints each font's family name + the box-tool line. - order_to_md.py — Shopify order JSON → one MD descriptor per personalized item. - stamp.py — PyMuPDF; stamps the descriptor onto the per-variant base PDF at the same geometry (points), with wrap/align/two-axis fit. --verify, sample fixtures. - README.md — tool usage. USER_MANUAL.md — merchant setup guide (Greek).

Requires: pip install pymupdf "fonttools[woff]" brotli (all installed here).

Config model (IMPORTANT)

Two layers, both authored by the merchant, read by snippets/live-preview.liquid:

Per product — ONE product metafield custom.live_preview, type JSON. Paste the box-tool output verbatim:

{ "box": {"x":23,"y":17.7,"w":54.4,"h":20.9},   // % of the cover image (storefront)
  "artworkPt": {"w":992.1,"h":708.7}, "boxPt": {...}, // print points (from the PDF)
  "font": {"family":"Astir","sizePct":70,"leading":1.15,"color":"#3a2a1a","align":"center"},
  "fonts": [ {"label":"Χειρόγραφη","family":"Astir","url":".../Astir.woff2",
              "sizePct":70,"leading":1.15} ] }

Per-font size & leading (2026-07-16)

Every font carries its OWN sizePct and leading — script/handwritten faces behave nothing like a text face, so one shared value can't serve them all. Authored per font in the box-tool (dropdown → tune → each remembers its own). - sizePct = % of the box height, and a CEILING — the auto-fit finds the largest size ≤ it that fits both axes. The box-tool prints the auto-fit result so you can see when the text can't reach your ceiling. - leading = a MULTIPLIER of the size (1.15 = 115%), never fixed points — the size changes per order (auto-fit / the −+ buttons), so only a ratio survives. It is the CSS line-height in the browser and lineheight= in stamp.py. - The snippet passes fontSpecs (family → {sizePct, leading}) in the config JSON; live-preview.js applies the selected font's pair on change and re-fits from that baseline; the effective sizePct + leading travel in the descriptor, so the cart preview, the order viewer and print all reproduce them. - Metafields written before this fall back (block font_size_pct, leading 1.15).

Per variant — two Variant metafields: - custom.preview_base_image (Image/File) — the blank personalisable cover. - custom.preview_text_color (Color) — the preset text colour.

⚠️ The custom_ prefix gotcha (cost us hours)

When a metafield definition's Name is typed as custom.preview_base_image, Shopify stores the key as custom.custom_preview_base_image (it prefixes custom_). snippets/live-preview.liquid therefore reads BOTH keys: v.metafields.custom.custom_preview_base_image | default: v.metafields.custom.preview_base_image. When creating definitions, put a plain Name (preview base image) and let Shopify derive the key.

Storefront behaviour (the dynamic flow)

1. Cold load: native gallery only (fast LCP, 0 CLS). No overlay work.
2. First keystroke: overlay mounts on the hero image; base swaps to the selected variant's base cover; text takes the variant's preset colour.
3. Clear text → reverts to the variant's original product image.
4. Switch variant → keeps the text, re-renders on the new variant's base + colour.
5. On add-to-cart: the properties listed above are captured (Κείμενο, _preview incl. boxPt/artworkPt/base/font.url, _preview_base, _custom_product, _preview_link).

Key implementation notes (each was a bug we fixed — do not regress): - Overlay positions over the real image content box (object-fit letterbox math), not the container, so it matches the box-tool at any size / on mobile. - Current variant is read from the .product-variant-id form input, not the pub/sub event (which was stale) — this is what makes variant switching work. - A MutationObserver re-mounts the overlay + re-applies the base swap when Dawn re-renders the gallery (synchronous; requestAnimationFrame was paused headless). - The config JSON must quote variant-id object keys ("5671...": {}), else JSON.parse throws and everything silently falls back to defaults. - box % is relative to the cover image, authored in the box-tool by loading that exact image (the covers are square 1200×1200; the print PDF is portrait — boxPt is the separate print mapping).

Print pipeline (Phase 2)

Shopify order ─▶ order_to_md.py ─▶ 1 MD per item ─▶ stamp.py ─▶ print PDF
                                   (front-matter +   (per-variant base PDF
                                    ```json descriptor)  from Illustrator)

Self-check: python order_to_md.py --order sample_order.json --out-dir out --covers covers.json then python stamp.py --descriptor out/*.md --out out/final.pdf --verify → dx/dy≈0. stamp.py now passes lineheight= (the descriptor's leading) on BOTH the fit pass and the final stamp, so print spacing matches the browser instead of the font default.

🔴 OPEN: how print gets its geometry — Stelios will define this

Do not "fix" this by auto-matching the mockup to the PDF — he said so explicitly: "Θα προτείνω να μην προσπαθήσεις να ταυτίσεις το Mockup με το Pdf. Δεν είναι το ζητούμενο. Θα τα φέρω εγώ με το μάτι… Στο Print θα κινηθούμε λίγο διαφορετικά." He will explain the print approach; wait for it before building anything here.

Facts established (2026-07-16), so the next session doesn't re-derive them: - The storefront base images are square (1200×1200 / 640×640) and show the book inside a grey scene — the cover does not fill the image. Measured on CUSTOM_COVER_NEUTRAL_SALMON.jpg: the cover sits at x=18.33% y=4.50% w=63.17% h=90.67% of the image (aspect 0.697 ≈ A5's 0.706). - Therefore box (% of the image) ≠ % of the cover, and the box-tool's old boxPt = box% × artworkPt was a naive mapping (it assumed the cover filled the image). The artworkPt: 420×595 in the current metafield was just the tool's old default — it was never meaningful. - The real print file wedding.pdf (4 pages, sizes differ!): page 1 = 992.1×708.7 pt = 350×250 mm, a full wrap — back cover (left) + spine (middle) + front cover (right) + binding bleed. Pages 2–4 are blank. The personalisation goes on the front (right) portion. - The box-tool can now load that PDF directly and reads the true page size, so artworkPt/boxPt are real artwork coordinates when you place the box on the PDF.

Setting the text up in InDesign (the size/leading correspondence)

Given a text frame of height H (pt) on the artwork, for a given order: - font size (pt) = H × sizePct / 100 (sizePct from that order's _preview) - leading (pt) = font size × leading → set it explicitly (e.g. 115%), never "Auto" — InDesign's auto is 120% and would not match. - Vertical Justification: Center (the browser centres the block vertically); horizontal alignment = the order's font.align. - Note ~25% of each line is the typeface's own ascender/descender room (measured on Cavafy) — that empty space is the font, not a bug; tighten it with leading.

Cart & order preview (customer + merchant, no hosting)

The personalised cover is never rasterised or stored — it is composed ad-hoc (base cover <img> + text overlay) from the descriptor everywhere it appears:

• Customer, in cart/drawer/notification: snippets/live-preview-cart.liquid draws the base cover natively (from _preview_base); assets/live-preview-cart.js (loaded globally) overlays the text from _preview, with a shrink-to-fit guard so a smaller render never clips. Click a preview to zoom (modal). wire() re-fits via ResizeObserver + setTimeout fallbacks once the element is laid out.
• Merchant, from the order admin: click the order's _preview_link (/pages/cover-preview?view=cover-preview&d=<base64 descriptor>) → templates/page.cover-preview.liquid re-renders the exact zoomable cover on the shop's own domain. One cover-preview Page total (reused per order via the ?d= param). No third-party, no storage.
• Hide cash-on-delivery for custom items: loop cart.items and check item.properties['_custom_product'] == 'Yes'.

Debugging

Block setting "Show debug info" renders two readouts under the field: - green (Liquid): position source, box %, per-variant metafield scan, built map. - blue (JS): parsed config, variantId, variant found?, applied colour, hero src. Turn it OFF for production.

Pending / next ideas

Immediate (blocking a real end-to-end test): 1. 🔴 Stelios has not yet tested the Live Preview on the live theme. He said he would and report back. Two admin steps must happen first (see Branch & deploy): the cover-preview Page must exist, and the "Live preview (in-house)" block must be added to the product in the LIVE theme editor. 2. 🔴 Re-author custom.live_preview with the new box-tool so every font gets its own sizePct/leading (the current metafield predates this and falls back). The tool now: image from the PC, PDF loading, font dropdown, per-font tuning. 3. 🔴 Wait for Stelios's print approach before touching print geometry (see the OPEN section under Print pipeline). Do not auto-match the mockup to the PDF.

Then: - Turn off the debug toggle + broaden to the other product families (invitations, notebooks…). Each new product = box-tool → paste JSON + variant metafields. - Wire order_to_md.py to the real Admin API export (batch production). - Optional: a true cart attribute _custom_product if a checkout-level COD-hide needs cart.attributes (today it's a line item property). - Optional: hosted static image in the order — deliberately avoided (the viewer link covers verification with zero infrastructure). - Create the cover-preview Page (one-time) so _preview_link resolves — the template no longer has to be assigned to it by hand. - Optional: a true cart attribute _custom_product (currently a line item property) if a checkout-level COD-hide reads cart.attributes. - Optional: hosted static image in the order (Cloudinary / app-proxy → Shopify Files) if a non-clickable inline image in the admin is ever needed. Deliberately avoided. - Position/scale MODES (#3 from the Cloudlift parity list) if requested. - Optional: merge to main once QA'd across products.

How to verify a change

Storefront JS is verifiable via the static harnesses in the session scratchpad (they mock the Dawn gallery DOM + PUB_SUB). For Liquid/metafields you need shopify theme push to the preview theme + the debug toggle. Print scripts run locally against the sample fixtures.

Live Preview — Οδηγός χρήσης

Live Preview — Οδηγός Χρήσης (στήσιμο προϊόντων)

Πρακτικός οδηγός για να στήσεις την προσωποποίηση (Live Preview) σε ένα προϊόν. Απλά βήματα — δεν χρειάζεσαι προγραμματισμό.

Τι κάνει

Ο πελάτης γράφει ένα κείμενο (π.χ. ονόματα, ημερομηνία) και το βλέπει ζωντανά πάνω στο εξώφυλλο του βιβλίου, στο χρώμα του κάθε variant. Αλλάζοντας χρώμα, το κείμενο «μεταφέρεται» στο αντίστοιχο εξώφυλλο. Ό,τι γράψει καταγράφεται στην παραγγελία, για να το τυπώσεις.

⚙️ Μία φορά συνολικά (setup ορισμών)

Στο Shopify admin → Settings → Custom data, φτιάξε 3 ορισμούς (definitions):

1. Products → Add definition - Name: live preview (γράψε το απλά έτσι — όχι custom.live_preview) - Type: JSON

2. Variants → Add definition - Name: preview base image - Type: File (εικόνα)

3. Variants → Add definition - Name: preview text color - Type: Color

⚠️ Στο Name γράψε απλό όνομα (π.χ. preview base image), χωρίς custom. μπροστά. Αν βάλεις custom. το Shopify διπλασιάζει το κλειδί και μπερδεύεται.

🎨 Στήσιμο ενός προϊόντος

Βήμα 1 — Εξώφυλλα & χρώματα ανά variant

Για κάθε χρώμα (variant) του προϊόντος, στη σελίδα του variant → ενότητα Metafields, συμπλήρωσε: - preview base image → την άδεια εικόνα εξωφύλλου αυτού του χρώματος (χωρίς το τυπωμένο κείμενο· εκεί θα μπει η προσωποποίηση). - preview text color → το χρώμα των γραμμάτων γι' αυτό το εξώφυλλο.

(Η κανονική εικόνα του variant δεν χρειάζεται ρύθμιση — εμφανίζεται όταν το πεδίο είναι άδειο.)

Βήμα 2 — Θέση κειμένου (& γραμματοσειρές)

1. Άνοιξε το αρχείο tools/live-preview/box-tool.html (διπλό κλικ → ανοίγει στον browser).
2. Στο Image URL βάλε την ίδια εικόνα που δείχνει το site ως εξώφυλλο (την τετράγωνη base εικόνα).
3. Σύρε με το ποντίκι το κόκκινο πλαίσιο εκεί που θες το κείμενο. Δες live πώς φαίνεται.
4. (Προαιρετικά) Στο Customer fonts πρόσθεσε γραμματοσειρές — δες Βήμα 4.
5. Πάτα Copy JSON.
6. Στο προϊόν → Metafields → live preview → κόλλα το JSON → Save.

Βήμα 3 — Πρόσθεσε το block στη σελίδα (μία φορά ανά template)

Theme editor → σελίδα προϊόντος → ενότητα Product information → Add block → «Live preview (in-house)» → σύρε το εκεί που θες → Save.

Βήμα 4 — Γραμματοσειρές (προαιρετικό)

1. Μετάτρεψε τις γραμματοσειρές σου σε web μορφή: python tools/live-preview/fonts_to_woff2.py --in "C:/φάκελος/με/fonts" Σου φτιάχνει .woff2 και σου τυπώνει το όνομα (family) κάθε μίας.
2. Ανέβασε τα .woff2 στο Shopify Files → copy το URL της καθεμιάς.
3. Στο box-tool → Customer fonts, μία γραμμή ανά γραμματοσειρά: Ετικέτα | Family | URL π.χ. Χειρόγραφη | Astir | https://cdn.shopify.com/.../Astir.woff2 (Η πρώτη = προεπιλογή. Η «Ετικέτα» είναι ό,τι βλέπει ο πελάτης.)
4. Copy JSON → ξανακόλλα στο metafield live preview.

✍️ Μέγεθος κειμένου, labels & tooltips

Αυτά ρυθμίζονται στο Theme editor → σελίδα προϊόντος → block «Live preview» (είναι ρυθμίσεις του block — Save στον editor αρκεί, δεν χρειάζεται push).

Ενότητα «Size»: - Autofit: text fills the box (προεπιλογή: ναι) — το κείμενο μεγαλώνει αυτόματα ώστε να γεμίζει πάντα το πλαίσιο. Σβήσ' το αν θες σταθερό μέγεθος (τότε παίζει το «Fixed size»). - Show size −/+ buttons — εμφανίζει δύο μικρά κουμπιά (−/+) κάτω από το πεδίο, ώστε ο πελάτης να μεγαλώνει/μικραίνει μόνος του το κείμενο. - Smallest the customer can go (% of the auto-fit) — το δάπεδο, ως ποσοστό του μεγέθους που βρίσκει μόνο του το autofit — όχι του πλαισίου. 40 σημαίνει «ο πελάτης μπορεί να το ρίξει μέχρι το 40% όσου χωράει». ⬆ Το πάνω όριο είναι πάντα το autofit — το «+» σβήνει όταν το κείμενο γεμίζει το πλαίσιο, γιατί πιο μεγάλο δεν χωράει. Αυτό είναι σωστό, όχι βλάβη. - Hard ceiling (%) — φρένο ασφαλείας ως % του ύψους του πλαισίου. Σπάνια παίζει: το πραγματικό ταβάνι είναι το sizePct της κάθε γραμματοσειράς (box-tool). - Step per click (% of the auto-fit) — πόσο αλλάζει σε κάθε πάτημα, στην ίδια μονάδα. 8 ≈ οκτώ πατήματα από το γεμάτο μέχρι το δάπεδο. - Starting size (% of the auto-fit) (προεπιλογή: 85) — πού προσγειώνεται το κείμενο πριν το πειράξει ο πελάτης. Στο 100 κολλάει πάνω στο ταβάνι της γραμματοσειράς: δύο λέξεις σε δύο γραμμές σταματούσαν να μεγαλώνουν μόνο εκεί, έβγαιναν τεράστιες, και το «+» ερχόταν ήδη σβηστό. Στο 85 ξεκινά λίγο πιο κάτω και το «+» ανεβάζει κανονικά μέχρι το γεμάτο.

Άνοιγμα υπάρχοντος προϊόντος στο box-tool (ενότητα 0): Παλιά κάθε αλλαγή απαιτούσε νέο στήσιμο από το μηδέν. Τώρα επικολλάς το custom.live_preview του προϊόντος (ή φορτώνεις το αρχείο του από tools/live-preview/product-configs/) και επανέρχονται όλα: γεωμετρία, ευθυγράμμιση βιτρίνας, γραμματοσειρές και οι ρυθμίσεις της καθεμιάς. ⚠ Το JSON δεν κουβαλά εικόνες — ξαναφόρτωσε τη βιτρίνα (και το PDF αν το χρειάζεσαι). Η ευθυγράμμισή σου ΔΕΝ χάνεται όταν φορτώνεις εικόνα μετά από import (σε καινούριο στήσιμο εξακολουθεί να κουμπώνει μόνη της στα σταθερά σημεία).

Ελάχιστο μέγεθος (min) ανά γραμματοσειρά: Δίπλα στο «Μέγεθος % ύψους (max)» υπάρχει πλέον «Ελάχιστο % ύψους (min)». Είναι το κατώφλι αναγνωσιμότητας: όσο κι αν γράψει ο πελάτης (enter, γραμμές, χαρακτήρες), αν για να χωρέσει πρέπει να πέσει κάτω από αυτό, το κείμενο δεν γίνεται δεκτό. Αφήνοντάς το κενό υπολογίζεται στο 60% του max της ίδιας γραμματοσειράς (το πεδίο το δείχνει ως «auto …»). Τσέκαρε «Δες το στο min» για να δεις με το μάτι πόσο μικρό είναι το μικρότερο που επιτρέπεις· η γραμμή κάτω λέει και πόσες γραμμές αντιστοιχούν σε αυτό.

Καρφωμένη προεπισκόπηση στο κινητό: - Pin a mini preview on mobile while typing (προεπιλογή: ναι) — στο κινητό το εξώφυλλο είναι πολύ πιο πάνω από το πεδίο, οπότε ο πελάτης γράφει στα τυφλά. Με αυτό ανοιχτό, μόλις γράψει καρφώνεται πάνω δεξιά μια μικρή ζωντανή κάρτα με το κείμενό του — εστιασμένη στο πλαίσιο κειμένου, ώστε να διαβάζεται. Σβήνει μόνη της όταν το πραγματικό εξώφυλλο είναι στην οθόνη, όταν σβήσει το κείμενο, και όσο είναι ανοιχτό το καλάθι ή το μενού. Η κάρτα έχει × για να την κλείσει, και με tap σε πάει στο κανονικό εξώφυλλο.

Ετικέτες (labels): «Font dropdown label», «Alignment control label», «Size control label» — το κείμενο που βλέπει ο πελάτης πάνω από κάθε control.

Tooltips (i): «Field / Font / Size / Alignment tooltip» — προαιρετικό βοηθητικό κείμενο· εμφανίζεται ως εικονίδιο (i) δίπλα στο label. Άφησέ το κενό για να μη φαίνεται.

Οι γραμματοσειρές του πελάτη ορίζονται μόνο πλέον από το metafield custom.live_preview (Βήμα 4) — το παλιό πεδίο «Customer font choices» στο block αφαιρέθηκε.

🚀 Δημοσίευση (κάθε φορά που αλλάζεις κώδικα)

shopify theme push --theme 201616654668 --store <handle>.myshopify.com

Αν αλλάζεις μόνο metafields (θέση/εικόνες/χρώματα/fonts), δεν χρειάζεται push — απλά Save + refresh τη σελίδα. Push χρειάζεται μόνο για αλλαγές κώδικα.

🔎 Έλεγχος / Debug

Στο block «Live preview» υπάρχει διακόπτης «Show debug info». Άναψέ τον για να δεις τι διαβάζει η εφαρμογή (θέση, εικόνες/χρώματα ανά variant). Σβήσ' τον όταν τελειώσεις — δεν πρέπει να φαίνεται στους πελάτες.

🛒 Στο καλάθι & στην παραγγελία (τι βλέπει ο πελάτης / εσύ)

Ο πελάτης, μόλις προσωποποιήσει και πατήσει «Προσθήκη στο καλάθι», βλέπει το εξατομικευμένο εξώφυλλο (όχι το άδειο) στο καλάθι, στο συρτάρι (drawer) και στο popup «προστέθηκε». Κλικ πάνω στη μικρογραφία → μεγέθυνση (zoom). Δεν χρειάζεται καμία ρύθμιση — δουλεύει αυτόματα.

Εσύ, στην παραγγελία (Shopify admin), βλέπεις: - Κείμενο — αυτό που έγραψε ο πελάτης. - Ένα σύνδεσμο _preview_link — κάν' τον κλικ και ανοίγει το ακριβές εξώφυλλο με ζουμ, για να επιβεβαιώσεις (π.χ. τόνους) πριν το τυπώσεις. - _custom_product: Yes — σημάδι ότι είναι εξατομικευμένο (χρήσιμο για να κρύβεις την αντικαταβολή σε custom προϊόντα).

⚙️ Setup μία φορά — η σελίδα προεπισκόπησης

Για να δουλέψει ο σύνδεσμος _preview_link, φτιάξε μία σελίδα (μία φορά για όλο το site — όχι ανά παραγγελία): 1. Shopify admin → Online Store → Pages → Add page. 2. Title: Cover preview (το handle θα γίνει cover-preview). Άφησέ τη κενή. 3. Δεξιά, Theme template → cover-preview → Save. (Προαιρετικό: ο σύνδεσμος διαλέγει μόνος του το σωστό template. Αν το βάλεις, γλιτώνεις μία ανακατεύθυνση.) 4. Μην τη βάλεις σε μενού — ανοίγει μόνο μέσω του συνδέσμου της παραγγελίας.

Μία σελίδα εξυπηρετεί όλες τις παραγγελίες (κουβαλά τα δεδομένα στο ίδιο το link). 300 ή 3000 παραγγελίες → πάντα μία σελίδα, μηδέν αποθηκευμένες εικόνες.

🟡 Το κίτρινο πλαίσιο «Προσοχή πριν την εκτύπωση»

Μερικές φορές, πάνω από το εξώφυλλο, θα δεις ένα κίτρινο πλαίσιο. Δεν σημαίνει ότι κάτι χάλασε — σημαίνει «κοίτα αυτό το ένα πράγμα πριν στείλεις το βιβλίο στο τυπογραφείο». Λέει ένα από τα εξής:

• «Η καταγραφή είχε κόψει λέξη στη μέση… Ξανακολλήθηκε.» Ο περιηγητής του πελάτη είχε καταγράψει ένα κόψιμο μέσα σε λέξη (π.χ. Our w / edding) σε κουτί όπου η λέξη χωρούσε ολόκληρη. Το σύστημα ξανακολλάει τη λέξη και μικραίνει λίγο το μέγεθος ώστε να χωρέσει. Αυτό που βλέπεις στη σελίδα είναι ακριβώς αυτό που θα τυπωθεί — απλώς κοίτα το μια φορά.
• «Ο πελάτης έβλεπε ΑΛΛΟ εξώφυλλο.» Η φωτογραφία που καταγράφηκε δεν είναι αυτή που είχε μπροστά του. Συνήθως σημαίνει ότι το εξατομικευμένο εξώφυλλο του variant λείπει. Μην τυπώσεις — έλεγξε το προϊόν στο box-tool.
• «Η γραμματοσειρά που ζητήθηκε δεν είναι αυτή που σχεδιάστηκε.» Δεν πρόλαβε να φορτώσει η σωστή γραμματοσειρά στην οθόνη του πελάτη. Η εκτύπωση θα βγει σωστή (χρησιμοποιεί το αρχείο της γραμματοσειράς απευθείας), αλλά ο πελάτης είδε κάτι λίγο διαφορετικό.

Χωρίς κίτρινο πλαίσιο δεν χρειάζεται να κάνεις τίποτα.

Το ίδιο φαίνεται και στην ουρά εκτύπωσης: αν το print_queue.bat γράψει «οι αποθηκευμένες γραμμές ήταν λάθος — έγινε νέο wrap», άνοιξε τον σύνδεσμο της παραγγελίας και δες το εξώφυλλο πριν το στείλεις.

📱 Η μικρή προεπισκόπηση στο κινητό

Όταν ο πελάτης γράφει και το εξώφυλλο έχει φύγει πάνω από την οθόνη, μια μικρή κάρτα μένει καρφιτσωμένη πάνω δεξιά. Το μέγεθος του κειμένου μέσα της δεν είναι σταθερό ανά προϊόν: εξαρτάται από το σχήμα του πλαισίου που έχεις ορίσει στο box-tool. Πλακουτσωτό πλαίσιο (π.χ. «Πες μου») έδινε πολύ μικρά γράμματα.

Τώρα υπάρχει κατώφλι: αν τα γράμματα βγαίνουν κάτω από το αναγνώσιμο, η κάρτα ζουμάρει πιο κοντά· κι αν το πλαίσιο είναι τόσο πλακουτσωτό που ούτε αυτό φτάνει, η κάρτα πλαταίνει (μένει χαμηλή, οπότε δεν καταλαμβάνει την οθόνη). Προϊόντα που ήδη διαβάζονταν καλά δεν μικραίνουν ποτέ.

Πρακτικά: αν φτιάξεις νέο προϊόν με πολύ χαμηλό πλαίσιο, η μικρή προεπισκόπηση θα το διορθώσει μόνη της. Αν πάλι θες μεγαλύτερα γράμματα παντού, το μέγεθος της κάρτας είναι στο snippets/live-preview.liquid (.lp-mini, width: 44vw).

🚨 Πώς μαθαίνουμε ότι κάτι έσπασε

Το Live Preview παρακολουθείται μόνο του. Το assets/live-preview-health.js τρέχει στη σελίδα προϊόντος και ελέγχει ότι όλα δούλεψαν — δεν περιμένει μήνυμα λάθους, γιατί οι σοβαρότερες βλάβες δεν βγάζουν κανένα.

Ελέγχει: μπήκε το πλαίσιο κειμένου στη σελίδα και έχει ύψος; φόρτωσε το εξώφυλλο; είναι το σωστό εξώφυλλο πίσω από το κείμενο, ή έμεινε η γενική φωτογραφία του προϊόντος; γράφτηκαν τα δεδομένα που πάνε στην παραγγελία; ζωγραφίστηκε το κείμενο σε σωστό μέγεθος; φόρτωσε η πραγματική γραμματοσειρά ή πέσαμε σε εφεδρική; Ο έλεγχος γίνεται στα 2.5 και 8 δευτερόλεπτα αφότου αρχίσει να γράφει ο πελάτης, και ξανά τη στιγμή του «Προσθήκη στο καλάθι» — εκεί μετράει περισσότερο, γιατί χαλασμένα δεδομένα εκείνη τη στιγμή γίνονται παραγγελία που δεν τυπώνεται.

Τι βλέπει ο πελάτης όταν σπάσει: ένα ήρεμο μήνυμα στη θέση της προεπισκόπησης («Η προεπισκόπηση του εξωφύλλου δεν φόρτωσε σωστά. Ανανεώστε τη σελίδα και δοκιμάστε ξανά — αν συνεχίσει, επικοινωνήστε μαζί μας.») και το «Προσθήκη στο καλάθι» παγώνει όσο διαρκεί. Αυτό είναι σκόπιμο: πελάτης που δεν βλέπει το εξώφυλλό του είναι ένα κλικ μακριά από παραγγελία που δεν μπορούμε να τυπώσουμε, και η επιστροφή χρημάτων κοστίζει πιο ακριβά από τη χαμένη πώληση. Μόλις ο επόμενος έλεγχος βγει καθαρός, το μήνυμα φεύγει και το κουμπί ξαναδουλεύει. Το κείμενο αλλάζει από το theme editor (Message shown when the preview breaks) χωρίς push.

Δεν παγώνουν όλα. Αν απλώς δεν πρόλαβε να φορτώσει η γραμματοσειρά, ο πελάτης αγοράζει κανονικά — η παραγγελία βγαίνει σωστή στο τύπωμα, αλλάζει μόνο το πώς φαίνεται στην οθόνη, και δεν αξίζει να χαθεί πώληση γι' αυτό.

Πού τα βλέπεις: - Clarity — φιλτράρεις τα sessions με tag lp_fault ή lp_error και βλέπεις το recording του πελάτη που το έπαθε. - GA4 — events lp_engaged → lp_submitted (πούλησε) ή lp_abandoned (ασχολήθηκε και έφυγε). Η σχέση των δύο είναι το ποσοστό εγκατάλειψης.

Τι ΔΕΝ στέλνεται ποτέ: το κείμενο του πελάτη. Είναι ονόματα. Φεύγει μόνο το μήκος και ο αριθμός γραμμών.

Προσοχή: Clarity και GA4 περνούν από τη συγκατάθεση cookies (Consentmo). Όποιος αρνήθηκε analytics δεν στέλνει τίποτα εκεί — άρα τα νούμερα της συμπεριφοράς είναι κατώτατο όριο, όχι ακριβής μέτρηση.

Να μας βρίσκει η βλάβη, αντί να την ψάχνουμε (setup μία φορά)

Τα δύο παραπάνω περιμένουν να τα κοιτάξει κάποιος. Για να έρχεται η βλάβη σε εμάς μόνη της, φτιάχνουμε ένα Google Form που γράφει σε ένα Sheet. Δεν χρειάζεται διακομιστής, ούτε νέος λογαριασμός, ούτε συνδρομή.

1. Google Forms → κενή φόρμα. Τίτλος π.χ. «Moodbook — Live Preview βλάβες».
2. Μία ερώτηση, τύπου Παράγραφος (μεγάλη απάντηση). Ονόμασέ την payload. Μην τη βάλεις υποχρεωτική.
3. Πάνω δεξιά ⋮ → Λήψη προσυμπληρωμένου συνδέσμου. Γράψε ό,τι να 'ναι στο πεδίο, πάτα Λήψη συνδέσμου και Αντιγραφή.
4. Shopify → theme editor → σελίδα προϊόντος → block Live preview → επικόλλησε τον σύνδεσμο στο Health beacon URL → Save. Τέλος.
5. Στη φόρμα, καρτέλα Απαντήσεις → σύνδεση με Υπολογιστικά φύλλα. Εκεί προσγειώνονται οι βλάβες, μία γραμμή η καθεμία.

⚠ Πρέπει να είναι ο προσυμπληρωμένος σύνδεσμος, όχι αυτός του κουμπιού «Αποστολή/Κοινή χρήση». Ξεχωρίζουν με το μάτι: ο σωστός τελειώνει σε …&entry.1234567890=κάτι, ο λάθος σε …?usp=publish-editor ή ?usp=sf_link. Ο λάθος δεν έχει μέσα του το πεδίο, οπότε δεν θα σταλεί ποτέ τίποτα — και μια σιωπηλή αναφορά μοιάζει ακριβώς με κατάστημα χωρίς βλάβες. Αν το μπερδέψεις, ο κώδικας γράφει προειδοποίηση στην κονσόλα του browser αντί να σωπάσει.

Γιατί ο προσυμπληρωμένος σύνδεσμος: κουβαλάει μέσα του το αναγνωριστικό του πεδίου, οπότε δεν χρειάζεται να ψάξεις τίποτα — ο κώδικας το διαβάζει από το URL.

Τι φτάνει στο Sheet: ώρα, κατάστημα, σελίδα, ο κωδικός της βλάβης, το προϊόν, το variant, η γραμματοσειρά. Μόνο βλάβες — η συμπεριφορά (ποιος ασχολήθηκε και έφυγε) μένει στο GA4, όπου τη διέπει η συγκατάθεση. Καμία προσωπική πληροφορία, κανένα αναγνωριστικό, ποτέ το κείμενο του πελάτη.

✅ Ο συναγερμός δουλεύει από άκρη σε άκρη

Ελεγμένο 21/08/2026, μετά την εγκατάσταση του trigger: μια βλάβη έγινε issue στο GitHub σε 3 δευτερόλεπτα, και η δεύτερη φορά της ίδιας βλάβης μπήκε ως σχόλιο στο ίδιο issue — άρα ο αριθμός σχολίων σου λέει πόσοι πελάτες το χτύπησαν.

Τι να κοιτάς: ένα ανοιχτό issue σημαίνει ότι κάποιος πελάτης βλέπει ΤΩΡΑ μήνυμα βλάβης και δεν μπορεί να αγοράσει. Κλείσ' το όταν φτιαχτεί.

⚠️ Οι βλάβες που καταγράφηκαν πριν μπει ο διακόπτης δεν θα γίνουν ποτέ issue — ο μηχανισμός πιάνει μόνο νέες. Ήταν τρεις, ελέγχθηκαν με το χέρι και δεν έμεινε τίποτα ανοιχτό.

💡 Μια δημοσίευση αλλαγών μπορεί να προκαλέσει μόνη της μια βλάβη: το κατάστημα είναι ζωντανό όσο ανεβαίνουν τα αρχεία, ένα προς ένα. Αν δεις βλάβη λίγα λεπτά μετά από δημοσίευση, πρώτα δοκίμασε το προϊόν μόνος σου — συνήθως έχει ήδη περάσει.

Και να μας ειδοποιεί μόνο του (βήμα 6)

Το Sheet είναι καλό αρχείο και κακός συναγερμός: κάποιος πρέπει να θυμηθεί να το ανοίξει. Το tools/live-preview/fault-to-github.gs το λύνει — κολλάς το script στο Sheet και κάθε βλάβη γίνεται issue στο GitHub, στο ίδιο repository όπου θα γίνει και η διόρθωση.

Τρία πράγματα κερδίζεις: το GitHub στέλνει email μόνο του σε κάθε νέο issue, οπότε ο συναγερμός δεν εξαρτάται πια από κανένα προγραμματισμένο check· ίδια βλάβη στο ίδιο προϊόν γίνεται σχόλιο αντί για νέο issue, οπότε ο αριθμός σχολίων δείχνει πόσοι πελάτες το έπαθαν· και το issue κλείνει όταν φύγει η διόρθωση, άρα υπάρχει ιστορικό.

Οι οδηγίες εγκατάστασης είναι μέσα στο ίδιο το αρχείο, στην αρχή. Το μόνο που θέλει προσοχή: fine-grained token, μόνο για αυτό το repository, μόνο Issues → Read and write, και μπαίνει στα Script properties, ποτέ μέσα στον κώδικα. Έτσι, ακόμη κι αν διέρρεε, το χειρότερο που θα μπορούσε να γίνει είναι θόρυβος σε έναν κατάλογο issues.

🛡️ Τι βλέπει ο πελάτης όταν κάτι πάει στραβά

Ποτέ πια κλειδωμένο κουμπί. Μέχρι τις 21/08/2026, οποιοδήποτε πρόβλημα στην προεπισκόπηση έκλεινε το «Προσθήκη στο καλάθι». Αυτό σταμάτησε: πελάτης που τον σταματάς τη στιγμή που πληρώνει, δεν ξαναγυρνά.

Αντ' αυτού, αν το πρόβλημα επιμείνει (το σύστημα πρώτα προσπαθεί μόνο του να το φτιάξει, και δεν λέει τίποτα στα πρώτα δευτερόλεπτα), βλέπει ένα ήρεμο μήνυμα:

Η προεπισκόπηση δεν εμφανίζεται σωστά αυτή τη στιγμή. Το κείμενό σας καταγράφεται κανονικά και μπορείτε να συνεχίσετε — το εξώφυλλο το ελέγχουμε εμείς πριν τυπωθεί.

Και μπορεί να αγοράσει κανονικά.

Γιατί είναι ασφαλές: το κείμενο που γράφει ο πελάτης είναι απλό πεδίο φόρμας. Φτάνει στην παραγγελία ακόμα κι αν πέσει όλο το σύστημα προεπισκόπησης. Και η γεωμετρία, η γραμματοσειρά και το artwork είναι δικά μας. Δηλαδή δεν υπάρχει παραγγελία που «δεν τυπώνεται» — υπάρχει μόνο παραγγελία που θέλει ξαναφτιάξιμο, και αυτό γίνεται αυτόματα.

Στην ουρά εκτύπωσης: «ΑΝΑΚΑΤΑΣΚΕΥΑΣΜΕΝΑ»

Αν το print_queue.bat γράψει «ΧΩΡΙΣ καταγραφή από τον browser — ανακατασκευάστηκε», σημαίνει: ο πελάτης έγραψε κείμενο, ο browser του δεν κατέγραψε τίποτα, και το φτιάξαμε από τις ρυθμίσεις του προϊόντος.

Τι να κάνεις: άνοιξε αυτό το ένα PDF και κοίτα το. Το κείμενο είναι σωστό· η γραμματοσειρά και το μέγεθος είναι η προεπιλογή του προϊόντος, όχι απαραίτητα ό,τι διάλεξε ο πελάτης. Αν φαίνεται καλό, τύπωσέ το. Αυτά τα εξώφυλλα δεν περνούν από τον αυτόματο έλεγχο (δεν υπάρχει οθόνη πελάτη για να συγκριθούν), γι' αυτό τα βγάζει ξεχωριστά στη λίστα.

✅ Πριν τυπώσεις: check_orders.bat

Διπλό κλικ στο tools/live-preview/check_orders.bat. Τρέχει σε δευτερόλεπτα, δεν γράφει τίποτα πουθενά, και απαντά σε ένα μόνο ερώτημα για κάθε παραγγελία:

Οι γραμμές που κατέγραψε ο browser περιγράφουν διάταξη που μπορούσε να υπάρξει στην οθόνη του πελάτη;

Τέσσερις απαντήσεις:

Μόνο το ΠΡΟΣΟΧΗ σημαίνει «μην τυπώσεις». Το ΧΩΡΙΣ δεν σημαίνει ότι το εξώφυλλο έχει λάθος — σημαίνει ότι κανείς δεν κατέγραψε τι είδε ο πελάτης, οπότε αξίζει μια ματιά. Το ΟΡΙΑΚΟ υπάρχει επειδή ο έλεγχος δεν πρέπει να σου λέει με βεβαιότητα κάτι που δεν ξέρει με βεβαιότητα: στις 25/08 είχε χαρακτηρίσει μια παραγγελία χαλασμένη με διαφορά μισού τοις εκατό, και η ίδια μέτρηση με άλλη βιβλιοθήκη έβγαζε το αντίθετο.

Γιατί υπάρχει

Η παραγγελία #7685 κατέγραψε «ο» μόνο του σε γραμμή, σε κείμενο όπου η πελάτισσα είχε γράψει «ο γάμος μας» μαζί. Στο μέγεθος που κατέγραψε η ίδια η παραγγελία, η φράση χωρούσε με τον μισό χώρο περίσσιο — δηλαδή εκείνο το σπάσιμο ήταν αδύνατο να έχει συμβεί. Ο υπάρχων φύλακας δεν το πιάνει: διορθώνει μόνο σπασίματα που κόβουν λέξη στη μέση, και αυτό έπεφτε σε κενό.

Τέσσερις από τις εννέα καταγραφές από τον Ιούνιο ήταν τέτοιες. Ο έλεγχος βγαίνει από την ίδια την παραγγελία, χωρίς φωτογραφία και χωρίς browser.

Τι δεν κάνει

Δεν είναι ο τελικός έλεγχος. Είναι γέφυρα μέχρι να συγκρίνουμε την αποθηκευμένη φωτογραφία με το τυπογραφικό. Πιάνει σπασίματα που ήταν αδύνατα — δεν πιάνει λάθος μέγεθος, λάθος θέση ή λάθος γραμματοσειρά.

🖨️ Παραγωγή (από παραγγελία σε PDF)

Διπλό κλικ στο tools/live-preview/print_queue.bat. Αυτό είναι όλο — δεν γράφεις καμία εντολή. Το πρόγραμμα μαζεύει τις παραγγελίες που δεν έχουν ακόμα αποσταλεί και φτιάχνει έναν φάκελο με ημερομηνία μέσα στο print_runs\ (π.χ. print_runs\20260824_1408\). Μέσα θα βρεις:

• ALL_COVERS.pdf — αυτό τυπώνεις. Όλα τα εγκεκριμένα εξώφυλλα μαζί, με τη σειρά της λίστας και επαναλαμβανόμενα ανά ποσότητα (2 τεμάχια = 2 σελίδες).
• MANIFEST.md — η λίστα ελέγχου: μία γραμμή ανά τεμάχιο παραγγελίας, με αριθμό, προϊόν, χρώμα, κείμενο, γραμματοσειρά και το αποτέλεσμα του αυτόματου ελέγχου.
• Ένα PDF ανά τεμάχιο (και μια μακέτα PNG για τα προσωποποιημένα), αν θες να κοιτάξεις κάτι μεμονωμένα.

Κάθε γραμμή της παραγγελίας παίρνει αυτό που της αναλογεί — δεν χρειάζεται να ανοίξεις Illustrator ούτε για τα απλά εξώφυλλα:

Τι σημαίνουν τα σημάδια στη λίστα

✅ — το εξώφυλλο πέρασε τον αυτόματο έλεγχο: αυτό που θα τυπωθεί ταιριάζει με αυτό που είδε ο πελάτης στην οθόνη του. Τύπωσέ το άφοβα.

⚠ ΧΩΡΙΣ ΠΛΗΡΗ ΕΛΕΓΧΟ — το εξώφυλλο τυπώνεται κανονικά και μπαίνει στο ALL_COVERS.pdf, αλλά κανένας έλεγχος δεν μπόρεσε να το συγκρίνει με την εικόνα που είδε ο πελάτης. Συνήθως γιατί η παραγγελία είναι παλιά (πριν τις 24/08 δεν κρατούσαμε φωτογραφία της οθόνης), ή γιατί εκείνη τη στιγμή δεν υπήρχε ίντερνετ.

Δεν σημαίνει ότι έχει λάθος. Σημαίνει ότι δεν το ξέρουμε. Αν είναι μία-δυο παραγγελίες, ρίξε μια ματιά στο preview link της παραγγελίας πριν τυπώσεις. Αν είναι όλες, κάτι έχει σπάσει στο σύστημα — πες μας.

Στο τέλος κάθε τρεξίματος βγαίνει μια γραμμή που λέει πόσα συγκρίθηκαν με τη φωτογραφία του πελάτη και πόσα όχι. Διάβασε αυτόν τον αριθμό, όχι μόνο τα πράσινα σημαδάκια: αν σήμερα ελέγχθηκαν 2 από 10 ενώ χθες ελέγχονταν 9 από 10, το πρόβλημα είναι ο έλεγχος, όχι τα εξώφυλλα.

❌ ΜΠΛΟΚΑΡΙΣΜΕΝΟ / ΔΕΝ ΤΑΙΡΙΑΖΕΙ — ο αυτόματος έλεγχος σταμάτησε ένα εξώφυλλο που δεν ταιριάζει με ό,τι είδε ο πελάτης (π.χ. το κείμενο σπάει σε γραμμές αλλιώς). Το αρχείο του βγαίνει με όνομα .BLOCKED.pdf, δεν μπαίνει στο ALL_COVERS.pdf και δεν το τυπώνεις — θέλει ανθρώπινο μάτι: το κοιτάμε, το διορθώνουμε, και στο MANIFEST προστίθεται ενότητα «Διορθώθηκε χειροκίνητα» με ένα .FIXED.pdf για εκτύπωση (έτσι έγινε με το #7675). Μία λεπτομέρεια: η διόρθωση δεν γράφεται πίσω στη Shopify, οπότε σε επόμενο τρέξιμο η ίδια παραγγελία θα ξαναβγεί «ΜΠΛΟΚΑΡΙΣΜΕΝΟ» — αν την έχεις ήδη τυπώσει από το .FIXED.pdf, αγνόησέ την.

✋ ΠΕΡΙΜΕΝΕΙ ΑΠΟΦΑΣΗ — τόνοι — ο πελάτης έγραψε το όνομα χωρίς τόνο («Νικος» αντί «Νίκος»). Είναι από τα πιο συχνά που συμβαίνουν και δεν είναι λάθος του συστήματος: αυτό ακριβώς πληκτρολόγησε.

Το εξώφυλλο φτιάχνεται κανονικά και κάθεται στον φάκελο ως .HOLD.pdf — απλώς δεν μπαίνει στο ALL_COVERS.pdf μέχρι να πεις εσύ τι γίνεται. Άνοιξέ το, δες πώς δείχνει το όνομα στο εξώφυλλο, και διάλεξε:

Αν θες να το κάνεις μόνος σου, από τον φάκελο tools\live-preview:

python text_fixes.py "#7721" --word "Στελιος=Στέλιος" --word "Ειρηνη=Ειρήνη"
python text_fixes.py "#7670" --as-typed

Η απόφαση μένει καταγεγραμμένη: την επόμενη φορά που θα τρέξεις την ουρά, η ίδια παραγγελία περνάει κατευθείαν. Δεν θα σε ξαναρωτήσει.

Τρία πράγματα που αξίζει να ξέρεις:

• Το σύστημα δεν προτείνει ποτέ ορθογραφία. Στο εξώφυλλο μπαίνει το όνομα ενός ανθρώπου· κανένας κανόνας δεν ξέρει αν ο πελάτης εννοούσε «Μαρία» ή «Μαριά». Σου δείχνει τη λέξη, αποφασίζεις εσύ.
• Η διόρθωση αλλάζει μόνο ΛΕΞΕΙΣ, στη θέση τους. Δεν ξαναμοιράζει το κείμενο σε γραμμές — άρα το εξώφυλλο βγαίνει ακριβώς όπως το είδε ο πελάτης, με τον τόνο πάνω από το γράμμα. Ο έλεγχος με τη φωτογραφία συνεχίζει να τρέχει και μετά τη διόρθωση, και το εξώφυλλο βγαίνει ✅ με σημείωση τι διορθώθηκε.
• Μισή διόρθωση δεν περνάει. Αν διορθώσεις το «Νικος» και ξεχάσεις το «Δημητρα», η παραγγελία μένει σε αναμονή. Ένα εξώφυλλο που γράφει «Νίκος & Δημητρα» είναι χειρότερο από ένα που γράφει «Νικος & Δημητρα», γιατί το πρώτο μοιάζει αποφασισμένο.

Τα ίδια λόγια βγαίνουν και στο check_orders.bat, πριν καν φτιαχτεί εξώφυλλο — εκεί τα βλέπεις όλα μαζί σε μια λίστα.

Τι πιάνει και τι όχι: πιάνει κάθε ελληνική λέξη δύο ή περισσότερων συλλαβών χωρίς τόνο, που στα νέα ελληνικά είναι πάντα λάθος. Δεν ενοχλεί για μονοσύλλαβα («μια», «για», «και»), για ΚΕΦΑΛΑΙΑ (η ελληνική τα γράφει άτονα έτσι κι αλλιώς), για λατινικά, αριθμούς ή emoji. Στην ουρά της 31/08 σήμανε 5 στις 39 γραμμές, και οι 5 ήταν πραγματικές — μαζί με ένα σκέτο ορθογραφικό («λσλυτερη»).

Τα emoji τυπώνονται κανονικά. Αν ο πελάτης γράψει 🤍 ή ❤ ή ★, μπαίνει έγχρωμο στο εξώφυλλο, όπως ακριβώς το είδε στην οθόνη του — και το preview PNG στον φάκελο το δείχνει κι αυτό. Καμία από τις γραμματοσειρές μας δεν έχει emoji, οπότε το σύστημα κάνει το ίδιο που κάνει ο browser: για εκείνον τον έναν χαρακτήρα γυρίζει στη γραμματοσειρά emoji των Windows. Μπαίνει στα 576 dpi, που είναι πάνω από ό,τι χρειάζεται το ψηφιακό μηχάνημα.

Αν κάποια στιγμή γράψει χαρακτήρα που καμία γραμματοσειρά δεν έχει (σπάνιο — π.χ. ιερογλυφικό), η παραγγελία βγαίνει ΠΡΟΣΟΧΗ: εκεί θα τυπωνόταν όντως κενό.

⚠ Cloudlift — χειροκίνητα — παραγγελίες με προσωποποίηση από την προηγούμενη εφαρμογή (πριν το δικό μας σύστημα). Το σύστημα δεν μπορεί να ξαναφτιάξει το εξώφυλλό τους, γι' αυτό τις βγάζει σε ξεχωριστή λίστα για να τις φτιάξεις όπως παλιά. Ποτέ μην τους δώσεις κλασικό εξώφυλλο — θα χανόταν το κείμενο του πελάτη χωρίς να το πάρει είδηση κανείς.

(Για τα «ΑΝΑΚΑΤΑΣΚΕΥΑΣΜΕΝΑ» δες την προηγούμενη ενότητα.)

Ποιες παραγγελίες μπαίνουν στην ουρά

Η ουρά παίρνει μόνο ανεκτέλεστες, ενεργές, πληρωμένες. Ακυρωμένες και αρχειοθετημένες δεν φτάνουν καν στα εξώφυλλα — τις κόβει η Shopify πριν κατέβει τίποτα. (Στις 31/08 τρεις ακυρωμένες τυπώνονταν κανονικά επί μέρες: οι

7669, #7670, #7688. Δεν ξαναγίνεται.)

Οι εκκρεμείς πληρωμές δεν χάνονται — μπαίνουν σε λίστα. Το IRIS και η κατάθεση σε τράπεζα μένουν «PENDING» μέχρι να μπουν τα λεφτά, που μπορεί να είναι την επόμενη μέρα. Αυτές δεν τυπώνονται αυτόματα, αλλά γράφονται με το όνομά τους στην αρχή του τρεξίματος:

⏸ ΔΕΝ ΜΠΗΚΑΝ — δεν έχει μπει ακόμα η πληρωμή (IRIS/κατάθεση):
     #7716  PENDING

Αν ξέρεις ότι το ποσό ήρθε και θες να τις τυπώσεις, πες το — τρέχουμε την ουρά με --include-pending. Ποτέ δεν εξαφανίζεται παραγγελία στη σιωπή: μια ουρά που μικραίνει μόνη της είναι χειρότερη από μια που τυπώνει κάτι παραπάνω, γιατί κανείς δεν ψάχνει αυτό που λείπει.

Αν θες να τυπώσεις κάτι που μπλοκαρίστηκε

Το κοιτάς, και αν κρίνεις ότι δεν πειράζει, το λες με τον λόγο:

python text_fixes.py "#7716" --accept-gate "το κοίταξα, είναι εντάξει"

Ο λόγος είναι υποχρεωτικός, και το τρέξιμο μαζί με το MANIFEST γράφουν ολόκληρο το μπλοκάρισμα που παρακάμφθηκε. Δεν σβήνει τον έλεγχο — αφήνει ίχνος, ώστε να το βρει κάποιος που δεν ξέρει ότι έγινε.

Η διόρθωση δεν επιτρέπεται να χαλάσει το στήσιμο

Ο τόνος έχει τους ίδιους χαρακτήρες («Νικος» και «Νίκος» είναι και τα δύο πέντε), γι' αυτό μπαίνει με ασφάλεια μέσα στις γραμμές που κατέγραψε ο browser. Μια διόρθωση που αλλάζει μήκος («Νικος → Νικόλαος») μπορεί να μη χωράει πια στη γραμμή. Το σύστημα μετράει και τα δύο, πριν και μετά, και αν η διάταξη χειροτερέψει μπλοκάρει: «Η ΔΙΟΡΘΩΣΗ ΧΑΛΑΕΙ ΤΗ ΔΙΑΤΑΞΗ». Ένα εξώφυλλο σωστά γραμμένο αλλά κακοστημένο είναι χειρότερο από πριν, γιατί κανείς δεν ξαναδιαβάζει ένα εξώφυλλο που μόλις διόρθωσε.

Το ξανατρέξιμο είναι ασφαλές

Το print_queue.bat μόνο διαβάζει τις παραγγελίες: δεν γράφει τίποτα στη Shopify και δεν κλείνει καμία παραγγελία ως απεσταλμένη. Κάθε τρέξιμο φτιάχνει καινούριο φάκελο — τρέξ' το όσες φορές θες, όποτε θες. Κάθε παραγγελία συνεχίζει να εμφανίζεται στην ουρά μέχρι να τη σημειώσεις εσύ ως απεσταλμένη (fulfil) στη Shopify.

(Για τρέξιμο μίας μόνο παραγγελίας ή τεχνικές λεπτομέρειες: tools/live-preview/README.md.)

🆘 Αν κάτι δεν δουλεύει

Live Preview — harness (έλεγχος πριν το deploy)

Live Preview — storefront harness

48 assertions about the one thing this feature keeps getting wrong: whether the line breaks recorded in an order are the line breaks the customer saw.

python build_harness.py

It builds a self-contained copy into a temp folder, serves it, and prints a URL. Open it and the suite runs on load; the page shows 48/48 — all green (or the failures in red) and publishes the same thing at window.__LP_RESULTS__.

Headless, in a sandbox that has Playwright:

page.goto(url)
page.wait_for_function("window.__LP_RESULTS__")
r = page.evaluate("window.__LP_RESULTS__")
assert not r["failures"], r["all"]

What it covers

The TOTAL is part of the result, not decoration

82/82 and 80/80 are both green and they are not the same answer. Two assertions live inside if (shot) and only run once paintOverlay() has actually produced a raster. When it returns null they do not fail — they do not EXIST, the total drops to 80, and the suite reports all-green while the thing it was extended to prove went unchecked.

That is not hypothetical: it is what the missing arrayBuffer in the fetch stub did, and it is why the capture assertions had never passed in any engine until 2026-08-25. Chromium stalled before reaching them; WebKit reached them and was lied to.

So when comparing runs across commits, read the total first. A count that falls is a path that stopped being exercised, and nothing else in the output will say so. Current expected: 82 under WebKit (tools/ios-safari/suite-webkit.mjs, first full pass on the Safari engine, d528c23).

The port is baked in — serve the build on the port you built it for

--build-only writes ABSOLUTE viewer URLs into harness.html (__LP_CASES__, http://127.0.0.1:8791/viewer.html?d=…, from --port, default 8791). Serve that output on any other port and every viewer iframe is cross-origin: the suite dies with a SecurityError reading contentDocument, or simply hangs waiting for a document it will never be allowed to read, and NOTHING says the port is the reason. Both of us lost time to it on 2026-08-24, separately, on the same day.

python build_harness.py --build-only --port 8801 --out DIR   # then serve DIR on 8801

Or just run it without --build-only and let it serve its own build.

Three things about this environment

• It assumes no internet. The Linux sandbox cannot reach moodbook.gr, cdn.shopify.com or docs.google.com, so everything the page needs is served from disk. But check whether YOUR session has network — a desktop session usually does, and then the live PDP beats this harness every time. Three defects on 2026-08-21 were settled only by driving the real product page: the two-line wrap, the clipped descender, and the mini card flickering 165↔203px. Each had survived a synthetic reproduction that measured the wrong thing. Use the harness to lock a fix in place; use the real page to find out whether you have actually understood the bug.
• A harness without @font-face lies. Text measured against a serif fallback wraps somewhere else entirely, so every line-break assertion becomes meaningless. The real Astir face is copied in from print-fonts/ and awaited before anything is measured.
• document.visibilityState is hidden in a headless pane, so requestAnimationFrame and IntersectionObserver never fire. Nothing here depends on them — silence from them is the environment, not the code. This is also why the sticky mini preview decides visibility with getBoundingClientRect on a throttled scroll handler rather than a frame callback.

Why it is built rather than checked in

lp.css is extracted from snippets/live-preview.liquid and viewer.html from templates/page.cover-preview.liquid at build time. A copied fixture drifts away from the theme silently and then passes forever; this one cannot.

Still open

Chromium has never reproduced 7673's break pattern — under either wrapping rule, at any width. WebKit is the standing suspect and has not been available in any session so far. When it is, run this same suite under it: if it produces a mid-word break, the guard should refuse it, and that is the answer either way.

Flipping book — εξώφυλλο, οπισθόφυλλο & σειρά σελίδων

Flipping Book — Προεπισκόπηση σελίδων (με εξώφυλλο & οπισθόφυλλο)

Το flipping book είναι το interactive «ξεφύλλισμα» του βιβλίου στη σελίδα προϊόντος (PDP). Ο επισκέπτης πατάει το κουμπί «ΞΕΦΥΛΛΙΣΕ ΤΟ» και βλέπει τις εσωτερικές σελίδες σαν πραγματικό βιβλίο, με εξώφυλλο και οπισθόφυλλο.

• Πού ζει ο κώδικας: sections/product-flipping-book.liquid (μία μόνο υλοποίηση· τη μοιράζονται όλα τα product templates).
• Πού εμφανίζεται: στο PDP, εφόσον το προϊόν έχει σελίδες. Το κουμπί «ΞΕΦΥΛΛΙΣΕ ΤΟ» ανοίγει το βιβλίο σε fullscreen.

Τι χρειάζεται κάθε προϊόν (metafields)

Όλα είναι product metafields στο namespace custom. Δημιουργούνται μία φορά στο Shopify admin → Settings → Custom data → Products, και μετά συμπληρώνονται ανά προϊόν.

Τα εξώφυλλα είναι ανεξάρτητα και προαιρετικά. Προϊόν χωρίς cover/back cover δουλεύει ακριβώς όπως πριν (ανοίγει κατευθείαν στο πρώτο δισέλιδο και τελειώνει στο τελευταίο).

⚠️ Το πιο σημαντικό: ονομασία αρχείων (σειρά σελίδων)

Το book_pages είναι λίστα από αρχεία. Το Shopify δεν κρατά τα αρχεία με αλφαβητική σειρά — τα κρατά με τη σειρά που τα διάλεξες στον picker (που είναι απρόβλεπτη). Για να μη χρειάζεται χειροκίνητο reorder, το theme ταξινομεί τις σελίδες αυτόματα κατά filename κατά την προβολή.

Γι' αυτό: ονόμασε τα αρχεία με zero-padding πριν τα ανεβάσεις.

• ✅ Σωστά: 001.jpg, 002.jpg, … 010.jpg, … 040.jpg
• ❌ Λάθος: 1.jpg, 2.jpg, … 10.jpg (το 10 θα έρθει πριν το 2)

Με σωστό zero-padding, η σειρά βγαίνει πάντα σωστή ό,τι σειρά κι αν τα διαλέξεις στον picker. Ισχύει αναδρομικά για όλα τα προϊόντα — αν διόρθωσες κάποτε τη σειρά με το χέρι, δεν χρειάζεται πια.

Το εξώφυλλο/οπισθόφυλλο δεν μπαίνουν στο book_pages — έχουν δικά τους metafields, οπότε δεν επηρεάζουν τη σειρά.

Πώς προσθέτεις ένα βιβλίο (βήμα-βήμα)

1. Ονόμασε τις εσωτερικές σελίδες 001, 002, … (zero-padded) και ανέβασέ τις στο custom.book_pages του προϊόντος.
2. Ανέβασε 1 εικόνα εξωφύλλου στο custom.book_cover (προαιρετικό).
3. Ανέβασε 1 εικόνα οπισθόφυλλου στο custom.book_back_cover (προαιρετικό).
4. Άνοιξε το PDP και πάτα «ΞΕΦΥΛΛΙΣΕ ΤΟ» για έλεγχο.

Πώς συμπεριφέρεται

Desktop (δισέλιδο / spread): - Το εξώφυλλο εμφανίζεται μόνο του, κεντραρισμένο. - Πατώντας μπροστά, ανοίγει με flip δεξιά→αριστερά και το πλαίσιο μεγαλώνει από single σε δισέλιδο, δείχνοντας το πρώτο spread. - Ακολουθούν τα εσωτερικά δισέλιδα. - Στο τέλος, το οπισθόφυλλο εμφανίζεται μόνο του, κεντραρισμένο, «κλείνοντας» με flip. - Ο μετρητής δείχνει «Εξώφυλλο» / «Οπισθόφυλλο» στα αντίστοιχα σημεία.

Mobile (fullscreen): κάθετο scroll με μία σελίδα τη φορά — εξώφυλλο πρώτο, εσωτερικές σελίδες, οπισθόφυλλο τελευταίο.

Απόδοση (performance)

• Οι σελίδες φορτώνουν lazy — μόνο ~2 εικόνες φορτώνουν αρχικά, οι υπόλοιπες όταν χρειαστούν. Άρα 50+ σελίδες δεν βαραίνουν το αρχικό load.
• Όλες οι εικόνες σερβίρονται από το Shopify CDN με cap πλάτους ~1200px (&width=1200 / image_url: width: 1200), όχι σε print resolution — τεράστια μείωση bytes χωρίς ορατή διαφορά.

Troubleshooting

Τεχνική σημείωση: το section κάνει render και desktop spread flip και mobile vertical scroll από το ίδιο book_pages. Τα εξώφυλλα διαβάζονται ως single file_reference με image_url (idiom του theme). Η σειρά επιβάλλεται με | sort πάνω στα CDN URLs (το filename ζει μέσα στο URL).

IDML round-trip

IDML ⇄ Markdown — Round-trip επεξεργασία (Moodbook)

Εργαλείο για να επεξεργάζεσαι το κείμενο ενός βιβλίου InDesign με το Claude (ή με το χέρι), διατηρώντας ανέπαφη όλη τη μορφοποίηση: layout, γραμματοσειρές, μεγέθη, στοίχιση, αλλαγές γραμμής, πίνακες, εικόνες, χρώματα, master pages.

Η ιδέα

Το IDML παραμένει η πηγή αλήθειας — δεν το ξαναφτιάχνουμε ποτέ από το Markdown. - Το EXPORT βγάζει σε .md κάθε πεδίο κειμένου (<Content>) του βιβλίου, με ένα σταθερό αναγνωριστικό [story#index]. - Επεξεργάζεσαι μόνο το κείμενο. - Το IMPORT ξαναγράφει στο πρωτότυπο IDML μόνο τα πεδία που όντως άλλαξαν. Όλα τα υπόλοιπα bytes μένουν ίδια → η μορφοποίηση δεν επηρεάζεται καθόλου.

Ροή εργασίας

1. Στο InDesign: File ▸ Export ▸ InDesign Markup (IDML). Βάλε το .idml στο IDML_IN.
2. Διπλό κλικ στο 1_EXPORT_idml_to_md.bat → δημιουργείται MD_WORK\<όνομα>.md.
3. Δώσε αυτό το .md στο Claude για διορθώσεις/προσθήκες (ή επεξεργάσου το μόνος σου). Άλλαξε μόνο το κείμενο μετά το ]. Μην αλλάζεις τα [αναγνωριστικά] ούτε να προσθέτεις/σβήνεις γραμμές [..#..].
4. Αποθήκευσε το .md πίσω στο MD_WORK (ίδιο όνομα).
5. Διπλό κλικ στο 2_IMPORT_md_to_idml.bat → βγαίνει IDML_OUT\<όνομα>.idml.
6. Στο InDesign: άνοιξε το IDML_OUT\<όνομα>.idml και Save As → .indd.

Δομή φακέλων

IDML_ROUNDTRIP/
├── 1_EXPORT_idml_to_md.bat   ← βήμα 1 (διπλό κλικ)
├── 2_IMPORT_md_to_idml.bat   ← βήμα 2 (διπλό κλικ)
├── IDML_IN/                  ← το πρωτότυπο .idml (μένει εδώ όλη την ώρα)
├── MD_WORK/                  ← τα επεξεργάσιμα .md
├── IDML_OUT/                 ← τα ενημερωμένα .idml
└── FILES/                    ← ο κώδικας (idml_roundtrip.py) + README

Μορφή του .md

## σελίδα 3

[u8253#0] Copyright© 2022 Moodbook
[u8253#1] Απαγορεύεται ρητά η αντιγραφή...

• [u8253#1] = story u8253, πεδίο κειμένου #1. Είναι ο «δεσμός» με το IDML — μην το πειράζεις.
• Οι επικεφαλίδες ## Σελίδα N δείχνουν τον πραγματικό αριθμό σελίδας του InDesign (για πλοήγηση). Το περιεχόμενο είναι σε σειρά σελίδων.
• Ένας χάρακας # ── σελίδες X–Y: επαναλαμβανόμενες σελίδες προτύπου ── σημαίνει ότι εκείνες οι σελίδες είναι από master/πρότυπο (δεν έχουν δικό τους κείμενο). Έτσι βρίσκεις και σελίδες που δεν έχουν μοναδικό περιεχόμενο.
• Στο τέλος, οι ενότητες ## Πρότυπο: <όνομα> είναι το επαναλαμβανόμενο περιεχόμενο των master (αν το αλλάξεις, αλλάζει σε όλες τις σελίδες που το χρησιμοποιούν).
• Γραμμές που ξεκινούν με # αγνοούνται.
• Κενή γραμμή = νέα παράγραφος. Διαδοχικές γραμμές [..#..] ΧΩΡΙΣ κενή γραμμή ανάμεσα ανήκουν στην ΙΔΙΑ παράγραφο (σπασμένη από μορφοποίηση, π.χ. έντονα).

Κανόνες για polish / διορθώσεις (ορθογραφικά, συντακτικά)

Δουλεύει κανονικά — απλώς πες στο Claude: - Άλλαξε μόνο το κείμενο μετά το ], κράτα ίδια τα [αναγνωριστικά] (μη τα αναριθμείς). - Διατήρησε τα κενά στην αρχή/τέλος κάθε γραμμής (κρατούν τα κενά μεταξύ λέξεων). - Σε ομαδοποιημένες γραμμές (μία παράγραφος με μορφοποίηση), διόρθωσε κάθε κομμάτι επιτόπου — μη μετακινείς λέξεις από τη μια γραμμή στην άλλη (αλλιώς τα έντονα/πλάγια μπορεί να πέσουν σε λάθος λέξεις· το κείμενο πάντως μένει σωστό).

Αν κατά λάθος προστεθούν/αναριθμηθούν γραμμές, το IMPORT το εντοπίζει και προειδοποιεί (δεν χαλάει τίποτα — εφαρμόζει μόνο έγκυρες αλλαγές).

Τι μπορεί / τι όχι

Μπορεί (ασφαλές, lossless): - Διορθώσεις και αλλαγές σε υπάρχον κείμενο — οπουδήποτε, σε παραγράφους ή πίνακες. - Επέκταση/σύντμηση κειμένου μέσα σε υπάρχον πεδίο. - Διαγραφή κειμένου (άφησε το πεδίο κενό).

Δεν καλύπτεται από αυτό το εργαλείο (κάν' το στο InDesign): - Δημιουργία νέων σελίδων/πλαισίων/εικόνων/πινάκων από το μηδέν. - Νέα έντονα/πλάγια μέσα σε ένα πεδίο που ήταν απλό κείμενο (η μορφοποίηση ανήκει στο IDML, όχι στο .md). Αν προσθέσεις πολύ κείμενο, το InDesign θα δείξει «overset» — πρόσθεσε σελίδες ή ενεργοποίησε το Type ▸ Smart Text Reflow.

Σημείωση για το round-trip

Αν τρέξεις EXPORT και μετά IMPORT χωρίς καμία αλλαγή, το αποτέλεσμα είναι byte-identical με το πρωτότυπο (επαληθευμένο). Έτσι ξέρεις ότι ποτέ δεν «χαλάει» κάτι από μόνο του — αλλάζει μόνο ό,τι επεξεργάστηκες.

Απαιτήσεις

Python 3 στο PATH (τα .bat ψάχνουν πρώτα py, μετά python).

MD → IDML converter

MD → IDML (Moodbook)

Μετατρέπει αρχεία Markdown σε αρχεία IDML έτοιμα για το Adobe InDesign, χρησιμοποιώντας τα styles του template.

Πώς το χρησιμοποιείς

1. Βάλε ένα ή περισσότερα αρχεία .md στον φάκελο IN.
2. Κάνε διπλό κλικ στο Convert_MD_to_IDML.bat.
3. Πάρε τα έτοιμα .idml από τον φάκελο OUT (ένα ανά αρχείο, με το ίδιο όνομα).

Άνοιξέ τα στο InDesign με File ▸ Open.

Δομή φακέλων

MD_TO_IDML/
├── Convert_MD_to_IDML.bat   ← το εκτελέσιμο (διπλό κλικ)
├── IN/                      ← βάζεις εδώ τα .md
├── OUT/                     ← βγαίνουν εδώ τα .idml
├── TEMPLATE/                ← TEMPLATE.idml (τα styles, fonts, σελίδες)
└── FILES/                   ← ο κώδικας (md_to_idml.py) + αυτό το README

Πώς δουλεύει

Το πρόγραμμα δεν φτιάχνει IDML από το μηδέν. Παίρνει το TEMPLATE/TEMPLATE.idml (που έχει ήδη όλα τα paragraph / character / font styles, master spreads και την αλυσίδα των text frames) και αντικαθιστά μόνο το κείμενο του βασικού story με το μετατραπέν Markdown. Έτσι το αποτέλεσμα ακολουθεί πάντα ακριβώς το template.

Αν θέλεις άλλο template, αντικατέστησε το αρχείο μέσα στο TEMPLATE/ με ένα δικό σου .idml. Πρέπει να ορίζει paragraph styles με ονόματα Body, Heading 1, Headings 2.

Αντιστοίχιση Markdown → InDesign

Μία γραμμή = μία παράγραφος (το κείμενο της πηγής έχει μία παράγραφο ανά γραμμή).

Σημειώσεις

• Ύψος γραμμών πίνακα: οι γραμμές μπαίνουν με ελάχιστο ύψος και «μεγαλώνουν όσο χρειάζεται» (At Least). Το InDesign υπολογίζει το τελικό ύψος όταν ανοίξεις το αρχείο. Αν θες μεγαλύτερα κενά κελιά (π.χ. για να γράφεις με το χέρι), αύξησε το ύψος μέσα στο InDesign.
• Πλήθος σελίδων: το template έχει 3 συνδεδεμένες σελίδες. Αν το κείμενο είναι μεγαλύτερο, το InDesign το δείχνει ως overset — πρόσθεσε σελίδες/frames κανονικά.
• Η μετατροπή έχει επαληθευτεί ότι παράγει ίδιο αποτέλεσμα με τις υπάρχουσες αναφορές IDML και για τα 11 κεφάλαια του Travel Journal.

Ρυθμίσεις (για προχωρημένους)

Στην αρχή του md_to_idml.py υπάρχουν σταθερές που μπορείς να αλλάξεις: HEADING_STYLES (αντιστοίχιση επικεφαλίδων), TABLE_MODE ("table" για πραγματικούς πίνακες ή "tabs" για κείμενο με tabs), και οι μετρικές των πινάκων.

Απαιτήσεις

Python 3 εγκατεστημένη και στο PATH (το .bat ψάχνει πρώτα py, μετά python).

Chapter merge

Συναρμολόγηση κεφαλαίων (Moodbook)

Δουλεύεις κάθε κεφάλαιο ξεχωριστά, και μετά το προσθέτεις στο κύριο αρχείο στο σημείο που εσύ επιλέγεις (πριν ή μετά από οποιαδήποτε σελίδα). Όλο το υπόλοιπο κείμενο μένει ακριβώς ίδιο.

Πώς το χρησιμοποιείς

1. Βάλε το κύριο .md στον φάκελο MAIN.
2. Βάλε τα κεφάλαια .md στον φάκελο CHAPTERS.
3. Άνοιξε το κύριο .md και, σε δική της γραμμή, στο σημείο που θες να μπει το κεφάλαιο, γράψε: @@INSERT: 02_prin_fygeis_eggrafa_vizes_AFTER@@ (το όνομα = το αρχείο μέσα στο CHAPTERS, χωρίς .md)
4. Διπλό κλικ στο MERGE.bat. Το αποτέλεσμα βγαίνει στο OUT\<όνομα>_merged.md.

Μπορείς να βάλεις πολλούς δείκτες (πολλά κεφάλαια) σε ένα πέρασμα — ο καθένας αντικαθίσταται με το αντίστοιχο κεφάλαιο.

«Πριν ή μετά από ποια σελίδα»

Επιλέγεις εσύ, βάζοντας τον δείκτη ΑΚΡΙΒΩΣ εκεί: - Μετά τη σελίδα Χ → βάλε τον δείκτη αμέσως μετά το περιεχόμενο εκείνης της σελίδας. - Πριν τη σελίδα Χ → βάλε τον δείκτη ακριβώς πριν αρχίσει εκείνη η σελίδα.

Σημαντικό για τους αριθμούς σελίδων

Ο πραγματικός αριθμός σελίδων προκύπτει από το InDesign όταν στοιχειοθετεί το κείμενο (ένα κεφάλαιο μπορεί να πιάσει 2 ή 5 σελίδες — μόνο το InDesign το ξέρει). Γι' αυτό το εργαλείο δεν ξαναριθμεί σελίδες· κρατά τα κεφάλαια στη σωστή σειρά και η αρίθμηση μπαίνει στο InDesign.

Προσοχή αν το κύριο είναι round-trip export (μορφή [id])

Αν το κύριο .md είναι export με [αναγνωριστικά], το κεφάλαιο που εισάγεις είναι νέο κείμενο χωρίς [id]. Άρα: - Για κύριο έγγραφο κειμένου (χειρόγραφο): τέλεια, το κεφάλαιο μπαίνει στη σειρά. - Για επιστροφή στο IDML: το νέο κεφάλαιο δεν γίνεται αυτόματα νέες σελίδες στο βιβλίο — αυτό απαιτεί δημιουργία σελίδων στο InDesign. Το round-trip ενημερώνει μόνο υπάρχον κείμενο.

Φάκελοι

MERGE_CHAPTERS/
├── MERGE.bat        ← διπλό κλικ
├── MAIN/            ← το κύριο .md (με τους δείκτες @@INSERT: ...@@)
├── CHAPTERS/        ← τα κεφάλαια .md
├── OUT/             ← το συναρμολογημένο .md
└── FILES/           ← κώδικας + README

Python 3 στο PATH (το .bat ψάχνει πρώτα py, μετά python).

InDesign insert script

InDesign script: Insert_Chapter.jsx (Moodbook)

Εισάγει ένα κεφάλαιο (.idml, π.χ. από MD_TO_IDML\OUT) μέσα στο ανοιχτό βιβλίο, σε σελίδα που επιλέγεις (μετά/πριν), διατηρώντας τα styles. Το InDesign ανανεώνει αυτόματα την αρίθμηση σελίδων.

Εγκατάσταση

Το script είναι ήδη εγκατεστημένο στους φακέλους Scripts Panel των InDesign 16 & 21 (...\Adobe\InDesign\Version XX\en_US\Scripts\Scripts Panel\). Φαίνεται στο Window ▸ Utilities ▸ Scripts ως Insert_Chapter.

ΣΗΜΑΝΤΙΚΟ: το Scripts panel τρέχει το αντίγραφο μέσα σε αυτόν τον φάκελο. Αν αλλάξει ο κώδικας, πρέπει να ξανα-αντιγραφεί εκεί (αλλιώς τρέχεις παλιά έκδοση). Όταν κάνω αλλαγή, την ξανα-εγκαθιστώ για σένα — απλώς ξανατρέξε το από το Scripts panel.

Χρήση

1. Άνοιξε το κύριο βιβλίο (.indd ή το .idml του) στο InDesign.
2. Στο Scripts panel, διπλό κλικ στο Insert_Chapter.
3. Στον διάλογο: - Κεφάλαιο (.idml): διάλεξε το αρχείο του κεφαλαίου. - Σελίδα αναφοράς: ο αριθμός σελίδας όπως φαίνεται στο βιβλίο (π.χ. 29). - Θέση: «μετά» (το κεφάλαιο ξεκινά στην επόμενη σελίδα) ή «πριν». - (προαιρετικά) Εφάρμοσε master του βιβλίου στις νέες σελίδες.
4. Έλεγξε το αποτέλεσμα και αποθήκευσε (το script δεν αποθηκεύει μόνο του).

Τα scripts

• Expand_Chapter.jsx — με το κεφάλαιο ανοιχτό: προσθέτει όσες σελίδες χρειάζονται ώστε να φανεί όλο το κείμενο (το MD_TO_IDML βγάζει πάντα 3 σελίδες με το υπόλοιπο κρυμμένο).
• Insert_Chapter.jsx — με το βιβλίο ανοιχτό: εισάγει το κεφάλαιο στη σελίδα που θες (κάνει και μόνο του την επέκταση αν χρειαστεί).
• Batch_Build_Chapters.jsx — χωρίς ανοιχτό έγγραφο: διάλεξε έναν φάκελο με .idml και το script ανοίγει το καθένα, (προαιρετικά) το κάνει Expand, και αποθηκεύει το αντίστοιχο .indd στον ίδιο φάκελο. Batch εκδοχή του «open → build → save».

Batch_Build_Chapters.jsx — χρήση

1. Window ▸ Utilities ▸ Scripts → διπλό κλικ στο Batch_Build_Chapters.
2. Διάλεξε τον φάκελο με τα κεφάλαια .idml.
3. Στον διάλογο επιλογών: - Χτίσε όλες τις σελίδες (Expand) — ίδια λογική με το Expand_Chapter (default: ναι). - Παράλειψε αν υπάρχει ήδη .indd — αλλιώς αντικαθιστά (default: αντικαθιστά). - Ψάξε και σε υποφακέλους (default: όχι). - Κλείσε κάθε έγγραφο μετά την αποθήκευση (default: ναι — για μαζικό τρέξιμο).
4. Στο τέλος: σύνοψη (OK / skipped / overset / fail) + log _batch_build_log.txt στον φάκελο.

Σημειώσεις: τρέχει με κατεσταλμένα dialogs (NEVER_INTERACT) ώστε missing-font/link προειδοποιήσεις να μη σταματούν το batch — γι' αυτό, αν κάποιο κεφάλαιο έχει θέμα, θα το δεις ως FAIL ή ⚠ overset στο log, όχι με popup. Τα .indd παίρνουν το ίδιο basename με τα .idml.

Η πλήρης ροή

1. Διορθώσεις υπάρχοντος → IDML_ROUNDTRIP (export → edit → import, lossless).
2. Νέο κεφάλαιο → γράψε prose .md → MD_TO_IDML → chapter.idml (ίδιο μέγεθος σελίδας με το βιβλίο, χάρη στο match_template_to_book.py).
3. Δες το κεφάλαιο σωστά (προαιρετικό) → άνοιξέ το → Expand_Chapter → αποθήκευσε → τώρα έχει όλες τις σελίδες του.
4. Συναρμολόγηση στο βιβλίο → άνοιξε το βιβλίο → Insert_Chapter → εισαγωγή στη σελίδα που θες → auto-renumber.

Σημαντικά

• Μέγεθος σελίδας: το κεφάλαιο πρέπει να έχει το ίδιο μέγεθος σελίδας με το βιβλίο. Ευθυγράμμισέ το με το MD_TO_IDML\FILES\match_template_to_book.py πριν φτιάξεις το κεφάλαιο.
• Facing pages: αν το κεφάλαιο έχει μονό αριθμό σελίδων, οι επόμενες σελίδες αλλάζουν recto/verso (κανονική συμπεριφορά InDesign). Για να κρατήσεις τα spreads, πρόσθεσε ζυγό αριθμό σελίδων.
• Μέγεθος κεφαλαίου (αυτόματο): αν το κείμενο ξεπερνά τις 3 σελίδες του template, το script προσθέτει μόνο του όσες σελίδες χρειάζονται μέσα στο κεφάλαιο (ώστε να μη «κόβεται» στη 3η) πριν τις εισάγει στο βιβλίο.
• Styles: διατηρούνται κατά όνομα (ίδια ονόματα στυλ → ταιριάζουν· νέα → προστίθενται).
• Εισαγωγή σε επίπεδο σελίδας: το κεφάλαιο μπαίνει ακριβώς μετά/πριν τη σελίδα που όρισες (όχι σε όρια spread). Οι σελίδες αντιγράφονται όλες μαζί ώστε να διατηρηθεί η ροή (threading) του κειμένου.
• Δοκιμή (πρώτη φορά, σε ΑΝΤΙΓΡΑΦΟ): το script δεν το έχω τρέξει σε InDesign από εδώ. Έλεγξε ότι: (1) το κεφάλαιο προσγειώθηκε στη σωστή σελίδα, (2) η σειρά των σελίδων είναι σωστή, (3) το κείμενο ρέει σωστά. Αν κάτι από αυτά αστοχήσει, πες μου τι είδες και το διορθώνω.

Designed chapter — README

DESIGNED_CHAPTER — Moodbook σχεδιασμένο κεφάλαιο → IDML

Αυτόνομο εργαλείο (architecture B). Δεν πειράζει το παλιό MD_TO_IDML / IDML_ROUNDTRIP.

Ροή

1. Γράφεις το κεφάλαιο σε Markdown με το συμβόλαιο σήμανσης → CHAPTER_AUTHORING_SPEC.md (περιέχει και το έτοιμο prompt για Claude chat).
2. Βάζεις το .md στο IN\.
3. Τρέχεις τον converter → παράγει σχεδιασμένο IDML στο OUT\ (στυλ/χρώματα/master/running header έρχονται από το TEMPLATE\CHAPTER_TEMPLATE.idml· το κείμενο ρέει στο primary text frame και οι σελίδες μεγαλώνουν με Smart Text Reflow).
4. 2ος χρόνος — εικονίδια: ανοίγεις το IDML στο InDesign και τρέχεις το jsx (Φ4) που τοποθετεί τα SVG από το ICONS\ anchored στα Headings 2 (βάσει του {icon=...} του MD).

Φάκελοι

• IN\ — εδώ βάζεις το .md προς μετατροπή.
• OUT\ — εδώ βγαίνει το .idml.
• ICONS\ — εδώ βάζεις τα 8 SVG (plug/shirt/shoe/ruler/clock/phone/droplet/thermometer) για τη Φ4.
• TEMPLATE\ — το validated CHAPTER_TEMPLATE.idml (body story μέσω master PrimaryTextFrame).
• FILES\ — ο κώδικας: chapter_to_idml.py (converter) + αργότερα place_icons.jsx.
• CHAPTER_AUTHORING_SPEC.md — το συμβόλαιο σήμανσης MD → InDesign styles.

Κατάσταση

• [x] Scaffold + template + spec.
• [x] Φ1 converter (rewire σε primary story u19b, flow κειμένου).
• [x] Φ2 opener (ChapterNum/Kicker/ChapterTitle/Standfirst) + callout.
• [x] Φ3 MB tables (header + zebra).
• [~] ΕΛΕΓΧΟΣ στο InDesign — το κείμενο ρέει & τα styles είναι σωστά, αλλά η σελιδοποίηση πρέπει να γίνει ΜΕΣΑ στο InDesign (μόνο αυτό φτιάχνει σελίδες).
• [ ] FILES\Build_Chapter.jsx — pagination (autoflow primary frame, master A-Parent) — υπό δοκιμή.
• [ ] Φ4 icon placement (θα μπει μέσα στο Build_Chapter.jsx· διαβάζει το OUT\*.icons.json).

Ροή τελική

MD (IN) → Convert_Chapter_to_IDML.bat → OUT*.idml (περιεχόμενο, overset σε 1 σελίδα) → άνοιξέ το στο InDesign → FILES\Build_Chapter.jsx (φτιάχνει τις σελίδες) → autonomous chapter *_paged.indd → Insert_Chapter.jsx για ένταξη στο βιβλίο.

Designed chapter — build guide

DESIGNED_CHAPTER — Build Guide

Πρακτικός οδηγός: από .md κεφάλαιο → σελιδοποιημένο .indd → ένταξη στο βιβλίο (.indb). Έκδοση οδηγού: v2.6 · Έκδοση συμβολαίου (MOODBOOK_AUTHORING_MAP.json): v2.6 (ευθυγραμμισμένα). Επιβεβαιωμένο τρίπλευρα: contract ↔ template ↔ guide (βλ. §10). Συνοδεύει το STATE_HANDOFF.md.

✅ Το version drift λύθηκε. Ο κανονικός αριθμός συμβολαίου είναι v2.6 (από το ίδιο το MOODBOOK_AUTHORING_MAP.json). Ο header και τα element tags του §6 ευθυγραμμίστηκαν σε αυτόν.

🔧 Ανοιχτά σημεία ευθυγράμμισης (template ↔ contract) — 3, μικρά, προς απόφαση: 1. Caption fallback του ::: art: το contract το δηλώνει ως fallback του ArtNote, αλλά δεν υπάρχει Caption style στο template. Αβλαβές σήμερα (το ArtNote υπάρχει). → πρόσθεσε Caption ή βγάλε το fallback. 2. TOC_Chapter: υπάρχει στο template αλλά λείπει από το style_inventory_v5 του contract. → πρόσθεσέ το στο JSON inventory (είναι χρήσιμο για Book TOC). 3. Art_FullPage object style: υπάρχει στο template, αλλά το contract (v2.3) δηλώνει ότι τα Art_* object styles καταργήθηκαν. → ή βγάλ' το από το template, ή ξανα-τεκμηρίωσέ το ρητά.

Επίσης: το contract αναφέρει το template ως CHAPTER_TEMPLATE_v5.idml, ενώ το ενεργό αρχείο λέγεται CHAPTER_TEMPLATE.idml — ίδιο v5, μόνο το όνομα διαφέρει· ευθυγράμμισε ονομασία αν θες exact-match παντού.

0. Συνοπτικά (TL;DR)

.md  →  IN\
   │  Convert_Chapter_to_IDML.bat            (Python· MD → IDML)
   ▼
OUT\<chapter>.idml  (+ .icons.json)
   │  [InDesign] Build_Chapter.jsx           (σελιδοποίηση → <chapter>_paged.indd)
   │  [InDesign] place_anchored.jsx          (section icons)
   │  [InDesign] εικόνες με το χέρι           (search «⟦ ΕΙΚΟΝΑ»)
   ▼
OUT\<chapter>_paged.indd  (έτοιμο κεφάλαιο)
   │  [InDesign] Book panel ▸ Add Document   (ένταξη στο .indb)
   ▼
MASTER_BOOK.indb  (Style Source + Synchronize + Export/Package)

1. Φάκελοι

2. Setup template — ΜΙΑ φορά (έγινε ήδη)

Το ενεργό TEMPLATE\CHAPTER_TEMPLATE.idml περιέχει ήδη όλα τα απαραίτητα styles για το πλήρες authoring vocabulary. Δεν χρειάζεται να κάνεις τίποτα στην κανονική ροή.

Επιβεβαιωμένο από το IDML (πλήρης κατάλογος → §10): - 27 paragraph styles — καλύπτουν front-matter, headings, σώμα, λίστες, όλα τα ::: blocks, checklist (multi-column), TOC, running header. - 5 character styles — InlineBold, Italic, CalloutLabel, PromptLabel, TableHeaderText. - 2 object styles — Checkbox (legacy· βλ. §8), Art_FullPage (frame για full-page εικόνα με το χέρι). - 1 table + 4 cell styles — MB_BodyTable με MB_HeaderCell / MB_BodyCell / MB_ZebraCell / MB_CalloutCell. - Native list types ΑΝΕΠΑΦΑ (επιβεβαιωμένο): BulletList=Bullet, NumberedList=Numbered, FormCheckBoxes=Bullet με glyph ☐ U+2610 σε Minion Pro Regular. Rule Below παρόν σε Lines, RunHeader, FormCheckBoxes.

👉 Μόνο αν ξανα-εξάγεις/round-trip-άρεις το template από το InDesign και χαθούν styles ή list types, ξανατρέξε:

python FILES\inject_styles.py    TEMPLATE\CHAPTER_TEMPLATE.idml FILES\artnote_style.json -o TEMPLATE\tmp.idml
python FILES\patch_liststyles.py TEMPLATE\tmp.idml -o TEMPLATE\CHAPTER_TEMPLATE.idml

• inject_styles.py → προσθέτει νέα styles (π.χ. ArtNote).
• patch_liststyles.py → φτιάχνει/διορθώνει το List Type σε BulletList/NumberedList (token BulletList/NumberedList).
• Το FormCheckBoxes glyph (☐ Minion Pro) φτιάχνεται/επιβεβαιώνεται στο InDesign (αυθεντική πηγή· βλ. §8 pitfall).

3. ΦΑΣΗ 1 — Μετατροπή MD → IDML

1. Βάλε το .md στο IN\.
2. Lint πρώτα: python FILES\lint_chapter.py → πρέπει «0 errors». Πιάνει το συχνό λάθος attributes σε λάθος γραμμή (count=10 κάτω από το ::: lines αντί ::: lines count=10) που αλλιώς περνά σιωπηλά και «σπάει» στο InDesign.
3. Διπλό κλικ στο Convert_Chapter_to_IDML.bat (ή python FILES\chapter_to_idml.py).
4. Έλεγξε το log: - [OK] <file> … (N sections, M icons) → επιτυχία. - κανένα [ΣΦΑΛΜΑ], ιδανικά κανένα [ΣΗΜ] (το [ΣΗΜ] σημαίνει ότι ένα style έχασε το list type → μπήκε prefix fallback· διορθώνεται στο InDesign). - [art][note] = κάθε ::: art έγινε σημείωση (note-only).
5. Παράγεται στο OUT\: - <chapter>.idml ← αυτό ανοίγεις στο InDesign. - <chapter>.icons.json ← sidecar για τα section icons. - <chapter>.icml ← (δεν το χρειάζεσαι).

4. ΦΑΣΗ 2 — InDesign, ανά κεφάλαιο

Πώς τρέχεις jsx: Window ▸ Utilities ▸ Scripts → (μία φορά) ρίξε τα FILES\*.jsx στον φάκελο User του Scripts panel → διπλό κλικ.

Σημείωση: τα checkboxes ΔΕΝ μπαίνουν πια με jsx — είναι το auto-bullet ☐ του FormCheckBoxes. Το checkbox pass του place_anchored.jsx είναι legacy/no-op.

Γρήγορα oπτικά checks (πρώτη φορά): - checklist: κάθε στοιχείο δείχνει ☐ + γραμμή για γράψιμο. - bullets • και αριθμοί 1. 2. 3. σωστά, με κάθε αριθμημένη λίστα να ξεκινά από 1. - {half} πεδία: 2-2 δίπλα-δίπλα. - section icons: σωστή θέση/μέγεθος (iteration-1).

5. ΦΑΣΗ 3 — Ένταξη στο βιβλίο (.indb Book)

Νέα διαχείριση: InDesign Book (.indb) με τα κεφάλαια ως members (όχι ως «links»). Το παλιό Insert_Chapter.jsx (που έχωνε σελίδες σε ένα μονολιθικό .indd) έχει αποσυρθεί.

1. Δημιουργία/άνοιγμα του βιβλίου: File ▸ New ▸ Book… → MASTER_BOOK.indb (μία φορά).
2. Add Document: στο Book panel, + → διάλεξε το OUT\<chapter>_paged.indd. Επανέλαβε ανά κεφάλαιο.
3. Σειρά: σύρε τα κεφάλαια στη σωστή σειρά μέσα στο panel.
4. Style Source: όρισε ποιο document είναι η πηγή styles (το εικονίδιο αριστερά). Σύσταση: το front-matter ή το 1ο κεφάλαιο (όχι dummy — βλ. παρακάτω).
5. Synchronize (προαιρετικό· δίχτυ ασφαλείας): Book panel menu ▸ Synchronize Options → τσέκαρε Paragraph/Character/Object/Table/Cell styles + Swatches (τα Master Pages μόνο αν θες να επιβάλεις γεωμετρία/headers παντού) → Synchronize.
6. Numbering: Book panel menu ▸ Book Page Numbering Options → συνεχής αρίθμηση· ανά κεφάλαιο Document Numbering Options αν χρειάζεται section start.
7. TOC (προαιρετικό): για πίνακα περιεχομένων σε επίπεδο Book, χτίσε TOC style που μαζεύει το paragraph style ChapterTitle και εμφανίζει την καταχώρηση με το TOC_Chapter (υπάρχει έτοιμο στο template).
8. Export/Package σε επίπεδο Book (όχι ανά κεφάλαιο): επίλεξε τα documents → Export Book to PDF / Package Book.

Style Source — η παγίδα των σελίδων: κάθε member (και ο Style Source) μετράει σελίδες και εξάγεται. - Προτιμώμενο: Style Source = πραγματικό document (front-matter/κεφ. 1) → χωρίς dummy. - Αν θες dedicated styles_master.indd (παράγωγο του template, βλ. STATE_HANDOFF): βάλ' το τελευταίο στο Book (να μη μετατοπίζει την αρίθμηση) και απο-επίλεξέ το στο export.

Προϋποθέσεις: ίδιο page size/margins σε όλα τα members (το ίδιο template το εγγυάται — A5, facing pages· βλ. §10).

6. Authoring quick-reference (markup → style)

Πλήρες συμβόλαιο: FILES\CHAPTER_AUTHORING_SPEC.md (+ MOODBOOK_AUTHORING_MAP.json). Συνοπτικά — κάθε style παρακάτω επιβεβαιώθηκε ότι υπάρχει στο template (εξαίρεση: το documented fallback Caption — βλ. ⚠️ κάτω):

Front-matter (στην κορυφή, μέσα σε ---): chapter_number→ChapterNum · kicker→Kicker · title→ChapterTitle (υποχρεωτικό) · standfirst→Standfirst.

Block: - ## Τίτλος {icon=NAME} → Headings 2 (icon: plug/shirt/shoe/ruler/clock/phone/droplet/thermometer) - απλή γραμμή → Body · **bold**→InlineBold · *italic*→Italic - - στοιχείο → BulletList (auto •) · 1. στοιχείο → NumberedList (auto 1.) - GFM πίνακας (|---|) → MB_BodyTable (κελιά: MB_HeaderCell header · MB_ZebraCell zebra · MB_BodyCell body · MB_CalloutCell για callout-κελί· header text → char style TableHeaderText)

Fenced (:::): - callout → Callout (+ label char style CalloutLabel) - quote → Quote (+ γραμμή απόδοσης → QuoteAttribution) - fields (+{half}) → FormLabel (label πεδίου) · ομαδικός τίτλος → FormGroupLabel - prompt → Prompt (+ label char style PromptLabel) - pull → Pull - lines count=N → Lines (Rule Below → φαίνονται οι γραμμές) - day count=N → DayLabel (+ Lines για το γράψιμο) - toplist title=… count=N → ToplistTitle + ToplistItem - checklist → ChecklistName / ChecklistColHeader / ChecklistItem ή FormCheckBoxes (βλ. κάτω) - art → ArtNote (note-only· βλ. κάτω)

::: checklist name="…" [columns="A|B|C"] rows=N — [v2.6] - Multi-column (default MUST HAVE | I HAVE | DONE): name → ChecklistName, μετά ένας borderless πίνακας (στήλη label με ChecklistItem + μία ☐-στήλη ανά κεφαλίδα ChecklistColHeader). Το ☐ = χαρακτήρας U+2610 σε Minion Pro, κεντραρισμένο. Στοίχιση από τον πίνακα — χωρίς jsx, χωρίς tab stops, χωρίς anchored objects. - Single column (columns="DONE"): κάθε - στοιχείο → FormCheckBoxes (☐ U+2610 auto-bullet, Minion Pro).

::: art src="…" [layout="…"] [caption="…"] [alt="…"] — [v2.3] NOTE-ONLY ΔΕΝ τοποθετεί εικόνα· βγάζει σημείωση ⟦ ΕΙΚΟΝΑ: <src> — <layout> · <caption> ⟧ (style ArtNote, magenta). Το layout (π.χ. full-page) είναι μόνο υπόδειξη μέσα στο κείμενο της σημείωσης — όχι geometry/object style· την εικόνα και το frame τα βάζεις με το χέρι (§4 βήμα 3). alt = μεταδεδομένο, δεν τυπώνεται.

⚠️ Caption fallback: το contract δηλώνει paragraph_style_fallback: "Caption" για το ::: art, αλλά δεν υπάρχει Caption style στο template. Αβλαβές σήμερα (το ArtNote υπάρχει, οπότε το fallback δεν ενεργοποιείται)· αν όμως χαθεί το ArtNote, ο fallback θα αποτύχει. Απόφασε: ή πρόσθεσε Caption, ή βγάλε το fallback. ℹ️ Το object style Art_FullPage υπάρχει ακόμη στο template, αλλά το contract (v2.3) το θεωρεί καταργημένο μαζί με τα υπόλοιπα Art_*. Ο converter δεν το αγγίζει· αν το χρησιμοποιείς για manual full-page placement, κάν' το συνειδητά — ή ευθυγράμμισε template/contract (βλ. header + §10).

Απαγορευμένα: #/###, >, ![](), raw HTML, code fences εκτός :::, σκέτο --- στο σώμα.

ℹ️ Το template κρατά και ένα paragraph style Heading 1 που δεν αντιστοιχεί σε authoring markup (το # είναι απαγορευμένο). Είναι reserved/legacy — μην το στοχεύεις από .md.

7. Tools (στο FILES\)

8. Pitfalls / να ξέρεις

• List-type round-trip pitfall (ΚΡΙΣΙΜΟ): το BulletsAndNumberingListType δεν επιβιώνει αξιόπιστα σε round-trips του InDesign. Το σωστό native token είναι BulletList/NumberedList (όχι UnorderedList/OrderedList). Αυθεντική πηγή = το InDesign· ο converter κάνει auto-detection και πέφτει σε prefix fallback (•/1./☐) αν λείπει — δεν σπάει ποτέ. Τρέχουσα κατάσταση: στο ενεργό template τα list types είναι ανέπαφα (επιβεβαιωμένο — §10), οπότε δεν ενεργοποιείται fallback· η προειδοποίηση ισχύει μόνο αν ξανα-εξάγεις template.
• FormCheckBoxes: στο τρέχον template είναι σωστό (List Type = Bullets, glyph = ☐ U+2610, Minion Pro Regular, με Rule Below για τη γραμμή γραψίματος). Αν χαθεί σε round-trip, ο converter βάζει ☐\t prefix (ίσως χωρίς σωστό glyph αν το body font δεν έχει ☐).
• Λείπει το fallback style Caption: ο converter το ονομάζει ως εναλλακτικό του ArtNote, αλλά δεν υπάρχει στο template (βλ. §6 ⚠️). Το ArtNote υπάρχει, οπότε ok σήμερα — αλλά κλείσε το κενό.
• Fonts: το IDML στηρίζεται σε Neutraface Text (κύρια) + Minion Pro (για το ☐). Κουβαλά και stray fonts (Myriad Pro, Kozuka Mincho Pr6N) — υπολείμματα InDesign defaults/round-trip· μην τα εμπιστεύεσαι σε styles. Κράτα εγκατεστημένα τα Neutraface Text + Minion Pro· έλεγξε Type ▸ Find/Replace Font για missing.
• Attributes στη γραμμή του fence (ΣΥΧΝΟ): ::: lines count=10 ✅ — όχι count=10 σε επόμενη γραμμή. Τρέξε lint_chapter.py πριν το convert· το πιάνει.
• All-caps ελληνικά: ο converter αφαιρεί τον τόνο αυτόματα για styles Capitalization=AllCaps (το InDesign AllCaps τον κρατά). GREP style ΔΕΝ μπορεί να αλλάξει χαρακτήρες — γι' αυτό λύθηκε στον converter. Στο τρέχον template μόνο το Kicker είναι AllCaps — αυτό είναι το style που αγγίζει ο κανόνας.
• Checklists: πλέον είναι πίνακας με ☐ glyph (Minion Pro) — στοιχίζονται μόνα τους, δεν χρειάζονται tab stops ούτε place_anchored. (Το παλιό anchored-Checkbox object style / pass καταργήθηκε — έβγαζε γιγάντια κουτιά· το object style Checkbox παραμένει στο template ως legacy, δεν χρησιμοποιείται.) Απαιτείται η γραμματοσειρά Minion Pro εγκατεστημένη (για το ☐).
• Writing lines (::: lines, day): τα Lines / RunHeader / FormCheckBoxes έχουν Rule Below (επιβεβαιωμένο) για να φαίνονται οι γραμμές — προστέθηκε byte-safe· αν ξανα-εξάγεις template από InDesign, κράτα το.
• Page size: template = βιβλίο (A5, facing pages), αλλιώς δεν ταιριάζει στο Book.

9. Εκκρεμή οπτικά τεστ (όταν ανοίξεις InDesign)

• [ ] auto-list (bullets/numbers, restart ανά λίστα) — rendering
• [ ] FormCheckBoxes ☐ + γραμμή — rendering
• [ ] multi-column checklist (borderless table, ☐ κεντραρισμένα) — στοίχιση/glyph
• [ ] {half} δίστηλα πεδία
• [ ] place_anchored.jsx section icons (θέση/μέγεθος)
• [ ] Art_FullPage frame σε full-page εικόνα (§4 βήμα 3)
• [ ] TOC από ChapterTitle → TOC_Chapter (αν στηθεί Book TOC)

10. Verified template snapshot — CHAPTER_TEMPLATE.idml

Άμεσα από επιθεώρηση του ενεργού IDML. Χρησιμοποίησέ το ως baseline για diff μετά από κάθε round-trip.

Document: A5 · 420.94 × 595.28 pt (~148 × 210 mm) · facing pages = true · margins T/B ≈ 36.85 pt, inside ≈ 42.52 pt, outside ≈ 39.69 pt.

Fonts (families): Neutraface Text (κύρια) · Minion Pro (☐ glyph) · Myriad Pro, Kozuka Mincho Pr6N (stray — μην τα στηρίζεις).

Colors (swatches): MB_Grey10 · MB_Grey14 · MB_Grey22 · MB_Grey85 · MB_GreyLine (K=28) · Magenta (χρήση: ArtNote) · PANTONE 405 C · διάφορα CMYK process (C/M/Y + μείξεις) από round-trip.

Paragraph styles (27): ChapterNum · Kicker (AllCaps) · Heading 1 (reserved) · Headings 2 · ChapterTitle · Standfirst · Body · Callout · Quote · QuoteAttribution · Pull · Prompt · BulletList (Bullet) · NumberedList (Numbered) · FormCheckBoxes (Bullet ☐ U+2610 / Minion Pro / Rule Below) · FormLabel · FormGroupLabel · Lines (Rule Below) · DayLabel · ToplistTitle · ToplistItem · ChecklistName · ChecklistColHeader · ChecklistItem · RunHeader (Rule Below) · ArtNote (Magenta) · TOC_Chapter.

Character styles (5): InlineBold · Italic · CalloutLabel · PromptLabel · TableHeaderText.

Object styles (2): Checkbox (legacy, αχρησιμοποίητο) · Art_FullPage (⚠️ contract-deprecated — βλ. κάτω).

Table style (1): MB_BodyTable. Cell styles (4): MB_HeaderCell · MB_BodyCell · MB_ZebraCell · MB_CalloutCell.

Native list types (ανέπαφα): BulletList = BulletList · NumberedList = NumberedList · FormCheckBoxes = BulletList (glyph ☐ U+2610, Minion Pro Regular).

Τρίπλευρη ευθυγράμμιση — contract (v2.6) ↔ template ↔ guide

Λοιπά (μη-blocking): Heading 1 reserved/χωρίς markup (το ##→Headings 2)· RunHeader δεν εκπέμπεται από .md (auto από ChapterTitle μέσω Text Variable σε master page)· stray fonts/swatches (Myriad Pro, Kozuka Mincho, process CMYK) από round-trip.

Chapter authoring spec

Moodbook — Chapter Authoring Spec (ενοποιημένο)

Έκδοση 2.6 · συμβόλαιο σήμανσης (.md) → InDesign styles Template: CHAPTER_TEMPLATE_v5.idml · μηχαναγνώσιμη όψη: MOODBOOK_AUTHORING_MAP.json (v2.4)

Τα δύο αρχεία είναι μία αλήθεια, δύο όψεις. Ο converter φορτώνει το JSON· αυτό εδώ είναι για ανθρώπους. Όποια αλλαγή γίνεται και στα δύο. Τα ονόματα styles είναι ΑΚΡΙΒΗ.

1. Δομή αρχείου

• Ένα .md ανά κεφάλαιο.
• Front-matter ανάμεσα σε --- (μόνο στην κορυφή):

Σειρά opener: ChapterNum → Kicker → ChapterTitle → Standfirst.

2. Στοιχεία block (εκτός fenced)

3. Inline

4. Πίνακες (GFM)

GFM με γραμμή |---|. Table → MB_BodyTable· header cells → MB_HeaderCell + TableHeaderText· σώμα → MB_BodyCell· εναλλάξ σειρές → MB_ZebraCell.

5. Fenced blocks (:::)

Παράμετροι: key=value ή key="τιμή με κενά"· πολλαπλές τιμές με |.

::: callout

Container MB_CalloutCell + Callout. Πρώτη ολόκληρη-bold φράση → CalloutLabel.

::: fields

- Ετικέτα → FormLabel (+ γραμμή). **Κείμενο** μέσα → FormGroupLabel (διαχωριστής ομάδας). {half} → πεδίο μισού πλάτους. Κενή/dotted ετικέτα = custom πεδίο για ονομασία.

::: quote

Σώμα → Quote· — Συγγραφέας (τελευταία γραμμή) → QuoteAttribution.

::: prompt

Σώμα → Prompt· πρώτη ολόκληρη-bold φράση (μπορεί να έχει αριθμό) → PromptLabel.

::: checklist name="…" [columns="A|B|C"] rows=N — [v2.6]

Δύο μορφές, ανάλογα με το πλήθος στηλών: - Multi-column (default MUST HAVE | I HAVE | DONE, ή 2+ στήλες): name → ChecklistName, μετά ένας borderless πίνακας — στήλη label (ChecklistItem) + μία στήλη ☐ ανά κεφαλίδα (ChecklistColHeader). Κάθε κουτί = χαρακτήρας ☐ (U+2610) σε Minion Pro, κεντραρισμένο· λεπτή κάτω-γραμμή ανά σειρά. Στοίχιση από τον πίνακα — χωρίς anchored object, χωρίς place_anchored, χωρίς tab stops. - Single column (columns="X" με μία τιμή): κάθε - στοιχείο → FormCheckBoxes με auto-bullet ☐ (U+2610, Minion Pro) + Rule Below.

Κενό block (χωρίς items) → όνομα + rows κενές σειρές (default 8). v2.6: το παλιό anchored Checkbox object + place_anchored καταργήθηκε (έβγαζε γιγάντια overlapping κουτιά).

::: pull

Pull — display flourish χωρίς απόδοση (≠ quote).

::: lines count=N

N γραμμές γραφής → Lines. Προαιρετική run-in **ΕΤΙΚΕΤΑ** πριν το block.

::: day count=N [lines=M]

Παράγει N μπλοκ ημέρας. Εσωτερική γραμμή = ετικέτες κεφαλίδας χωρισμένες με | (π.χ. ΗΜΕΡΑ | τοποθεσία) → DayLabel (ετικέτα + inline κενό). Από κάτω χώρος γραφής → Lines (ενσωματωμένο travel log). lines=M ορίζει ρητό αριθμό γραμμών.

::: toplist count=N title="…"

title → ToplistTitle· N αριθμημένες κενές θέσεις (1.,2.,…) → ToplistItem.

::: art src="…" [layout="…"] [caption="…"] [alt="…"] — NOTE-ONLY (v2.3)

ΔΕΝ τοποθετεί τίποτα. Εκπέμπει ΜΙΑ ορατή παράγραφο-σημείωση· την εικόνα τη βάζει ο χρήστης με το χέρι στο InDesign. (Άλλαξε από v2.2: η αυτόματη τοποθέτηση μέσω jsx καταργήθηκε γιατί δεν δούλευε αξιόπιστα.) - Style: ArtNote (fallback: Caption αν λείπει από το template). - Κείμενο: ⟦ ΕΙΚΟΝΑ: <src>[ — <layout>][ · <caption>] ⟧ - src = πληροφοριακό (μπορεί να αναφέρει αρχείο που δεν υπάρχει ακόμη)· τα #Uxxxx γίνονται decode μόνο για εμφάνιση. - layout = προαιρετική υπόδειξη που μπαίνει στο κείμενο (π.χ. full-page)· όχι geometry/object style. - caption → μπαίνει στο κείμενο της σημείωσης. alt = μεταδεδομένο, ΔΕΝ τυπώνεται. - Καταργήθηκαν: resolve src σε Links/, μετατροπή format, τα Art_* object styles, κάθε art jsx.

6. Διευκρίνιση -

Κρίνεται από το περικλείον block: μέσα σε ::: fields → πεδίο (FormLabel)· μέσα σε ::: checklist → στοιχείο (ChecklistItem multi-column ή FormCheckBoxes single)· top-level → bullet (BulletList).

7. Απαγορευμένα

# (μονό), ###, >, εικόνες ![](), raw HTML, code fences εκτός :::, μονό --- στο σώμα.

ΠΡΟΣΟΧΗ — attributes στη γραμμή του fence. Τα count/name/title/columns/rows/src/layout/… ΠΡΕΠΕΙ να μπαίνουν στην ΙΔΙΑ γραμμή με το ::: (π.χ. ::: lines count=10). Σε δική τους γραμμή αγνοούνται σιωπηλά και το block «σπάει». Τρέξε python FILES\lint_chapter.py πριν το convert — το πιάνει. All-caps ελληνικά: ο converter αφαιρεί αυτόματα τον τόνο σε styles Capitalization=AllCaps (π.χ. Kicker) ώστε «Τα ταξίδια» → «ΤΑ ΤΑΞΙΔΙΑ» (όχι «ΤΑΞΊΔΙΑ»).

8. ΔΕΝ προέρχονται από το .md

• RunHeader: auto από ChapterTitle (Text Variable, master page).
• Heading 1: δεσμευμένο/αχρησιμοποίητο από το ##.
• Εικόνες/χάρτες/illustrations: [v2.3] καμία δεν προέρχεται από το .md. Το ::: art βγάζει ΜΟΝΟ σημείωση ⟦ ΕΙΚΟΝΑ ⟧· τις εικόνες τις τοποθετεί ο χρήστης με το χέρι στο InDesign.
• Checkbox ☐: [v2.4] πλέον είναι το auto-bullet του FormCheckBoxes (U+2610, Minion Pro) — όχι anchored object. Το Checkbox object style μένει στο template αλλά δεν χρησιμοποιείται από τον converter.

9. Επανάληψη σε επίπεδο βιβλίου

ΔΕΝ είναι κανόνας parse. Ορίζεται στο InDesign/Book: π.χ. CHAPTER_15 = 1 ταξίδι × 5· CHAPTER_12 ΜΕΡΟΣ Α = προτροπές × 5· CHAPTER_03 = template spread ηπείρων.

Translation QA

Moodbook — Translation QA (EN / FR / DE)

Verify a Cowork translation of the book without reading the target language, and get one report per language listing every suggested correction — ready to paste back into Cowork.

It checks three things:

1. Structural (round-trip safety): line count, # comment lines byte-identical, blank-line alignment, leading/trailing whitespace preserved, <?…?> InDesign codes, deletions kept empty, [id] order. These would break re-injection into InDesign.
2. Mechanical (deterministic, no language needed): missing/empty/added segments, text left untranslated, leftover Greek letters, missing numbers/prices/dates, suspicious length differences, inconsistent term translations, and the glossary.
3. LanguageTool (grammar / spelling / French & German typography) via a local LanguageTool server. Surface correctness only — it does not judge meaning; the meaning check is the bilingual review Claude/the Cowork skill does separately.

The three layers are complementary: structural stops broken re-injection, mechanical + LanguageTool catch omissions, numbers, grammar, agreement and typography. A native human proofread of the final laid-out PDF is still the gold standard for a printed book.

The Cowork side — the moodbook-translation-engine skill

The translating itself happens in Cowork, driven by a skill packaged at ..\moodbook-translation-engine\ (zip: ..\moodbook-translation-engine.zip). It does the transcreation (keeps the Moodbook voice), honours the [id] line-lock, consults this glossary.csv, runs a preflight (scripts/verify_roundtrip.py — the same structural checks as layer 1, but before the file leaves Cowork), and returns the translated .md plus a bilingual meaning review (the part LanguageTool can't do).

Upload it once: in Cowork, add the skill from moodbook-translation-engine.zip. Then hand it a chapter and say e.g. "translate to French" — it writes a file that drops straight into TARGET_FR/. So: Cowork (skill) = translate + voice + preflight + meaning review; this folder (PC) = round-trip + the authoritative QA above. The preflight and the structural layer overlap on purpose — one gate before hand-off, one after.

Folder layout

TRANSLATION_QA/
├── qa_config.json         ← paths, languages, LanguageTool settings
├── glossary.csv           ← optional key terms: el,en,fr,de
├── Run_Translation_QA.bat ← run the QA
├── Start_LanguageTool.bat ← start the local LanguageTool server
├── FILES/translation_qa.py
├── TARGET_EN/  TARGET_FR/  TARGET_DE/   ← put the translated .md here
└── REPORTS/               ← REPORT_<lang>.md (+ .json) land here

The Greek source is read from the round-trip MD (../IDML_ROUNDTRIP/MD_WORK by default — change source_dir in qa_config.json if needed). Translated files must have the same filename and keep the same [id] lines as the source.

LanguageTool (the grammar/typography layer) — ALREADY INSTALLED

A portable Java 21 + LanguageTool 6.6 are bundled under _tools\ (no system install, no admin). Nothing to set up. To use the grammar layer, just double-click Start_LanguageTool.bat and leave its window open while you run the QA.

If the LanguageTool window is closed, the QA still runs — it just does the mechanical checks and notes that LanguageTool was skipped.

How to run

1. Translate in Cowork → save each chapter as TARGET_FR\<name>.md (same name + [id]s as the Greek source). Same for TARGET_EN\, TARGET_DE\.
2. (For grammar) double-click Start_LanguageTool.bat and leave the window open.
3. Double-click Run_Translation_QA.bat (or Run_Translation_QA.bat fr de for a subset).
4. Open REPORTS\REPORT_<lang>.md → hand it to Cowork to apply the fixes.

Each report is grouped by chapter, sorted most-severe first, and every item is tied to its [id] so Cowork edits the exact paragraph (and the fix round-trips straight back into the IDML).

glossary.csv — extend it for every book

The glossary drives the consistency + key-term checks. Format (the "specific format" for input everywhere in this tool):

el,en,fr,de[,es,it,...]
Προορισμός,Destination,Destination,Reiseziel
Αναχώρηση,Departure,Départ,Abreise

• First column must be el (the Greek key). Every other column is a language code — add as many as you want (es, it, pt…); the QA and the tools read whatever columns exist.
• UTF-8, header row required. Empty cell = skip that language for that term.
• Uses: proper nouns that must stay identical (Moodbook, Trikala → same in every column), and recurring labels you want translated one fixed way (planner fields, product names). The QA flags any segment where the Greek term appears but the expected translation is missing.

Ships pre-filled with the common travel-planner labels (Destination/Départ/Reiseziel, …). Adjust any you disagree with — the glossary is your source of truth.

Growing the glossary (new labels, new books)

Two helper commands (FILES\glossary_tool.py), also wired as .bat:

1) EXTRACT candidate terms from a book — Glossary_Extract.bat (or pass another book's MD folder). Mines the short, recurring Greek labels and writes glossary_candidates.csv (el,freq,en,fr,de, most frequent first, known terms pre-filled):

Glossary_Extract.bat                              (this book: ..\IDML_ROUNDTRIP\MD_WORK)
Glossary_Extract.bat "D:\...\OtherBook\MD_WORK"   (another book)

2) FILL the language columns in glossary_candidates.csv (in Cowork), then MERGE — Glossary_Merge.bat (drag the filled CSV onto it, or pass its path):

Glossary_Merge.bat glossary_candidates.csv

Merge adds new terms, updates existing ones only where a cell is filled, and absorbs new language columns — so one master glossary.csv grows across all your books and languages.

What the severities mean

• HIGH — almost certainly wrong: missing/empty segment, untranslated text, a dropped number/price/date, a missing chapter file.
• MEDIUM — likely: leftover Greek, glossary term not used.
• GRAMMAR — a LanguageTool grammar/spelling/typography hit (with suggestion).
• LOW — worth a glance: length anomaly, an extra number.

Notes / limits

• LanguageTool judges surface correctness, not meaning — pair it with the bilingual meaning review.
• Numbers with different decimal/thousands separators are normalised before comparing, so 1.300 and 1,300 are treated as the same number.
• German runs longer than Greek/English, so the length-anomaly threshold is deliberately lenient; tune length_ratio_flag in qa_config.json if you see noise.

Content QA

Moodbook — Greek Content QA

A second-pass quality net for original Greek prose you generate for the books (not translation — that's TRANSLATION_QA). It exists because a single generation pass can't reliably apply 3 blacklists + 26 checkpoints + 35 tags every time. This tool enforces the mechanizable ~40% deterministically, so the report becomes a concrete worklist ("resolve these N items") instead of "remember everything".

The Greek writing skills stay the source of truth (greek-language-rules, brand-voice, house-style, writing-engine, editing-engine). This tool does not replace them — it enforces the checkable subset and frees the LLM/you to spend judgement on the rest.

What it checks (three layers → one report per chapter)

1. Language rules (deterministic, from the skill): blacklist verbs/words (flag-then-test), Formal Register, unnatural collocations, inline dash, comma-before-«και», tonos-on-caps, evergreen (prices/dates in body), word repetition.
2. Spelling — portable Hunspell el_GR (via spylls, bundled under _tools\). Catches real misspellings LanguageTool's Greek cannot. Proper nouns/endemic terms → names.txt.
3. Sameness (cross-chapter): repeated paragraph openers / 4-word tics across the whole corpus (template furniture excluded) → REPORT_sameness.md. Fights structural monotony.

What it does not do: judge naturalness, flow, clause-accretion, "earned warmth", "is it emotionally true". Those are not mechanizable — they stay with the LLM editorial pass (below) and a human final read for print.

Folder layout

CONTENT_QA/
├── README.md
├── Run_Content_QA.bat          ← run the linter on DRAFTS\*.md
├── content_rules.csv           ← GENERATED from the skill (blacklists/collocations/keeps)
├── style_sheet.csv             ← consistency registry (one chosen form per term) — you grow it
├── used_moves.csv              ← ledger of openers/themes per chapter (anti-repetition)
├── names.txt                   ← Hunspell allowlist (proper nouns / endemic terms)
├── skills-source/
│   └── greek-language-rules.md ← copy of the skill; the rules' single source of truth
├── FILES/
│   ├── build_rules.py          ← greek-language-rules.md  →  content_rules.csv
│   ├── greek_lint.py           ← the linter + spelling + sameness
│   └── build_ledger.py         ← DRAFTS  →  used_moves.csv
├── _tools/hunspell_el/         ← portable Hunspell el_GR dictionary (el_GR.aff/.dic)
├── DRAFTS/                     ← put the chapters to check here (*.md)
└── REPORTS/                    ← REPORT_<chapter>.md  +  REPORT_sameness.md

content_rules.csv is generated — never hand-edit it. Edit the skill (skills-source/greek-language-rules.md, or re-copy your latest .skill) and regenerate.

How to run

1. (once, and after editing the skill) regenerate the rules: py FILES\build_rules.py
2. Put drafts in DRAFTS\ (round-trip [id] format or plain prose).
3. Double-click Run_Content_QA.bat (or Run_Content_QA.bat WELCOME_paged). → REPORTS\REPORT_<chapter>.md + REPORTS\REPORT_sameness.md.
4. Update the anti-repetition ledger: py FILES\build_ledger.py → used_moves.csv.

Flags: --nospell (skip Hunspell, faster) · --cognates (enable the noisy cognate heuristic).

Reading the report

Grouped by chapter, each finding = tag + anchor [id] + snippet + suggestion, e.g.:

- **[Verb Semantic Transfer]** `u416#2` — Λέξη προς έλεγχο: «Κρατάς» (ρίζα «κρατώ»)
    - «Κρατάς στα χέρια σου ένα Moodbook.…»
    - ➜ «φρόντισε αυτή που τον φέρνει στον κόσμο»
    - ℹ flag-then-test: OK αν είναι γειωμένη εικόνα· λάθος αν είναι calque.

• flag-then-test: blacklist verbs (κρατώ/βλέπω/χτίζω…) are suspicions, not bans — the tool flags, the editor judges (a grounded image is fine; a bare calque is not).
• False positives are expected and by design (e.g. noun «Κρατήσεις» matched as verb κρατώ; a dict gap on a compound). The framing is "flag → judge"; add real proper nouns to names.txt.

The LLM editorial pass — the non-mechanizable half (do NOT skip)

The linter is only ~40% of the job. The rest — naturalness, flow, clause-accretion, earned warmth — needs a separate, focused editorial pass, because generating and self-checking in one pass produces stiff, half-checked prose. Run it as its own step, ideally with a fresh eye (new context/model), giving it the linter report as the starting worklist:

1. Generate (Cowork, moodbook-writing-engine): brief → generate→audit→rewrite → draft.
2. Save the draft to DRAFTS\ and run this tool → the linter report.
3. Editorial pass (Cowork, moodbook-editing-engine): read the whole chapter as a harsh editor against the full skill, resolving the linter's flagged items AND the flow/ naturalness the tool can't see. Discipline (editing-engine §3): silent-fix objective errors; query anything touching meaning/facts/voice; and for a naturalness problem re-generate the sentence fresh — do not patch it (patching stitches a fix into a flawed line; re-flowing reads natural).
4. Re-run this tool → target: zero mechanical findings. Repeat until clean.
5. For a printed book, a human native read of the typeset proof remains the ceiling.

Split the checklist into focused sub-passes (one concern each) rather than one omni-pass — that's how the deep rules actually get applied.

The registries (growable data, like the translation glossary)

• content_rules.csv — generated; the blacklist/keep data. Grow by editing the skill.
• style_sheet.csv — el,chosen,note: one fixed form per recurring term (ΑΤΜ vs ATM, transliterations, «ΚΑΛΟ ΝΑ ΞΕΡΕΙΣ» template). Manual; enforce consistency across chapters.
• used_moves.csv — per-chapter opener/closing/themes (+ devices/images for you/Cowork to annotate). Cowork reads it before writing a new chapter to pick different moves.
• names.txt — Hunspell allowlist; add a line whenever a correct word is flagged [Spelling].

Honest scope

Mechanical + spelling + sameness are enforced 100% every run. Naturalness/flow/emotion are not — they need the editorial pass and, for print, a human. Cognate/κακοφωνία detection is a heuristic (off by default) because it needs morphology. The value here is that the report turns "remember to apply every rule" into a short, concrete list — and never forgets the mechanical part.

Design system

Moodbook Design System

Visual language for all Moodbook marketing assets — ad creative (Meta, Google), social, lifecycle/email, website art direction, and product-cover artwork. Greek-first brand (moodbook.gr), handmade premium journals, made in Trikala.

Brand colors: navy #193150 + amber #F8990F (from the logo), on a neutral #F1F1F1 digital canvas. Full reasoning layer in DESIGN_SYSTEM.md.

This package turns the spec into code: the two JSON files under tokens/ are the single source of truth, and everything else (CSS variables, the Tailwind theme, the per-format canvas templates, and the rendered exports) is generated from them. Nothing hard-codes a hex, font, or dimension.

Folder layout

moodbook-design-system/
├── README.md                      ← this file (build + run instructions)
├── DESIGN_SYSTEM.md               ← full spec / reasoning layer
├── package.json
├── tailwind.config.js             ← GENERATED · Tailwind theme extension
├── tokens/                        ← SOURCE OF TRUTH (edit only here)
│   ├── design-tokens.json         ← DTCG: core primitives + semantic aliases
│   └── asset-specs.json           ← formats, dimensions, safe areas, print/cover, logo map
├── assets/
│   └── logo/
│       ├── moodbook-logo-color.png          ← supplied
│       ├── moodbook-logo-mono-black.png      ← GENERATED (brand-navy mono, placeholder)
│       └── moodbook-logo-reversed-white.png  ← GENERATED (placeholder)
├── scripts/
│   ├── build-tokens.mjs           ← Style Dictionary v4 → tokens.css + tailwind.config.js + tokens.json
│   ├── make-logo-variants.mjs     ← derive mono/reversed logos from the color PNG
│   ├── build-templates.mjs        ← per-format HTML canvases from asset-specs.json
│   ├── build-preview.mjs          ← preview index (all templates on one page)
│   └── export.mjs                 ← render templates → PNG/WebP/JPG (digital path)
└── build/                         ← GENERATED (safe to delete & rebuild)
    ├── tokens.css                 ← CSS custom properties (the layer CSS consumes)
    ├── tokens.json                ← flat resolved token map (used by the scripts)
    ├── index.html                 ← preview of all templates
    ├── templates/                 ← one HTML canvas per format + manifest.json
    └── exports/                   ← rendered images (after `npm run export`)

Convention: tokens/ + assets/ + scripts/ are source; build/ and tailwind.config.js are regenerated. Edit the JSON, then rebuild — never hand-edit generated files.

The Original Sin series theme (a mode layered on the base) lives under branches/original-sin/ — its ORIGINAL_SIN.md, AD_POLICY.md (per-title × per-platform paid-ad ruling), and tokens/original-sin-tokens.json.

Authoring source → git (the "Claude Design" connection)

The canonical spec + tokens are authored in a working folder outside this repo (Claude/design_system in the master data base). This repo is their git home. Pull the latest authored source into their tracked locations here with one command, then rebuild + commit:

npm run sync:source     # copies DESIGN_SYSTEM.md, design-tokens.json, ORIGINAL_SIN.md,
                        # original-sin-tokens.json, AD_POLICY.md → their tracked paths
npm run build && npm run build:original-sin:all
git add -A && git commit

Override the source folder with MOODBOOK_DS_SOURCE=/path npm run sync:source. The sync is idempotent (only writes files whose bytes changed) and never deletes.

Install

Requires Node ≥ 18 (developed on Node 24). The export step uses Puppeteer, which downloads a headless Chromium on install.

cd moodbook-design-system
npm install

Build (regenerate everything from the tokens)

npm run build

That runs, in order:

Run any step on its own with npm run build:tokens, build:logo, etc.

Preview (visual review)

Serve build/ over HTTP (the templates load tokens.css via relative paths) and open the index:

npx serve build           # or: python -m http.server 8755 --directory build
# open http://localhost:8755/index.html

Each card embeds the real template in a scaled iframe. Toggle the safe-area guide with the checkbox inside each canvas (also visible in the standalone build/templates/<key>.html).

Export (render to images)

npm run export                                   # all formats → build/exports/*.png (sRGB/72dpi)
node scripts/export.mjs --only meta_feed_portrait,web_hero_desktop
node scripts/export.mjs --format webp --quality 82
node scripts/export.mjs --scale 2               # 2× raster (still sRGB digital)
node scripts/export.mjs --print                 # prints the CMYK/print path, renders nothing

The safe-area guide and toolbar are hidden automatically in exports.

Using the tokens in code

CSS — import the variables and reference them:

@import "tokens.css";
.cta { background: var(--color-cta-primary-bg); color: var(--color-cta-primary-text);
       border-radius: var(--radius-md); font-family: var(--font-brand); }
.cover-title { font-family: var(--font-title); }  /* per-concept slot, see below */

Tailwind — extend from the generated theme:

// your tailwind.config.js
import moodbook from "./moodbook-design-system/tailwind.config.js";
export default { presets: [moodbook], content: [/* … */] };
// → bg-cta-primary-bg, text-text-primary, font-brand, rounded-md, shadow-soft, …

The title font is a slot, not a fixed font (§4.3)

There is no single Moodbook title font — each book/campaign title is set in a face chosen to express that concept. So --font-title (and Tailwind font-title) is a slot that defaults to the brand sans and is overridden per concept:

.wedding-cover { --font-title: "GFS Didot", serif; }   /* elegant / keepsake */
.wtf-cover     { --font-title: "Anton", sans-serif; }  /* bold / irreverent */

The standing brand sans is --font-brand (Commissioner — clean Google Fonts, full Greek). See DESIGN_SYSTEM.md §4 for the per-concept selection method and the Greek display shortlist. Per-concept title faces are print/cover only; on the web, titles fall back to --font-brand.

Print (CMYK / 300dpi / 3mm bleed) — intentionally NOT auto-rendered

Chromium renders sRGB only, so the export script implements the digital path correctly and does not fake CMYK. To produce print-ready files:

1. Author print layouts in InDesign/IDML (the brand already runs an IDML pipeline), or build print PDFs with a CMYK-aware renderer.
2. Flatten to PDF/X-1a with an ICC profile (FOGRA/ISO Coated) via Ghostscript (-sColorConversionStrategy=CMYK -dProcessColorModel=/DeviceCMYK) or Scribus' scripter.
3. Add 3mm bleed + 5mm safety and the spine band (see tokens/asset-specs.json → covers.*). Keep critical text ≥8mm from trim; grain parallel to spine; design must read on the matte velvet-touch surface (the finish is physical only — never depict it as texture, §11).

Run node scripts/export.mjs --print to print this guidance.

Open TODO(confirm) items (from DESIGN_SYSTEM.md §15)

These are carried into the generated files as TODO(confirm) comments — current values are safe approximations; none block the build:

1. (Resolved in v1.2) Web font = Commissioner (Google Fonts, complete Greek). Cera/Neutraface are no longer used for web rendering, so there is no Neutraface-Greek risk.
2. (Resolved in v1.2) Velvet removed as a visual guideline — the snow/xanadu/sapphire product colorways were dropped; covers render as flat color. The velvet-touch finish stays only as a factual print spec in tokens/asset-specs.json.
3. Logo SVG + variants — only moodbook-logo-color.png was supplied. The mono/reversed PNGs are auto-derived placeholders (re-coloured by the color PNG's alpha). A true SVG and proper mono/reversed marks should be exported from the vector master (moodbook-logo-master.pdf, not supplied). Also confirm whether an icon-only sun submark exists for favicons/avatars.
4. --font-title — variable by design (§4.3); set per concept, not a single locked font.

v1.1.0-draft — token pipeline (Style Dictionary v4 / DTCG), Tailwind theme, per-format canvas templates with safe-area guides, and a digital render/export pipeline.

Design system — full spec

Moodbook Design System

Version: 1.1.0-draft · Owner: Stelios / moodbook.gr · Status: ready for review → Claude Code build

Visual language for every Moodbook marketing asset: ad creative (Meta, Google), social, lifecycle/email, website art direction, and Moodbook product-cover artwork. Written as a standalone knowledge base — assumes no prior knowledge of the brand.

Language convention (deliberate): structure and tokens are English (code convention). All on-brand verbal content — copy, CTAs, eyebrows, black-list — stays Greek, because it ships as-is.

Confidence convention: [confirmed] (grounded in the logo / e-shop / product), [confirmed-direction] (brand clearly leans this way), [to-confirm] (verify before ship). See §15.

v1.2 changes: web/standing font is now Commissioner (clean Google Fonts, complete Greek) — we do not enforce exotic type on the web (rendering risk); per-concept title faces are print/cover only. Removed Xanadu/Sapphire and dropped velvet as a visual/design guideline (it renders poorly) — covers are shown as flat color, the velvet-touch stays only as a factual print finish. v1.1 changes: brand colors corrected to the real logo palette navy #193150 + amber #F8990F (terracotta dropped); web canvas is the e-shop grey #F1F1F1 (cream is now product/editorial only); logo section filled from the supplied master + asset files under /assets/logo.

1. How to use this (for Claude Code)

Machine source of truth lives in /tokens: - tokens/design-tokens.json — colors, type, spacing, radius, shadow (DTCG / Style Dictionary v4; core primitives + semantic aliases). - tokens/asset-specs.json — formats, dimensions, safe areas, print/cover specs, logo file map. - assets/logo/ — vector master (PDF) + transparent PNGs (color / mono-black / reversed-white).

Build targets (§14): tokens.css, a Tailwind theme, per-format canvas templates with safe-area guides, and an export script. This .md is the reasoning layer.

2. Brand foundation

Handmade premium stationery — interactive, themed journals/planners — designed and produced in Trikala at the URBAN print shop. Born March 2020 (lockdown survival act). Flagship: Wedding Planner (1,300+ sold). Lines: motherhood, travel, themed/nostalgia ("The Original Sin", VHS), wellbeing.

Essence: μελαγχολική επανάσταση (melancholic rebellion) — analog warmth as quiet resistance to digital saturation. Premium but warm; literary but never pompous; and, per the logo, friendly and human (hand-drawn, a little playful).

Four emotional pillars (every asset serves ≥1): Νοσταλγία & Αυθεντικότητα · Συναισθηματική Αναγνώριση (every feeling valid; melancholy as creative force) · Δημιουργικότητα & Αυτογνωσία · Ήρεμη Επανάσταση (the revolution is an invitation, not an order).

Voice — "Ήρεμη Δύναμη": a wise friend, not an authority. Warm, conversational, grounded, lightly self-aware, zero corporate. Hybrid-artisan register: «εγώ» for personal/emotional, «εμείς» for production/team. Ground the "why" in lived local detail (θεσσαλικός κάμπος, ποδήλατο, πίτα μπατζίνα).

NOT: didactic, distant, melodramatic, aggressive, salesy, neon, templated.

3. Color system

The palette is the logo: a deep navy and a warm amber, set on a neutral grey digital canvas so the colorful product covers carry the color. The brand has exactly one warm accent — amber (not terracotta; that was a wrong earlier guess and is removed).

Core palette

Usage rules

• Digital canvas = site grey; brand dark = navy. Navy is text, structure, and the primary button.
• Amber is the one warm accent — used for energy/highlight/celebration and the warm CTA. Generous but not wall-to-wall; it's a sun, not a flood.
• Text on amber is navy, never white (amber is light → white fails contrast).
• Let products supply color. On the grey canvas, the featured product's own cover color is the natural secondary accent — pull it in to match the product shown; the navy+amber stay as the brand frame.
• Cream (paper) = warmth on purpose: lifestyle photography surfaces, print interiors, cover mockups — not the web background.
• Burgundy = sale/scarcity only, and soft ("Χειροποίητο, περιορισμένη παραγωγή"), never "ΤΕΛΕΥΤΑΙΑ ΕΥΚΑΙΡΙΑ!". No neon, no aggressive gradients, no gloss.

Pairings & contrast

• Primary CTA = navy fill + white text (~10:1). Warm CTA = amber fill + navy text.
• Body text AA ≥4.5:1: navy and grey-600 both pass on site/white/paper. Large display ≥3:1.
• Amber on navy reads well for accents/large type; avoid amber for small body text.

4. Typography

Three distinct roles. Getting this right is the brand's hardest recurring design task, so this section is a working method, not just a list.

4.1 Wordmark (logo) — fixed, not a font

The logo lettering is custom hand-drawn (outlined in the vector master). It is the brand signature; never re-typeset it in a font. Use the supplied logo files.

4.2 Brand type (standing text) — Commissioner [confirmed]

All standing text — headings, sub-headlines, body, UI, buttons, captions, specs, data, and titles as rendered on the web — uses a clean Google font with complete, authentic Greek:

font-family: "Commissioner", "Source Sans 3", system-ui, sans-serif;

• Commissioner is a humanist sans (Rotis-adjacent feel) with excellent Greek, variable weights and reliable rendering — chosen so the web never depends on exotic type. Served from Google Fonts.
• We deliberately don't chase typographic precision on the web (it risks looking broken). Expressive per-concept title faces live in print/cover artwork only (§4.3), embedded in InDesign; on web those titles fall back to Commissioner.
• Set headings in Commissioner SemiBold/Bold, body in Regular, eyebrows in small caps + wide tracking (ΧΕΙΡΟΠΟΙΗΤΟ ΣΤΑ ΤΡΙΚΑΛΑ).

4.3 Title type — VARIABLE per concept [by design · print/cover only]

There is no fixed Moodbook title font, and this applies to print/cover artwork — not the web (on web, titles render in Commissioner). Each book/campaign cover title is set in a face chosen to express that concept (the WTF title and the Wedding title should not feel the same), embedded in InDesign. It eats time, so the system gives you a method + a pre-vetted shortlist to pull from instead of starting cold each time.

Hard constraints for any title face: 1. Proper Greek glyphs — correct tonos, not a Latin font faking accents. 2. Commercial + embedding/print license (you're printing covers and embedding in PDF/IDML). 3. Legible at the title size on a matte cover. 4. Distinct from the brand sans (the title should sing; Commissioner carries everything else).

Concept → title-style → candidate faces (starting points — always test the actual Greek title string + check license):

Where to source fast: - Greek Font Society (GFS) — the authoritative catalog of faces designed for Greek; first stop for elegant/classic/script titles, licenses are clear. - Google Fonts, filtered by Language → Greek — for free/embeddable options; confirm the Greek subset is actually present. - A custom hand-lettered title (in the spirit of the logo) for the quirky/irreverent lines — often faster and more on-brand than hunting for a quirky Greek font that doesn't exist.

Title selection checklist (paste into each cover brief): ☐ Concept named in one word ☐ Style picked from the matrix ☐ 2–3 candidates set with the real Greek title ☐ tonos renders correctly ☐ license OK for print + embed ☐ reads on the matte cover at size ☐ distinct from Commissioner.

4.4 Scale & metrics

display-xl 64 · display-lg 48 · h1 36 · h2 28 · h3 22 · body-lg 18 · body 16 · small 14 · caption 12 (px, digital). Line-height: display 1.1, body 1.5, long-form Greek 1.65. Tracking: display -0.01em; eyebrows 0.08em.

5. Logo [confirmed]

Horizontal hand-drawn wordmark MOOΔBOOK with a sun/swirl mark over the first letter. Colors navy #193150 + amber #F8990F. Files in assets/logo/: - moodbook-logo-master.pdf — vector master (color + mono pages). - moodbook-logo-color.png, moodbook-logo-mono-black.png, moodbook-logo-reversed-white.png — transparent.

Usage: color or mono on light backgrounds; reversed-white on navy/dark or busy photos. Clear space ≥ wordmark cap-height all sides. Min size 96px digital / 18mm print. Placement quiet corner or subtle watermark — never competing with the focal point. Still useful: a true SVG export (master is PDF), and confirmation of whether an icon-only submark (the sun) exists for avatars/favicons.

6. Imagery & art direction

Authentic over staged; the analog ritual in warm light. Always: natural/golden light · hands in action (writing, flipping pages) · visible texture (70g paper grain, matte finish) · cozy companions (coffee, quiet corner) · local grounding (URBAN shop, stone houses, bicycles — sparingly) · the imperfection of the handmade. Never: stock-photo look, plastic smiles, over-staging, neon, color-distorting filters, countdown timers, dark patterns, death-adjacent imagery (use «μη φεύγουν οι αναμνήσεις στα screenshots», not morbid framing). Faces & gaze: one human focal point; authentic expression; gaze toward the product leads the viewer. On the grey canvas: product photography pops against site grey — keep backgrounds clean and let the product's cover color be the color.

7. Texture & material language

The material is paper and ink. Prefer clean edges (radius.none for editorial; radius.md 8px for buttons/chips — a nod to the friendly hand-drawn wordmark). Soft, rare shadows (shadow.soft, navy-tinted) suggesting a lifting page, never glossy drops. Dividers: hairline rule or thin band. Optional subtle paper grain on warm/paper surfaces; keep the grey web canvas flat and minimal.

8. Iconography & graphic devices

• Line style: thin, slightly hand-drawn line work in navy (echoing the logo), not flat material glyphs.
• Motifs: open book, pen nib, ribbon bookmark, single botanical line, map/continent outlines (travel), calendar grid; the sun mark from the logo as a recurring brand glyph.
• Signature device: the staccato imperative ladder at the CTA — «Πάρε ένα στυλό. Κλείσε το κινητό. Γράψε.» (climactic CTA only).
• Eyebrow/proof chips: small-caps in a pill chip («300+ νύφες», «Bestseller από το 2020»).
• Avoid 01/02/03 numbering unless genuinely sequential; ≤1–2 emoji and only where natural.

9. Layout & composition

Neuromarketing-led (~95% of buying is non-conscious). - Grid: 12-col for web/large canvases; fixed formats use safeMargin from asset-specs.json. - Spacing: 4px base (space.1…32); generous negative space (premium, low cognitive load — and it suits the minimal grey canvas). - One focal point. Eye path: faces → motion → color → text. Readable in <3s. One idea per asset. - Hierarchy: focal image → title (concept face) → one benefit (Commissioner) → single CTA → logo. - Fogg (B=MAP): serve ≥1 of Motivation (emotion + social proof), Ability (simple, few steps), Prompt (one CTA after the emotional peak). - Text overlay ≤20%. One product per ad (2–3 only in a deliberate bundle).

10. Copy on assets

Emotion first, feature second; one benefit per headline; always one specific detail (number, material, place); never generic claims. Positive framing — never open with «Δεν».

Headline banks: Emotional: «Ό,τι γράφεις, μένει δικό σου» · «Η ήσυχη γωνιά σου» · «Γράψε, μην scrollάρεις». Benefit: «244 σελίδες οργάνωσης» · «Βελούδινο εξώφυλλο» · «Premium χαρτί 70gsm». Social proof: «300+ νύφες το εμπιστεύτηκαν» · «Αξιολόγηση 4.9/5» · «Bestseller από το 2020». Soft CTA: «Δες το από κοντά» · «Ξεκίνα την οργάνωση».

Black-list: lazy anglicism calques, clichés, toxic positivity, aggressive urgency, death-adjacent loss framing, generic superlatives. Deliberate contrastive anglicism allowed only when English is the point (digital-vs-paper: «backspace», «undo», «notifications»).

Seasonal angles: Σεπ–Οκτ Wedding · Νοε–Δεκ Δώρα · Ιαν New Year · Φεβ Valentine's · Μαρ–Μάι Γιορτές γονέων · Ιουν–Αυγ Travel/Summer. Rotate creative every 4–6 weeks.

11. Asset recipes (anatomy per format)

Pull exact dimensions/safe-areas from asset-specs.json.

• Meta feed (1080×1350, 4:5) — conversion: focal photo (hands + product on grey or warm surface) top ⅔ → title (concept face on print art / Commissioner on web) → one benefit (Commissioner) → proof chip → single CTA (navy, or amber for seasonal) → logo corner. One accent beyond navy: amber or the product's cover color.
• Story/Reel (1080×1920): full-bleed; all text/logo/CTA inside safe area (top 250 / bottom 320 / sides 64). Video: Hook 0–3s → Problem 3–8s → Peak 8–20s → CTA last 3–5s. ASMR signature (page-flip, pen on paper). Subtitles always.
• Carousel (4:5, ≤10): slide 1 hook → 2–6 problem→solution → last slide single CTA. Consistent header band across slides (mere exposure).
• Google Display (300×250, 336×280, 728×90, 160×600): ruthless — product thumb + 1 headline + 1 CTA button (navy). Tiny logo, no paragraphs.
• Demand Gen (1200×628 / 1200×1200 / 960×1200): lifestyle photo-led, light text, single benefit + soft CTA.
• Cover mockup (~1200×1680, 5:7): on #F1F1F1 flat for on-shop consistency, or warm surface with hands for ads; clean product render (don't try to depict velvet texture — it renders poorly).
• Web hero (Dawn): desktop 1920×1080 and a separate mobile 1080×1350 crop (Dawn Slideshow lacks native mobile swap). High-contrast overlay text (the burgundy + offset-white "letterpress" treatment works on pale images).

12. AI image generation (covers & lifestyle)

For AI-generated Moodbook imagery (Firefly / Nano Banana / Gemini / Midjourney), use this scaffold and layer Greek text in InDesign afterward (image models mangle Greek glyphs; the title face is chosen per §4 anyway).

[Format & dimensions + spine if wraparound]
Subject: [product / scene], handmade premium journal world.
Style: warm editorial, natural golden light, analog & tactile; matte finish, visible paper grain. Reference feel: Kinfolk / Cereal — restrained, NOT corporate stock.
Palette: deep navy (#193150) + warm amber (#F8990F) as brand colors; neutral grey (#F1F1F1) or warm cream (#FBF6EC) ground; optional single product cover-color accent.
Composition: one focal point, generous negative space, reserve a clean area for the title.
Include: hands writing/flipping, paper grain, cozy companions (coffee, quiet corner).
Avoid: stock-photo look, plastic smiles, neon, glossy highlights, busy layouts, on-image text, death-adjacent imagery.
Aspect ratio: [..]. High resolution, print-ready.

Note: client/freelance covers (yearbooks, memorial books) are not Moodbook assets — per-project palettes, must not inherit these tokens.

13. Accessibility & export

• Contrast: body AA ≥4.5:1 (navy & grey-600 pass); large ≥3:1; text-on-amber = navy.
• Video: subtitles everywhere; design for muted; Peak-End.
• Alt text: every published image.
• Digital: sRGB, 72dpi, PNG/JPG/WebP; overlay ≤20%; site assets on #F1F1F1.
• Print: CMYK, 300dpi, 3mm bleed, 5mm safety, PDF/X-1a; design must read on a matte surface; grain parallel to spine.

14. Build targets for Claude Code

From /tokens + /assets: 1. tokens.css — :root custom properties from design-tokens.json (resolve aliases: --color-bg-site, --color-cta-primary-bg, --font-brand, --space-4, …). 2. tailwind.config.js — theme extension mapping the same tokens. 3. Per-format canvas templates (HTML/SVG) from asset-specs.json — correct dimensions + toggleable safe-area guide layer, wired to token vars, following §11. Embed the logo from assets/logo with the right variant per background. 4. Export script — render to the right output_profile (digital sRGB/72 vs print CMYK/300 + bleed) per format key. 5. (Optional) a Style Dictionary pipeline so JSON is the single source for CSS/Tailwind/design-tool tokens.

Never hard-code hex or dimensions in templates — reference tokens.

15. Governance — confirmed vs to-confirm

Confirmed: brand colors navy #193150 + amber #F8990F (logo); site canvas #F1F1F1; logo form/variants/files; web/standing type = Commissioner (Google Fonts, full Greek); per-concept title faces = print/cover only; A5 trim, soft-touch matte hardcover, 70/100g acid-free, 200+ pp; hardcover wrap 35×25cm @300dpi CMYK; burgundy = seasonal/sale; soft-scarcity; four pillars; hybrid-artisan voice; platform formats; copy/black-list.

To-confirm (small, non-blocking): 1. Logo SVG — export a true SVG from the vector master; confirm if an icon-only sun submark exists (favicon/avatar). 2. (Resolved) Web font — Commissioner; Cera/Neutraface no longer used on web (no Neutraface-Greek risk). · (Resolved) Title font — variable per concept, print/cover only. · (Removed) Xanadu/Sapphire and the velvet colorways. · (Removed) "Velvet" as a visual guideline — covers shown as flat color; velvet-touch stays only as a print finish. · (Resolved) Warm accent = logo amber. · (Dropped) Drive brand book. · (Deferred) Project-scoped research.

This system is ready to hand to Claude Code for the §14 build; the to-confirm items can be filled in place without restructuring.

Design system (Claude) — handoff

Handoff to Claude Code

Copy the prompt block below into Claude Code to turn this design system into code and push it to a new permanent branch.

Before you paste: - If Code runs outside this repo, prepend: "the repo is at <path or URL>". - If you'd rather skip the PR and just keep the branch, change the PR line to "push only, no PR".

ΕΡΓΟ: Μετατροπή του Moodbook Design System σε κώδικα και ανέβασμα σε νέο μόνιμο branch.

ΠΛΑΙΣΙΟ
Δουλεύεις μέσα στο repo του Moodbook. Σου έχω βάλει τον φάκελο `moodbook-design-system/`
(README.md, DESIGN_SYSTEM.md, HANDOFF_TO_CODE.md, tokens/design-tokens.json, tokens/asset-specs.json, assets/logo/*).
ΜΗΝ βασιστείς σε προηγούμενη γνώση σου για design systems ή για το brand — η γνώση σου εδώ
μπορεί να είναι παρωχημένη. ΜΟΝΗ αυθεντία είναι τα αρχεία αυτά. Διάβασέ τα ΠΛΗΡΩΣ πριν γράψεις κώδικα
(ιδίως DESIGN_SYSTEM.md §3 χρώματα, §4 τυπογραφία, §11 asset recipes, §14 build targets, §15 governance).

SINGLE SOURCE OF TRUTH
Τα δύο JSON στο tokens/ είναι η μόνη πηγή τιμών. ΠΟΤΕ μην κάνεις hard-code hex, γραμματοσειρές ή
διαστάσεις στα templates/CSS — όλα να παράγονται από τα tokens. Το `design-tokens.json` είναι DTCG
(core primitives + semantic aliases)· κατά το build ανάλυσε τα aliases `{core.color.x}` στο semantic layer.

ΖΗΤΟΥΜΕΝΑ ARTIFACTS (build targets, §14)
1. Στήσε pipeline με Style Dictionary v4 (DTCG) ώστε το JSON να παραμένει η πηγή και να ξαναπαράγονται όλα.
2. `tokens.css` — CSS custom properties από το semantic layer (π.χ. --color-bg-site, --color-cta-primary-bg,
   --font-brand, --space-4 κ.λπ.).
3. `tailwind.config.js` — theme extension που χαρτογραφεί τα ίδια tokens (colors, fontFamily, fontSize,
   spacing, borderRadius, boxShadow).
4. Per-format canvas templates (HTML/SVG) για ΚΑΘΕ format key στο asset-specs.json:
   - σωστές διαστάσεις,
   - toggleable safe-area guide layer (overlay που ανοίγει/κλείνει),
   - συνδεδεμένα με τα CSS vars (όχι σταθερές τιμές),
   - ενσωματωμένο logo από assets/logo με ΣΩΣΤΟ variant ανά φόντο (color/mono σε light, reversed-white σε
     navy/σκούρα/φωτό),
   - ακολούθησε τα recipes του §11 ανά τύπο (Meta feed/story/carousel, Google Display/Demand Gen, web hero, cover mockup).
5. Export script (Node) που κάνει render τα templates στο σωστό output_profile ανά format:
   digital = sRGB/72dpi (PNG/JPG/WebP). Για print = CMYK/300dpi + 3mm bleed: αν δεν γίνεται αξιόπιστα από
   HTML, ΜΗΝ το ψευτο-υλοποιήσεις — υλοπoίησε σωστά το digital path και ΓΡΑΨΕ καθαρά τι χρειάζεται για το print
   path (π.χ. lib/εργαλείο) ως πρόταση.
6. Ένα index/preview HTML που δείχνει όλα τα templates μαζί για γρήγορο visual review.
7. Build README με οδηγίες εγκατάστασης/τρεξίματος (install, build tokens, render/export).

ΚΑΝΟΝΕΣ ΣΥΜΠΕΡΙΦΟΡΑΣ
- Ο ΤΙΤΛΟΣ είναι μεταβλητός ανά concept (§4.3): υλοποίησέ τον ως slot/prop/CSS var (π.χ. --font-title),
  ΟΧΙ σταθερή γραμματοσειρά. Το σταθερό brand sans είναι Cera/Neutraface (--font-brand).
- Για τα to-confirm του §15 (ελληνική κάλυψη Neutraface, ακριβή velvet hex, true SVG logo) ΜΗΝ εφεύρεις τιμές:
  άφησε σχόλια `// TODO(confirm): ...` και χρησιμοποίησε τις τρέχουσες προσεγγιστικές τιμές των tokens.
- Δομή φακέλων καθαρή (π.χ. /design-system για input, /design-system/build για παραγόμενα). Πρότεινε αν έχεις
  καλύτερη σύμβαση για το repo.

GIT WORKFLOW (σημαντικό)
- Δημιούργησε ΝΕΟ ΜΟΝΙΜΟ branch με όνομα `design-system` από το τρέχον branch (μην αγγίξεις/merge στο main).
- Commit: ολόκληρο τον φάκελο `moodbook-design-system/` + όλα τα παραγόμενα artifacts.
- Μηνύματα commit περιγραφικά (Conventional Commits, π.χ. `feat(design-system): tokens, tailwind theme,
  canvas templates, export script`).
- Push το branch στο origin. Άνοιξε draft PR προς main για review, αλλά ΜΗΝ κάνεις merge και ΜΗΝ σβήσεις το branch.
- Χωρίς force-push. Αν χρειαστεί κάποια μη αναστρέψιμη ενέργεια, ρώτησέ με πρώτα.

ΑΝΑΦΟΡΑ ΣΤΟ ΤΕΛΟΣ
Δώσε μου: (α) το όνομα του branch + link του PR, (β) δέντρο των αρχείων που δημιούργησες,
(γ) λίστα με τα ανοιχτά TODO(confirm), (δ) πώς να τρέξω το build και το preview τοπικά.

Branch & merge strategy (for Code)

• The design system + any series/variant (e.g. Original Sin) live in ONE folder moodbook-design-system/. Build parent + series together on the design-system branch (or a fresh branch off main) as one body — NOT a separate long-lived parallel branch per series.
• Do NOT merge to main while draft/unbuilt. Merge only after (1) real artifacts are built & verified (tokens.css, Tailwind theme, templates, tests pass) and (2) the parent's open items are closed (fonts/Neutraface, ad-policy pass, etc.).
• Then squash-merge to main and delete the branch. Avoid long-lived branches — as a solo dev they drift and get forgotten.
• DEPLOY CAVEAT (important): this repo is the Shopify theme and auto-deploys to the LIVE theme on push to main (GitHub integration, theme #199828406604). The design system lives in moodbook-design-system/, a non-theme folder Shopify's integration ignores — so merging it to main does NOT affect the live theme rendering. Long-term tidy: move the design system + QA tooling to a separate tooling repo, so the live-theme repo carries no non-theme code and design changes don't trigger a live sync. Until then, keeping it in its own folder is acceptable.

Metaobject export guide

Metaobject & Metafield — Full Export Guide

Copy-paste GraphQL Admin API queries to export everything: every metafield definition (per owner type), every metaobject definition, every metaobject entry, and the actual metafield values on resources. Plus Bulk Operations for exporting at scale with zero pagination.

Why GraphQL and not the Admin UI: GraphQL sees everything, including DRAFT metaobjects and reference values the storefront / all_products cannot reach. (That blind spot cost us a full debugging cycle on the reviews work.)

0. How to run these

Option A — Shopify GraphiQL app (interactive, recommended)

1. Install https://shopify-graphiql-app.shopifycloud.com/login into the store.
2. When prompted for scopes, enable at least: read_products, read_metaobjects, read_metaobject_definitions, read_content, read_customers, read_orders, read_markets, read_locations, read_companies.
3. Paste a query, set the Variables panel (bottom-left) when a query uses $variables, run, copy the JSON.

Option B — curl / Admin API token (for automation + Bulk downloads)

Create a custom app (Admin → Settings → Apps → Develop apps), grant the read scopes above, install, copy the Admin API access token, then:

curl -s -X POST \
  "https://moodbook.myshopify.com/admin/api/2025-01/graphql.json" \
  -H "X-Shopify-Access-Token: shpat_XXXXXXXX" \
  -H "Content-Type: application/json" \
  -d '{"query":"{ shop { name } }"}'

Use --data-binary @query.json for big queries. Bump the API version (2025-01) as Shopify releases newer stable versions.

Pagination (every connection)

Connections return max 250 per page. Every query below includes pageInfo { hasNextPage endCursor }. To get the next page, pass the endCursor as the after: variable and repeat until hasNextPage is false.

Rate/cost

Each response has extensions.cost.throttleStatus. If you get throttled, lower first: or wait for currentlyAvailable to refill (restoreRate/sec).

1. Metaobject DEFINITIONS (the schema of every metaobject type)

query MetaobjectDefinitions($after: String) {
  metaobjectDefinitions(first: 100, after: $after) {
    edges {
      node {
        id
        name
        type
        displayNameKey
        access { admin storefront }
        capabilities {
          publishable { enabled }
          translatable { enabled }
          renderable  { enabled }
          onlineStore { enabled }
        }
        fieldDefinitions {
          key
          name
          required
          type { name }
          validations { name value }
        }
      }
    }
    pageInfo { hasNextPage endCursor }
  }
}

Variables: { "after": null } (then feed endCursor). This gives you the full list of metaobject types — you need each type value for Section 2.

2. Metaobject ENTRIES (all rows of one type, incl. DRAFT)

Run once per type (use the type values from Section 1).

query MetaobjectEntries($type: String!, $after: String) {
  metaobjects(type: $type, first: 100, after: $after) {
    edges {
      node {
        id
        handle
        displayName
        updatedAt
        capabilities { publishable { status } }   # ACTIVE vs DRAFT
        fields {
          key
          value      # references come back as GID(s) — resolve via Section 1/2
          type
        }
      }
    }
    pageInfo { hasNextPage endCursor }
  }
}

Variables: { "type": "review_item", "after": null } Repeat for product_seo_hub, brand, organization, audience_segment, use_case_scenario, keyword_taxonomy, … (every type from Section 1).

Reference fields (metaobject_reference, list.metaobject_reference, product_reference, file_reference, article_reference, …) export as GIDs in value. Cross-reference them with the corresponding entries / resources to fully resolve.

3. Metafield DEFINITIONS (per owner type)

metafieldDefinitions requires an ownerType. Run once per owner type.

query MetafieldDefinitions($ownerType: MetafieldOwnerType!, $after: String) {
  metafieldDefinitions(first: 250, ownerType: $ownerType, after: $after) {
    edges {
      node {
        namespace
        key
        name
        description
        ownerType
        type { name }
        validations { name value }
        access { admin storefront }
      }
    }
    pageInfo { hasNextPage endCursor }
  }
}

Run with each of these ownerType values (the practical set for a store):

PRODUCT, PRODUCTVARIANT, COLLECTION, CUSTOMER, ORDER, DRAFTORDER,
COMPANY, COMPANY_LOCATION, LOCATION, MARKET, BLOG, ARTICLE, PAGE, SHOP,
DISCOUNT, MEDIA_IMAGE

Example variables: { "ownerType": "PRODUCT", "after": null }

4. Metafield VALUES on resources (the actual data)

Definitions (Section 3) are the schema; the values live on each resource. Pattern (products shown; swap the connection for collections / articles / etc.):

4.1 Products + their metafields

query ProductMetafields($after: String) {
  products(first: 50, after: $after) {
    edges {
      node {
        id
        handle
        title
        metafields(first: 100) {
          edges { node { namespace key type value } }
        }
      }
    }
    pageInfo { hasNextPage endCursor }
  }
}

4.2 Variants + metafields

query VariantMetafields($after: String) {
  productVariants(first: 100, after: $after) {
    edges {
      node {
        id
        sku
        product { handle }
        metafields(first: 50) { edges { node { namespace key type value } } }
      }
    }
    pageInfo { hasNextPage endCursor }
  }
}

4.3 Collections / Articles / Pages / Shop (same shape)

# Collections
{ collections(first: 50) { edges { node { id handle title
  metafields(first: 100) { edges { node { namespace key type value } } } } }
  pageInfo { hasNextPage endCursor } } }

# Articles (Blog posts)
{ articles(first: 50) { edges { node { id handle title
  metafields(first: 100) { edges { node { namespace key type value } } } } }
  pageInfo { hasNextPage endCursor } } }

# Pages
{ pages(first: 50) { edges { node { id handle title
  metafields(first: 100) { edges { node { namespace key type value } } } } }
  pageInfo { hasNextPage endCursor } } }

# Shop-level metafields (single object, no pagination)
{ shop { name metafields(first: 250) { edges { node { namespace key type value } } } } }

4.4 Single resource by handle / id (targeted, e.g. the dummy product)

{
  product(id: "gid://shopify/Product/15784005468492") {
    title handle status
    metafields(first: 100) { edges { node { namespace key type value } } }
  }
}

5. Bulk Operations — export EVERYTHING, no pagination

For "literally everything" at scale, the Bulk Operations API runs a query server-side over the entire dataset and returns one JSONL file. Only one bulk op runs at a time per shop.

5.1 Start a bulk export (all products + all their metafields)

mutation {
  bulkOperationRunQuery(
    query: """
    {
      products {
        edges {
          node {
            id
            handle
            title
            metafields {
              edges { node { namespace key type value } }
            }
          }
        }
      }
    }
    """
  ) {
    bulkOperation { id status }
    userErrors { field message }
  }
}

Bulk rules: omit first:/after: on connections (the bulk engine paginates for you); nested connections become separate JSONL lines linked by __parentId. Keep nesting ≤ 2 levels of connections.

5.2 Poll until done

{
  currentBulkOperation {
    id
    status            # CREATED → RUNNING → COMPLETED (or FAILED)
    errorCode
    objectCount
    fileSize
    url               # download link (valid ~1 week) when COMPLETED
    partialDataUrl
  }
}

When status is COMPLETED, download url — it's a JSONL file (one JSON object per line).

5.3 Bulk export metaobject entries (per type)

Bulk can also run on a single metaobject type:

mutation {
  bulkOperationRunQuery(
    query: """
    { metaobjects(type: "review_item") { edges { node {
        id handle displayName type
        fields { key value type }
    } } } }
    """
  ) { bulkOperation { id status } userErrors { field message } }
}

Run one bulk op per metaobject type (get the type list from Section 1). Wait for each to COMPLETE (poll 5.2) before starting the next — one bulk op at a time.

6. Suggested full-export workflow

1. Section 1 → get all metaobject definitions + the list of types.
2. Section 3 → metafield definitions for each owner type (PRODUCT first).
3. Section 2 → entries for each metaobject type (or 5.3 bulk per type).
4. Section 4 / 5.1 → metafield values on products (bulk for the full catalog), then collections / articles / pages / shop as needed.
5. Save each result as <thing>.json (or .jsonl for bulk) and keep them together — the reference GIDs in one file resolve against the others.

Quick reference — what each section answers

Tip: keep a recent stable API version in the endpoint (/admin/api/2025-01/…). The GraphiQL app uses the latest automatically; for curl/bulk pin it explicitly.

Metadata schema

Schema snapshot — metafield & metaobject definitions

Version-controlled schema only snapshot of the Moodbook store's custom data definitions. Committed so the structure is visible/diffable in Git.

• Source: shopify-full-export.sh run on 2026-05-31 (store moodstaff).
• Contents: metaobject definitions + metafield definitions per owner type. These describe the shape of the data (namespaces, keys, types, validations, capabilities) — no entries, no metafield values, no PII.

Files

• metaobject-definitions.json — all 30 metaobject types + their field definitions.
• metafield-definitions-<OWNER>.json — metafield definitions per owner type (PRODUCT 35, PRODUCTVARIANT 11, ORDER 7, SHOP 7, CUSTOMER 4, ARTICLE/COLLECTION/ PAGE 1 each; others 0). Files are the raw GraphQL responses.

What is intentionally NOT here (kept local / git-ignored)

The actual data — metaobject entries (reviews, SEO hubs, …) and resource metafield values — stays out of Git because it is store data / mild PII (e.g., reviewer names). It lives only in the local shopify-export-*/ folders (git-ignored). See ../METAOBJECT_EXPORT_GUIDE.md.

Refresh

Re-run the export, then copy the definition files here:

bash shopify-full-export.sh
f=$(ls -dt shopify-export-*/ | head -1)
cp "${f}metaobject-definitions.json" "${f}"metafield-definitions-*.json schema/
git add schema/ && git commit -m "chore(schema): refresh definitions snapshot"

Related: ../METAOBJECT_IMPORT_PIPELINE_DESIGN.md proposes an authored data/ format for writing definitions/entries back to Shopify. This schema/ folder is the read-only current-state reference.

Delivery date extraction

Delivery Date Extraction

Cron-driven script that checks delivery status of fulfilled Shopify orders by polling courier APIs / tracking pages, and writes the delivery datetime back to each order as a metafield.

Used downstream to trigger review-request emails after the parcel has actually been delivered.

Couriers covered

What it writes

For every fulfilled order in the last 30 days, on each successful check, the script upserts these order metafields (namespace custom):

Important — metafield definitions: create these definitions once in Shopify Admin so they become visible in the order detail UI:

Settings → Custom data → Orders → Add definition

The script will write metafields even without definitions (they'll exist in API responses), but creating the definitions makes them appear cleanly in the order page sidebar.

Local setup

# 1. Clone, cd, create virtualenv (optional)
python -m venv .venv
.\.venv\Scripts\activate    # PowerShell on Windows

# 2. Install deps
pip install -r requirements.txt

# 3. Copy env template and fill values
cp .env.example .env
# Edit .env

# 4. First run with DRY_RUN=1 — no metafields written
python delivery_tracker.py

# 5. When happy, set DRY_RUN=0 in .env and run for real

GitHub Actions deployment

1. Create a new private GitHub repo and push this folder.
2. Repo → Settings → Secrets and variables → Actions → New repository secret, add each: - SHOPIFY_DOMAIN (e.g. moodstaff.myshopify.com) - SHOPIFY_ACCESS_TOKEN (shpat_...) - BOXNOW_CLIENT_ID - BOXNOW_CLIENT_SECRET - BOXNOW_PARTNER_ID
3. The workflow .github/workflows/track-deliveries.yml runs every 4 hours automatically. You can also trigger it manually from the Actions tab → Track Deliveries → Run workflow.

Cost

GitHub Actions free tier on private repos: 2000 min/month. This job runs ~3 minutes × 6 times/day × 30 days = ~540 min/month — well within free tier.

Phase 2 (future): trigger review email

Once custom.delivered_at is reliably populated, the email trigger can be implemented in any of these ways (out of scope for this repo):

• Shopify Flow with trigger Order metafield updated + condition delivery_status equals delivered_home or delivered_pickup, action send-email after-delay-7-days
• Klaviyo / Mailchimp customer journey listening to a metafield-update webhook
• A second scheduled job that finds orders where delivered_at is between 7 and 8 days ago and review_email_sent is unset, then sends + marks

Overview / handoff

Moodbook Ads Bot — Session Handoff Document

From session: 21-22/05/2026 (Module 0.6 Phase B/E + 0.7 + branch protection + PAT bridge) To: New chat session Date: 22/05/2026 Repo: saristidis/moodbook-ads-bot Status: Production stable, daily-report operational, ready for next phase

0. How to use this document

You are an AI marketing consultant continuing work on the Moodbook Ads Bot — a Google Ads automation system for Stelios's premium Greek e-commerce brand. This document captures:

1. Where the project stands operationally
2. What technical decisions were made in the previous session and why
3. What's next in the roadmap
4. Working patterns that have proven valuable
5. Critical constraints and invariants

Start by reading this entire document. Then verify state against main branch in the repo before suggesting any action. The repo is the source of truth — this document captures intent, but main reflects reality.

1. Quick state snapshot

Production state (live and operational)

Current economics (live values in config/economics.yaml v1.3)

• AOV (configured): €20.83 — DRIFT WARN: 30-day actual is €22.20 (+6.6%)
• Variable cost per order: €5.20 (COGS 4.70 + packaging 0.50 + 2% payment fees - 0.42 shipping profit)
• Contribution margin: €15.63 per order
• Contribution margin (safety-adjusted): €15.40 per order
• Break-even ROAS (net, math): 1.353x
• Break-even ROAS (net, safety-adjusted + 8.7% buffer): 1.47x
• Break-even ROAS (reported, safety-adjusted): 1.71x
• Monthly Moodbook overheads: €2,010 (60% allocation of shared €3,100 + €150 dedicated)
• Break-even orders/month: ~129

Active campaigns (4)

• Wedding PMax — yellow (rolling)
• Pregnancy PMax — yellow (rolling)
• Tell me Mom PMax — green (rolling, promoted from yellow after v1.3 economics)
• Grandpa(ma) PMax — yellow (rolling)

Paused campaigns (since ~01/05/2026)

• Baptism 2026 PMax
• Tell me Dad PMax
• Wedding Search

2. The user (Stelios)

Business profile (don't re-derive — these are established facts): - Sole proprietor running Urban Print (production) + Moodbook (consumer brand) under one legal entity - Premium positioning — never propose discount-based promotions - Greek e-commerce, primarily mobile traffic (~83%) - Wedding/Pregnancy/Grandparent/Parent/Baptism/Baby personalized journals - Currently 4 active campaigns, planning expansion to 19 product campaigns - AOV currently €20.83 configured / €22.20 actual (drift to recalibrate) - ~178 orders/month, mostly from paid traffic (~66%) - Returning customer rate: ~8% (acquisition economics dominate)

Technical profile: - Junior technical level - Business expert - Defers to AI on technical decisions - Retains business decision authority - Expects clear options + recommendations, not bare technical descriptions

Communication preferences: - Greek for business content (product names, copy, narratives) - English for technical documentation - Direct, no fluff - Surface findings before applying fixes - Multiple options with trade-offs > single recommendation

3. Established working patterns (DO NOT BREAK THESE)

These patterns emerged through extensive collaboration and have proven valuable. Continue them.

Pattern 1: Smoke test before parallel work

When multiple PRs would run in parallel and share infrastructure, always open one test PR first to validate the shared infrastructure end-to-end. Caught 3+ distinct bugs that would have been hard to isolate in parallel.

Pattern 2: Halt and verify before applying fix

Before executing any API call, infrastructure change, or "fix" — verify it works as expected. The previous session caught: - bypass_pull_request_allowances proposed for branch protection (would not have worked) - --repo flag missing in auto-merge.yml (would have crashed) - allow_auto_merge: false at repo level (would have blocked all auto-merges)

If you propose a technical fix, run it through verification (read API docs, check workflow body, etc.) before recommending execution.

Pattern 3: First end-to-end exercise flag

When a new contract is being exercised for the first time (new module, new API integration, new workflow), explicitly flag it as such. Triggers extra scrutiny moment, both from Claude Code and from Stelios review.

Pattern 4: Math verification before path commitment

Multiple times in previous session, I (the AI consultant) proposed math reasoning that didn't reconcile when Claude Code ran the calculations. For any non-trivial multi-step economic formula, ask Claude Code to verify the calculation before committing to a path.

Pattern 5: Don't push yaml value changes through amendments docs without confirmation

Spec amendments are methodology specifications. They should not silently shift business-relevant values (BE thresholds, zone boundaries, AOV) without explicit Stelios confirmation.

Pattern 6: AI proposes, Stelios approves, system applies

Never automatic mutations to Google Ads, Shopify, or any external system. Every change goes through: 1. AI analyzes and proposes 2. PR created with proposed changes 3. Stelios reviews 4. Stelios approves 5. System applies

This is the core safety principle. Don't propose architectures that violate it.

4. Recent critical decisions (with rationale)

Decision: Methodology A for safety margins (PR #11)

After the v1.3 shipping correction, the math for break-even ROAS didn't reconcile cleanly with the original Module 0.6 spec. We had 3 options: - A: Keep yaml as-is, document the "shipping safety on profit" wart - B: Adopt mathematically coherent Methodology, update yaml (zones shift) - C: Hybrid with two BE values

Chose Methodology A. The shipping incoherence is mathematically odd but operationally negligible (€0.08/order, 0.5% of CM). Proper shipping risk modeling deferred to future module. Zone reclassifications avoided.

Decision: PAT bridge instead of ruleset migration (22/05)

GitHub Rulesets don't allow Integration bypass actors in personal repos. We have 3 paths: - Path 1: Migrate to GitHub org (proper fix, ~half-day work) - Path 2: PAT bridge (5-min fix, bridge until org migration) - Path 3: Accept failure (operationally bad)

Chose Path 2 with PAT scoped to Contents: Read and write only on this single repo. Org migration tracked as long-term item.

Decision: 7-day rolling window for zones (Module 1 v2 amendment)

Daily zone classification was misleading due to: - Conversion lag (up to 3 days) - Customer journey latency (4+ days) - Low daily volume creating noise

Module 1 v2 will use 7-day rolling for zone classification. Daily values shown informationally without zone badge. Campaigns with <5 days of history return insufficient_data.

Decision: Sequential PRs not parallel (after first finding)

Originally planned 3 parallel PRs (Module 0.6 Phase B-D + Module 0.7 + Module 1 v2). After smoke tests surfaced 2 distinct bugs sequentially, we serialized: 1. Module 0.7 first 2. Module 0.6 Phase B+E next 3. Module 1 v2 only after 0.6 stabilizes

This was the right call.

Decision: Light pre-processing with Haiku for Module 0.8 audits

For the planned Campaign Audit Data Gatherer (Module 0.8), light pre-processing happens with Haiku (pattern matching, statistical flagging, executive summary). Strategic analysis stays with Opus (in consulting chats). Cost: ~$0.20/month for the Haiku layer.

5. The roadmap

Immediate next priority

Module 0.8 — Campaign Audit Data Gatherer (NEW, agreed in previous session, spec to be written)

Purpose: Automate the data gathering step of the bi-weekly campaign audits that Stelios currently does manually in consulting chats. Saves 1-2 hours/week.

Specifications (agreed): - Cadence: Monday and Thursday morning (06:00 EET, before Stelios wakes up) - Storage: Google Drive (folder structure: moodbook-audits/YYYY-MM-DD-audit.md) - Windows: Multi-window approach - 3-4 days (recent slice) - 14 days (stable tactical view, used for search term sample sizing) - 30 days (strategic context, used for asset performance) - Detail level: Standard to exhausting (between Sections 1-10 described below) - Pre-processing: Light Haiku-based statistical flagging - Priority: Before Module 1 v2 (provides immediate consulting workflow value)

Sections to include (1-10): 1. Performance overview (3 windows) 2. Search terms analysis (14d, top 50) 3. Asset performance (30d, with Low rating flags) 4. Audience insights (demographics, devices, geo, time-of-day) 5. Bid strategy + signals (tROAS, optimization score, recommendations) 6. Shopping feed health (PMax product groups) 7. Conversion path 8. Portfolio overview (cannibalization, budget allocation) 9. Recent changes log (from repo state) 10. Top-of-mind issues (Haiku pre-processed executive summary)

Spec document to be written: MODULE_0_8_CAMPAIGN_AUDIT_DATA.md

Short-term (next 1-2 sessions)

• AOV recalibration: €20.83 → €22.20 (drift signal already detected). Affects BE math. Careful PR with zone impact table. Should happen before Module 1 v2 to avoid moving target.
• Module 0.6 Phase C — SensitivityAnalyzer
• Module 0.6 Phase D — tools/sensitivity.py CLI tool

Medium-term

• Module 1 v2 — Profit-aware reporting + anomaly detection (full spec ready, 4-5 days)
• SQLite metrics DB
• Day-of-week Z-score with rolling fallback
• Rolling 7-day window for zones (replaces daily)
• Monthly break-even tracker with cumulative loss carry-forward (no win carry-forward)
• tROAS auto-read from Google Ads API
• Email redesign with glossary links instead of tooltips
• Sonnet narrative for anomalies
• GitHub Issues for critical anomalies
• First commit: §0.1 cleanup (rename used_in_calculations → net_to_business)

Long-term

• GitHub org migration (replaces PAT bridge, enables rulesets bypass actors)
• Module 2 — Negative Keywords automation (Haiku classifier + PR proposals)
• Module 3 — Customer Match / RFM (Data Manager API, lapsed exclusion)
• Module 4 — Custom Labels audit (retrofit)
• Module 5 — Creative Fatigue + GenAI ad copy (Sonnet copywriter)
• Module 6 — tROAS Calibration
• Module 7 — Seasonal Calendar
• Module 8 — Mobile Funnel/GA4 audit (deferred — GA4 issues)
• Module 9 — Forecast/Saturation CLI
• Module 10 — Recommendations Triage
• Future shipping risk module (proper modeling)

6. Outstanding follow-up items

7. Critical invariants (in CLAUDE.md, must be preserved)

1. Premium positioning: Never discount-based promotions
2. Greek primary, English in code/logs
3. Brand glossary: "βιβλίο" not "ημερολόγιο" for grandparent campaigns; gift framing; religious sensitivity for baptism
4. Books = 6% VAT, never 24%
5. PII hashing required (SHA-256) before persistence
6. DRY_RUN=true default, no mutations without PR
7. Cost caps are hard limits ($25/month Anthropic budget)
8. Module 0.5 conversion value invariant: ALWAYS use conversion_value_net_eur for break-even comparisons
9. Module 0.6 safety margin invariant
10. Module 0.7 5-layer validation pipeline invariants
11. Module 0.6 v1.3 shipping accounting invariant: shipping is NET PROFIT (-€0.42 cost = +€0.42 profit)

8. Recent files in the repo (key references)

Specs (specification documents/): - MODULE_0_6_SAFETY_AND_SENSITIVITY.md — original Phase A-D spec - MODULE_0_6_AMENDMENTS.md — Phase A clarifications - MODULE_0_6_V1_3_COMPAT_AMENDMENTS.md — Methodology A documentation - MODULE_0_7_AUTOMATED_VALIDATION.md — original validation pipeline spec - MODULE_0_7_AMENDMENTS.md — threshold updates + email conventions - MODULE_1_v2.md — original Module 1 v2 spec (1025 lines, comprehensive) - MODULE_1_v2_AMENDMENTS.md — rolling windows, tROAS auto-read, MTD redesign

Documentation (docs/): - economics_history.md — full audit trail of economics.yaml changes (v1.0 → v1.3) - branch_protection_via_rulesets.md — current status: PAT bridge active, awaiting org migration - SETUP.md — bootstrap instructions

Code structure: - config/economics.yaml v1.3.0 (canonical economic config) - config/glossary.yaml v1.3.0 (synced with economics) - config/settings.yaml, config/validation_thresholds.yaml - src/calculators/profitability.py — Methodology A implementation - src/validation/ — 5-layer pipeline - tools/validate_economics.py — config validation script - tools/setup_branch_protection.sh — ruleset script (dormant, ready for org migration)

9. Things NOT to do

• Don't propose architectural changes to bypass the AI-proposes-Stelios-approves principle
• Don't suggest discount-based promotional strategies
• Don't propose silent yaml value changes
• Don't skip the verification step on technical fixes
• Don't propose 3+ parallel PRs that share infrastructure (smoke test first)
• Don't bypass the validation pipeline for production changes
• Don't propose auto-pause logic for campaigns (even critical anomalies just open Issues; Stelios decides)
• Don't introduce parallel-methodology calculations in reports (Module 0.6 violated this once, fixed in Phase B+E migration)
• Don't propose actions that require Google Ads API write scope beyond what's already approved (token approval pending Basic Access)

10. Communication style for this user

• Greek for business content, brand voice
• English for technical documentation
• Direct, concise — Stelios reads on mobile while making coffee
• Multiple options with trade-offs > one prescription
• Surface findings, don't hide them
• Explicit "halt and ask" when uncertain
• Reflective when patterns are established (e.g., "this confirms Pattern X from previous work")
• Honest about own limitations (e.g., math verification, complex multi-step reasoning)

11. Day-1 task in new session

FIRST PRIORITY — AOV discrepancy investigation (added 22/05 evening)

Stelios verified actual Shopify AOV values: - Q1 2026: €20.01 - YTD 2026 (Ιαν-Μάι): €20.40 - 2025 full year: €19.74

But the daily-health-check Layer 3 reports actual AOV as €22.20. These don't reconcile. The €22.20 figure does not match any Shopify-derived view of the business.

This is likely a measurement bug, not a drift signal. Before recommending AOV recalibration:

1. Read the AOV computation code in the health check - Likely in tools/health_check.py or src/validation/layer_3_reality.py - Identify: what window, what filter, what aggregation method
2. Re-run the same query against Shopify manually to reproduce
3. Identify the discrepancy source: - Bug in the formula (e.g., including shipping/VAT incorrectly) - Different window than expected - Filter mismatch (e.g., including refunded orders) - Sample bias in the rolling window
4. Document the finding before proposing any fix

Only after the discrepancy is understood, propose either: - Fix the health_check bug (preferred if confirmed) - Update economics.yaml AOV from €20.83 to the verified actual value (likely ~€20.40, not €22.20) - Document a known difference between two valid methodologies

Stelios's observation: AOV should not be static forever. A proper quarterly recalibration mechanism makes sense, but only after the measurement is trusted.

THEN — alternative paths after the AOV investigation is closed:

When Stelios opens the new session, expect one of:

Option A: He asks for the Module 0.8 spec (Campaign Audit Data Gatherer) → Write MODULE_0_8_CAMPAIGN_AUDIT_DATA.md based on §5 above

Option B: He asks about Module 0.6 Phase C-D → Resume Phase C-D from original Module 0.6 spec

Option C: He asks about something else entirely → Listen first, don't assume direction

In all cases, start by acknowledging that previous session ended with daily-report restoration via PAT bridge AND with an open AOV measurement discrepancy that should be the first investigation.

12. Quick verification commands for new session start

# Verify current main state
cd moodbook-ads-bot && git pull origin main
git log -5 --oneline

# Verify economics state
python tools/validate_economics.py

# Check current daily-report state
ls -la state/snapshots/ | tail -10
ls -la reports/daily/ | tail -10

# Verify branch protection still active
gh api repos/saristidis/moodbook-ads-bot/branches/main/protection | jq .required_status_checks

Expected: pass on all checks, recent files dated 22/05/2026 or later.

13. Open question worth discussing early

The AOV measurement discrepancy (configured €20.83 vs health check €22.20 vs verified Shopify Q1 €20.01, YTD €20.40) is the highest-priority investigation for the new session. The €22.20 figure surfaced by Layer 3 of the daily-health-check does not reconcile with Shopify's own reporting.

This is potentially a measurement bug, not a drift signal. Section 11 above documents the investigation steps. Do not propose AOV recalibration before understanding why the measurements diverge.

Once resolved, Stelios's broader observation stands: AOV should not be static forever, and a proper quarterly recalibration PR mechanism makes sense — but only after the measurement layer is trusted.

End of handoff document.

Continuation context: The previous session ended at ~13:25 UTC on 22/05/2026 with the PAT bridge PR (#14) successfully merged and the daily-report manually triggered to confirm operational restoration. The next email (auto-scheduled for 07:30 EET on 23/05/2026) should arrive normally.

The next session should pick up from there, with main at commit d39e2cf (daily report 2026-05-22) or later.

Supplemental feed — tech spec

Tech Spec: Google Merchant Center Supplemental Feed via Shopify Liquid

Project: Moodbook (moodbook.gr) Theme: Shopify Dawn 15.4.1 Target Implementer: Claude Code Date: Wednesday, May 13, 2026 Estimated Effort: 2-3 hours Status: Ready for implementation

1. Objective & Business Context

What we're building

A public XML endpoint on the Moodbook Shopify store that exports product custom labels (stored as Shopify metafields) in Google Merchant Center supplemental feed format. The Merchant Center will fetch this URL daily and merge the custom_label_0 values with the primary product feed.

Why we're building this

The store has 6 active Google Ads Performance Max campaigns sharing the same Merchant Center product feed. Without custom labels, the Google algorithm cannibalizes between campaigns, serving the same product through multiple campaigns and inflating CPA. Custom labels enable strict listing group filters per campaign, eliminating cannibalism.

Shopify's native Google channel does not expose custom_label_0 through custom_label_4 fields in its primary feed. A supplemental feed is the standard workaround. The Merchant Center merges supplemental data with primary feed entries based on matching id field.

Background context the implementer should know

• The store sells premium handmade journals/diaries in Greek market
• Average product price: €19.70, average COGS: €4.70
• Break-even ROAS: 1.31x — every wasted impression costs real margin
• 6 PMax campaigns: Wedding, Pregnancy, Tell Me Mom, Grandparent, Baptism, plus one expansion campaign
• The supplemental feed must be reliable — if it fails silently, custom labels disappear from GMC and listing groups break

2. Acceptance Criteria

The implementation is complete when:

1. ✅ A public URL https://moodbook.gr/pages/google-supplemental-feed returns valid XML
2. ✅ XML output is clean — no theme chrome (no <html>, <head>, <body>, no header/footer markup)
3. ✅ Response Content-Type is appropriate (application/xml or text/xml)
4. ✅ Every product variant in the Shopify catalog appears as one <item> entry
5. ✅ Each <item> contains a <g:id> matching format: shopify_GR_{product.id}_{variant.id}
6. ✅ Each <item> contains <g:custom_label_0> with the value from product.metafields.custom.custom_label_0
7. ✅ Each <item> contains <g:custom_label_1> only if the metafield has a non-blank value (conditional output)
8. ✅ Items without custom_label_0 metafield are excluded from output (don't pollute feed with empty entries)
9. ✅ XML validates with no parser errors (test via xmllint or online XML validator)
10. ✅ Implementation works on Dawn 15.4.1 with no modifications to core theme files (only new files added)
11. ✅ The page is publicly accessible without authentication
12. ✅ The page is excluded from search engine indexing (robots meta or X-Robots-Tag header)

3. File Structure & Implementation Approach

Files to create (3 new files, zero modifications to existing files)

File 1: templates/page.google-feed.liquid

This is the Shopify template that gets assigned to the page in admin. It assigns a custom layout and renders the feed snippet.

{% layout 'feed' %}
{%- render 'google-supplemental-feed' -%}

File 2: layout/feed.liquid

This is a minimal layout with zero theme chrome. It only renders the XML content with proper XML declaration.

<?xml version="1.0" encoding="UTF-8"?>
{{- content_for_layout -}}

Critical: No <html>, no <head>, no <body>, no liquid theme tags. Just the XML declaration and the layout content.

File 3: snippets/google-supplemental-feed.liquid

This is the main logic — iterates products, generates XML items. See Section 4 for detailed spec.

Why this 3-file architecture

• Separation of concerns: template assigns layout, layout provides minimal wrapper, snippet contains logic
• Update safety: core Dawn files untouched → theme updates won't break this
• Reusability: if we add other feeds (Bing, Facebook), the layout can be reused

Files to NOT modify

• layout/theme.liquid — keep untouched
• templates/page.liquid — keep untouched (this is the default page template)
• Any sections/*.liquid — keep untouched
• Any other core Dawn files

4. Liquid Logic Specification

High-level pseudocode

For each product in the store catalog:
    Skip if product.metafields.custom.custom_label_0 is blank

    For each variant of the product:
        Emit <item> with:
            <g:id> = "shopify_GR_{product.id}_{variant.id}"
            <g:custom_label_0> = product.metafields.custom.custom_label_0

            If product.metafields.custom.custom_label_1 is not blank:
                <g:custom_label_1> = product.metafields.custom.custom_label_1

Implementation details

Iteration source: Use collections.all.products to get all products in the catalog.

Pagination: Use {% paginate ... by 5000 %} to handle large catalogs in a single response. The store has 57 products × ~6 variants average = ~340 entries, well within limits. If pagination is needed (>5000 items), implement it transparently in a single response.

Metafield access: - Use product.metafields.custom.custom_label_0 (not custom_label_0.value) - For blank checks, use != blank (Shopify's idiomatic check) - Metafields with Storefront access: enabled are accessible from Liquid (already configured)

ID format: - Format: shopify_GR_{product.id}_{variant.id} - Example: shopify_GR_9166915666252_48682551738700 - This format must match exactly the IDs in the primary Shopify Google feed (verified from Merchant Center export)

Whitespace handling: - Use {%- and -%} (with hyphens) on liquid tags inside the XML to minimize whitespace - Don't worry about pretty formatting of XML — Merchant Center doesn't care - DO ensure no leading whitespace before <?xml version="1.0"...?> declaration

Character escaping: - Custom label values are controlled (we set them: WEDDING, PREGNANCY, etc.) so no escaping issues expected - BUT defensively wrap values in CDATA or use Liquid's xml_escape filter to be safe: liquid <g:custom_label_0>{{ product.metafields.custom.custom_label_0 | xml_escape }}</g:custom_label_0>

Empty product handling: If a product has zero variants (rare but possible), the inner loop produces nothing. The outer "skip if no label" check handles this gracefully.

Edge cases to handle

5. XML Output Format Specification

Required structure

<?xml version="1.0" encoding="UTF-8"?>
<rss xmlns:g="http://base.google.com/ns/1.0" version="2.0">
  <channel>
    <title>Moodbook Supplemental Feed</title>
    <link>https://moodbook.gr</link>
    <description>Custom labels supplemental feed for Google Merchant Center</description>
    <item>
      <g:id>shopify_GR_9166915666252_48682551738700</g:id>
      <g:custom_label_0>OUR_WEDDING</g:custom_label_0>
    </item>
    <item>
      <g:id>shopify_GR_9166915666252_48682551705932</g:id>
      <g:custom_label_0>OUR_WEDDING</g:custom_label_0>
    </item>
    <!-- ... more items ... -->
  </channel>
</rss>

Element-by-element spec

What to NOT include

• Do NOT include <g:title>, <g:price>, <g:availability>, <g:link>, <g:image_link>, etc. — those come from primary feed
• Do NOT include <g:custom_label_2> through <g:custom_label_4> — we decided not to use these
• Do NOT include any non-g: namespace fields

6. Shopify Admin Setup (after Liquid is deployed)

Step 6.1: Create the public page

After the 3 files are deployed to the theme:

1. Shopify Admin → Online Store → Pages → Add page
2. Title: Google Supplemental Feed
3. Content: leave blank
4. Visibility: Visible
5. Search engine listing: click Edit website SEO → set meta description to noindex (or rely on robots.txt — see Section 7)
6. Theme template: select page.google-feed from the dropdown
7. Save

Resulting URL: https://moodbook.gr/pages/google-supplemental-feed

Step 6.2: Search engine exclusion

To prevent indexing, choose one of two approaches:

Approach A — robots.txt addition:

Shopify allows editing robots.txt.liquid. Add a Disallow rule:

{%- if request.path == '/pages/google-supplemental-feed' -%}
User-agent: *
Disallow: /pages/google-supplemental-feed
{%- endif -%}

Approach B — X-Robots-Tag (preferred, but harder in Shopify):

Shopify does not allow custom HTTP headers from Liquid templates. Approach A is the practical option.

Recommendation: Use Approach A. Edit templates/robots.txt.liquid (create if doesn't exist) following Shopify's robots.txt customization guide.

7. Testing & Validation Protocol

Test 7.1: Browser test (immediate after deployment)

1. Open https://moodbook.gr/pages/google-supplemental-feed in a browser
2. Expected: Browser displays raw XML (or downloads it, depending on browser settings)
3. Failure modes to check: - HTML page with theme chrome → layout file not applied correctly - 404 → page template not assigned in admin - 500 error → Liquid syntax error in snippet

Test 7.2: XML validity check

Run the output through XML validator:

curl -s https://moodbook.gr/pages/google-supplemental-feed | xmllint --noout -

Expected: No output (xmllint outputs nothing on success). Failure modes: Parser errors indicate malformed XML.

Test 7.3: Content sanity check

curl -s https://moodbook.gr/pages/google-supplemental-feed | grep -c '<item>'

Expected: Returns approximately 200-340 (depending on actual variant count across the catalog). If <50, something is wrong. If 0, no products are being iterated.

Test 7.4: Sample item inspection

Manually inspect 3-5 items in the output:

curl -s https://moodbook.gr/pages/google-supplemental-feed | head -50

For each sample item, verify: - <g:id> follows the shopify_GR_{product_id}_{variant_id} format - <g:custom_label_0> contains one of the known values (OUR_WEDDING, PREGNANCY_JOURNAL, etc.) - No empty tags or weird character encoding

Test 7.5: Specific product verification

Cross-reference with known products:

Use grep to find the product's ID and verify its label:

curl -s https://moodbook.gr/pages/google-supplemental-feed | grep -A 1 "9166915666252" | head -5

(The number 9166915666252 is the product ID for "Ο γάμος μας" — should return items with OUR_WEDDING label)

Test 7.6: Performance check

time curl -s -o /dev/null https://moodbook.gr/pages/google-supplemental-feed

Expected: Response time under 3 seconds. If slower, consider caching strategies (but unlikely needed for this catalog size).

Test 7.7: Robots.txt verification

curl -s https://moodbook.gr/robots.txt | grep google-supplemental

Expected: Returns the Disallow line, confirming exclusion from search engines.

8. Google Merchant Center Setup

Note: This section is what Stelios (the user) does manually after the Liquid implementation is verified working. Document these steps clearly for handoff.

Step 8.1: Create supplemental data source

1. Merchant Center → Products → Data sources → Add data source
2. Select "Supplemental data source" (NOT "Primary")
3. Name: Moodbook Custom Labels Supplemental
4. Connect to: choose existing primary feed (Shopify GR feed)

Step 8.2: Set fetch schedule

1. Method: Scheduled fetch
2. File URL: https://moodbook.gr/pages/google-supplemental-feed
3. Fetch frequency: Daily
4. Fetch time: 04:00 UTC (06:00 EET — off-peak hours)
5. Country: Greece
6. Language: Greek

Step 8.3: Initial fetch

After saving, click "Fetch now" to trigger immediate first fetch. Wait 5-30 minutes for processing.

Step 8.4: Verify merge

1. Merchant Center → Products → All products
2. Click on a known product (e.g., "Ο γάμος μας" variant)
3. In the product details panel, look for Custom labels section
4. Verify custom_label_0 shows the expected value

9. Monitoring & Reliability (Critical)

Why monitoring matters

If the supplemental feed silently breaks (Liquid error, theme change, etc.), the custom labels disappear from GMC within 24-48 hours. PMax listing groups become non-functional. We won't know until campaign performance degrades — potentially days later.

UptimeRobot setup

The user (Stelios) should configure these monitors after deployment:

Monitor 1: Feed availability + content check - Type: HTTP(s) with Keyword - URL: https://moodbook.gr/pages/google-supplemental-feed - Keyword: custom_label_0 - Alert condition: "Alert when keyword does NOT exist" - Check interval: 6 hours - Alert contacts: Stelios's email + ideally SMS

Monitor 2: Known label verification - Type: HTTP(s) with Keyword - URL: same - Keyword: OUR_WEDDING - Alert condition: "Alert when keyword does NOT exist" - Check interval: 6 hours - Alert contacts: same

Monitor 3 (optional): Item count sanity - Use a different monitoring service that supports response-size checks (Cronitor free tier) - Alert if response size deviates ±30% from baseline

These monitors give us time-to-detect under 6 hours for silent corruption.

10. Implementation Notes for Claude Code

Dawn 15.4.1 specifics

• Dawn 15 uses Online Store 2.0 architecture — sections-based theme
• Template files for pages are in templates/ directory
• Layouts in layout/ directory
• Snippets in snippets/ directory
• The default page layout is layout/theme.liquid — we deliberately bypass it with feed.liquid

Liquid syntax reminders

• Metafield access: product.metafields.custom.custom_label_0 returns the value directly (no need for .value)
• Blank check: {% if product.metafields.custom.custom_label_0 != blank %}
• Variant iteration: {% for variant in product.variants %} (always available, no need for .all)
• Product iteration via collection: {% for product in collections.all.products %} (yields all published products)
• Pagination: {% paginate collections.all.products by 5000 %} ... {% endpaginate %}

Common pitfalls

1. XML declaration on second line: If there's any whitespace or content before <?xml, the file is invalid XML. Use {%- at the start to strip preceding whitespace.

2. HTML entity encoding: Shopify Liquid sometimes auto-escapes content. For XML output with the xml_escape filter, ensure entities like & come through correctly (not double-escaped to &).

3. Caching: Shopify CDN may cache the page. After updates to the snippet, force-refresh in browser (Cmd+Shift+R) or wait 5-10 minutes for cache invalidation.

4. Theme preview vs published: Test in published theme, not preview. Page templates may behave differently in preview mode.

5. Layout assignment: The {% layout 'feed' %} directive must be the first non-whitespace content in the template file. Even comments before it may break the layout assignment.

File creation order

Recommended order to minimize errors:

1. First create snippets/google-supplemental-feed.liquid (the logic)
2. Then create layout/feed.liquid (the wrapper)
3. Finally create templates/page.google-feed.liquid (the assignment)
4. Then go to Shopify Admin and create the page with the new template

This order means each file's dependencies exist when it's referenced.

Rollback plan

If something breaks:

1. Quick disable: In Shopify Admin → Pages → Google Supplemental Feed → Visibility → Hidden. This makes the URL return 404 without removing the files.
2. GMC disable: In Merchant Center → Data sources → Moodbook Custom Labels Supplemental → Pause the fetch.
3. Full rollback: Delete the 3 created files. Theme returns to pre-implementation state.

No customer-facing impact in any rollback scenario — these files are only for Google Merchant Center consumption, not for any storefront UI.

11. Out of Scope (Do NOT Implement)

To prevent scope creep, the following are explicitly out of scope for this task:

• Variant-level metafields (we decided to use product-level only)
• custom_label_2, custom_label_3, custom_label_4 (decided not to use these)
• Performance/seasonality logic (we decided to skip seasonality)
• Multi-language feed variants (single GR feed only)
• Multi-market support (only GR market)
• Admin UI for managing labels (Shopify's native metafield UI is sufficient)
• Automatic label population based on product attributes (manual population, already complete)
• Email/Slack notifications on feed errors (handled by UptimeRobot)
• Caching layer (Shopify CDN sufficient for this scale)

12. Deliverables Checklist

By task completion, the following should exist:

• [ ] templates/page.google-feed.liquid created and committed
• [ ] layout/feed.liquid created and committed
• [ ] snippets/google-supplemental-feed.liquid created and committed
• [ ] Shopify page created at /pages/google-supplemental-feed using the new template
• [ ] Robots.txt updated to disallow /pages/google-supplemental-feed
• [ ] All 7 tests in Section 7 pass
• [ ] Documentation comment at the top of the snippet file explaining what it does and when it was created
• [ ] User (Stelios) handed off Section 8 (GMC setup) and Section 9 (UptimeRobot setup) instructions

13. Success Definition

The implementation is successful when:

1. The URL returns valid XML with ~200-340 items
2. Each item has correct <g:id> and <g:custom_label_0> values
3. GMC successfully fetches and merges the data (verifiable in product details panel within 24 hours of GMC setup)
4. UptimeRobot monitors are configured and showing green status
5. No customer-facing storefront changes
6. No degradation in Shopify store performance metrics

Appendix A: Reference Data

Known custom_label_0 values in current catalog

These are the only valid values that should appear in the output. If any other values appear, there's a data issue:

OUR_WEDDING
PREGNANCY_JOURNAL
WEDDING_GUESTBOOK
MY_BAPTISM
FIVE_MINUTES_CALENDAR
PES_MOU_PAPPOU
LIFE_PLANNER
KIDSFEELINGS
TRAVEL_PLANNER
SCHOOL_PLANNER
THE_CAT_BOOK
NEW_MOMS_JOURNAL
NEW_DADS_JOURNAL
PES_MOU_GIAGIA
TELL_ME_MOM
TELL_ME_DAD
THE_HONEYMOON
NEWBORN_DIARY
THEMATIC_NOTEBOOKS
CREATIVE_NOTES
ORIGINAL_SIN
MUGS

Catalog statistics (for QA reference)

• Total unique products: 57
• Total unique variants in GMC feed: ~114
• Total feed entries (with channel/language permutations): ~438
• Expected <item> count in supplemental feed: ~340 (product variants only, no channel duplicates)

The supplemental feed iterates Shopify variants directly, so the count will match the variant count, not the GMC entry count. GMC handles the channel/language fan-out automatically when merging.

Primary feed ID format (must match)

Format: shopify_GR_{numeric_product_id}_{numeric_variant_id}

Real examples from current primary feed: - shopify_GR_9166915666252_48682551738700 - shopify_GR_9166915666252_53429918368076 - shopify_GR_7601047666929_56840438186316 - shopify_GR_7513032098033_42087453589681

The numeric IDs are the Shopify internal IDs, accessible via Liquid as product.id and variant.id.

Appendix B: Example Final Output (excerpt)

This is what the final XML should look like for a sample of products:

<?xml version="1.0" encoding="UTF-8"?>
<rss xmlns:g="http://base.google.com/ns/1.0" version="2.0">
  <channel>
    <title>Moodbook Supplemental Feed</title>
    <link>https://moodbook.gr</link>
    <description>Custom labels supplemental feed for Google Merchant Center</description>
    <item>
      <g:id>shopify_GR_9166915666252_48682551705932</g:id>
      <g:custom_label_0>OUR_WEDDING</g:custom_label_0>
    </item>
    <item>
      <g:id>shopify_GR_9166915666252_48682551738700</g:id>
      <g:custom_label_0>OUR_WEDDING</g:custom_label_0>
    </item>
    <item>
      <g:id>shopify_GR_9166915666252_48682551771468</g:id>
      <g:custom_label_0>OUR_WEDDING</g:custom_label_0>
    </item>
    <item>
      <g:id>shopify_GR_9166915666252_52082676859212</g:id>
      <g:custom_label_0>OUR_WEDDING</g:custom_label_0>
    </item>
    <item>
      <g:id>shopify_GR_9166915666252_53429918368076</g:id>
      <g:custom_label_0>OUR_WEDDING</g:custom_label_0>
    </item>
    <item>
      <g:id>shopify_GR_9166915666252_56712431829324</g:id>
      <g:custom_label_0>OUR_WEDDING</g:custom_label_0>
    </item>
    <item>
      <g:id>shopify_GR_7601047666929_56840438186316</g:id>
      <g:custom_label_0>PREGNANCY_JOURNAL</g:custom_label_0>
    </item>
    <!-- ... and so on for all variants ... -->
  </channel>
</rss>

Note: Six entries for "Ο γάμος μας" (one per color variant), all sharing the same OUR_WEDDING label. The Merchant Center will then fan these out across the channel/language permutations automatically.

Module 1 Technical Spec_ Profit Core & Watchdog

Module 1 — Profit Core & Watchdog (Technical Specification)

Project: Moodbook Google Ads Automation Bot

Module: 01 — Profit Core & Anomaly Detection

Target Audience: Claude Code (Developer Agent)

Dependencies: Module 00 (Foundation & Authentication)

1. Overview & Architecture

This module implements the core profitability calculations (POAS) and a statistically rigorous anomaly detection system (Z-Score) to monitor Moodbook's Google Ads performance without human micro-management. It establishes a local SQLite database to retain 26 weeks of historical data required for accurate day-of-week baselining.

Data Flow:

1. Extract: Pull yesterday's spend/conversions (Google Ads API) and COGS/Net Revenue (Shopify API).

2. Store: Save metrics to state/metrics.db (SQLite).

3. Analyze: Calculate POAS against Break-even ROAS. Compute Z-Scores comparing yesterday's performance to the 26-week historical average for that specific day of the week.

4. Synthesize: Pass detected anomalies to Claude Sonnet 4.6 to generate a human-readable, business-focused alert in Greek.

5. Act: Output a Markdown report and raise a GitHub Issue if critical thresholds are breached.

2. State Management (SQLite Schema)

To compute a 26-week rolling baseline efficiently within GitHub Actions (without requiring BigQuery), we introduce a lightweight SQLite database stored at state/metrics.db.

Claude Code MUST execute the following schema initialization in src/db/setup.py:

CREATE TABLE IF NOT EXISTS campaign_daily_metrics (
date TEXT NOT NULL,
day_of_week INTEGER NOT NULL, -- 0=Monday, 6=Sunday
campaign_id TEXT NOT NULL,
campaign_name TEXT NOT NULL,
spend_eur REAL NOT NULL,
impressions INTEGER NOT NULL,
clicks INTEGER NOT NULL,
conversions REAL NOT NULL,
conversion_value_eur REAL NOT NULL,
cogs_eur REAL NOT NULL,
net_revenue_eur REAL NOT NULL,
poas REAL,
roas REAL,
PRIMARY KEY (date, campaign_id)
);

3. Mathematical Logic & Algorithms

Claude Code MUST implement the following mathematical models exactly as defined in src/calculators/math_models.py.

3.1 Profitability (POAS)

$$Break-even\ ROAS = \frac{1}{Margin\ \%}$$

$$POAS = \frac{Net\ Revenue\ -\ COGS}{Ad\ Spend}$$

• Threshold Rule: If $POAS \< 1.0$, the campaign is operating at a loss. If $POAS > 1.5$, it is a candidate for scaling.

3.2 Anomaly Detection (Z-Score)

To avoid false positives from weekend slumps, anomalies are calculated by comparing yesterday to the same day-of-week over the last 26 weeks.

1. Filter History: Select rows where campaign_id == X AND day_of_week == yesterday.day_of_week AND date >= (yesterday - 26 weeks).

2. Calculate Mean ($\mu$) and Standard Deviation ($\sigma$) for the spend_eur and poas columns.

3. Calculate Z-Score: $$Z = \frac{Current\ Value - \mu}{\sigma}$$

• Alert Trigger Rule: Trigger an anomaly alert ONLY IF $|Z| > 2.0$ AND absolute $\Delta Spend > 5.0$ EUR.

4. Google Ads API Queries (GAQL)

The following GAQL query MUST be added to src/clients/google_ads.py to fetch campaign performance. Claude Code must use segments.date = YESTERDAY to ensure mature data.

SELECT
segments.date,
segments.day_of_week,
campaign.id,
campaign.name,
metrics.cost_micros,
metrics.impressions,
metrics.clicks,
metrics.conversions,
metrics.conversions_value
FROM campaign
WHERE segments.date = YESTERDAY
AND campaign.status = 'ENABLED'
AND campaign.advertising_channel_type = 'PERFORMANCE_MAX'

5. Claude AI Integration (The Analyst Prompt)

When anomalies are detected (Z-score triggers), the data is passed to Anthropic API (Claude Sonnet 4.6) for narrative synthesis. Claude Code MUST implement this system prompt in prompts/watchdog_analyst.md:

System Prompt:

You are an expert Google Ads Media Buyer for "Moodbook" (a premium Greek e-commerce brand). You analyze raw anomaly data and explain the business impact to the owner, Stelios.

Rules:

1. Always write the final output in professional Greek.

2. Focus on POAS (Profit On Ad Spend) and wasted budget.

3. Format your response as a clear, bulleted list of "Διαπίστωση" (Finding) and "Προτεινόμενη Ενέργεια" (Suggested Action).

4. Never suggest discounts. Maintain a premium brand tone.

5. Be concise. Do not use generic marketing fluff.

6. Required Files & Code Structure Modifications

Claude Code must create/modify the following files:

• src/db/metrics_db.py: Handles SQLite connection, schema creation, and insertion of daily metrics.

• src/calculators/z_score.py: Implements Pandas logic to query SQLite and return a list of Anomaly dataclass objects.

• src/agents/watchdog.py: Orchestrates the flow: Pull GA/Shopify data -> Insert to DB -> Run Z-Score -> If anomalies > 0, call Anthropic API -> Generate Alert.

• .github/workflows/module1-watchdog.yml: A new GitHub Actions workflow running daily at 07:30 EET.

7. Acceptance Criteria

• AC1: state/metrics.db is successfully created and updated after a run, then committed back to the repository.

• AC2: The Z-Score calculator correctly handles edge cases (e.g., standard deviation = 0 when historical data is identical or missing).

• AC3: The system successfully identifies a synthetic anomaly injected via tests (e.g., mocking a $Z = 2.5$ spike) and logs it.

• AC4: Claude Sonnet 4.6 returns a properly formatted Greek narrative when anomalies are passed to it.

• AC5: DRY_RUN=true prevents any automated emails/issues from being sent, logging them to the console instead.

Module 10 Technical Spec_ Recommendations Triage & QS Engineering

Module 10 — Recommendations Triage & QS Engineering (Technical Specification)

Project: Moodbook Google Ads Automation Bot

Module: 10 — Google Recommendations Triage & Quality Score Deconstruction

Target Audience: Claude Code (Developer Agent)

Dependencies: Module 00 (Foundation), Module 01 (Profit Core), Module 02 (Negatives)

1. Overview & Architecture

This module protects the Moodbook account from "algorithmic bloat" by triaging Google's auto-applied recommendations. It also implements Quality Score (QS) Engineering by deconstructing QS into its sub-components (eCTR, Ad Relevance, LP Experience) to identify exactly why a keyword/asset is expensive.

Data Flow:

1. Extract: Pull pending recommendations and asset-level Quality Score indicators from the API.

2. Triage: Claude Haiku 4.5 classifies recommendations as ACCEPT_SAFE, REVIEW, or REJECT_DEFAULT.

3. Deconstruct: Break down QS for search keywords and PMax asset groups.

4. Clean: Auto-dismiss REJECT_DEFAULT recommendations.

5. Propose: Open a Pull Request for manual review of high-impact changes.

2. Database Extension (SQLite)

Track recommendation actions to avoid re-reviewing dismissed items.

CREATE TABLE IF NOT EXISTS recommendations_log (
recommendation_id TEXT PRIMARY KEY,
type TEXT,
action TEXT, -- ACCEPTED, REJECTED, DISMISSED
ai_reason TEXT,
date TEXT
);

3. Logic & Triage Rules

Claude Code must implement these classification rules in src/agents/triage.py:

3.1 Recommendation Categories

• REJECT_DEFAULT: "Add Broad Match", "Remove conflicting negatives", "Enable Auto-apply", "URL Expansion".

• ACCEPT_SAFE: "Fix disapproved ads", "Add Sitelinks/Callouts", "Data-driven attribution".

• REVIEW: "Adjust tROAS targets", "Increase budget".

3.2 Quality Score (QS) Engineering

Deconstruct the 1-10 score into points:

4. Google Ads API Query (GAQL)

-- Get Recommendations
SELECT
recommendation.id,
recommendation.type,
recommendation.impact,
recommendation.description
FROM recommendation

-- Get Keyword Quality Score
SELECT
keyword_view.resource_name,
ad_group_criterion.quality_info.quality_score,
ad_group_criterion.quality_info.creative_quality_score,
ad_group_criterion.quality_info.post_click_quality_score,
ad_group_criterion.quality_info.search_predicted_ctr
FROM keyword_view

5. Claude AI Integration (The Triage Agent)

System prompt in prompts/recommendation_analyst.md:

System Prompt:

You are a Skeptical Google Ads Consultant for Moodbook. Your goal is to maximize profit, not Google's revenue.

Tasks:

1. Reject any recommendation that increases broad matching without proof of intent.

2. Reject removal of negative keywords.

3. Approve "no-brainer" structural fixes (Sitelinks, extensions).

4. Explain the Ad Rank impact of Quality Score improvements.

5. Language: Greek.

6. Acceptance Criteria

• AC1: RecommendationService.DismissRecommendation is called automatically for "Bloat" types.

• AC2: Quality Score deconstruction is reported clearly in the weekly digest.

• AC3: Any tROAS adjustment recommendation is strictly moved to the "Review" category (PR required).

• AC4: DRY_RUN=true prevents any dismissal call to the API.

Module 2 Technical Spec_ Negative Keyword & Search Theme Management

Module 2 — Negative Keyword & Search Theme Management (Technical Specification)

Project: Moodbook Google Ads Automation Bot

Module: 02 — Semantic Negative Keyword Discovery & Search Theme Audit

Target Audience: Claude Code (Developer Agent)

Dependencies: Module 00 (Foundation), Module 01 (Database Infrastructure)

1. Overview & Architecture

This module automates the identification of wasteful search queries and manages internal competition (cannibalization) between Performance Max campaigns. It uses n-gram analysis combined with Claude Haiku's semantic understanding to distinguish between high-intent searches and irrelevant traffic.

Data Flow:

1. Extract: Pull search term insights from campaign_search_term_insight for all enabled PMax campaigns.

2. Process: Aggregate metrics at the n-gram level (unigrams, bigrams, trigrams) using Pandas.

3. Classify: Use Claude Haiku 4.5 to categorize top-cost n-grams based on the Moodbook product taxonomy.

4. Analyze Overlap: Apply Cosine Similarity to detect if campaigns are competing for the same intent.

5. Propose: Generate a YAML file of proposed negative keywords and open a GitHub Pull Request.

2. Database Extension (SQLite)

Claude Code must add a table to state/metrics.db to track "processed" and "excluded" terms to optimize API usage and avoid redundant classifications.

CREATE TABLE IF NOT EXISTS processed_search_terms (
search_term TEXT PRIMARY KEY,
classification TEXT, -- relevant, irrelevant, cross_product, competitor
last_seen_date TEXT,
action_taken TEXT -- negative_added, ignored
);

3. Mathematical Logic & Algorithms

3.1 N-gram Metrics Aggregation

For each campaign, tokenized search terms must aggregate cost, clicks, and conversions. An n-gram is a candidate for negative exclusion if:

(Cost > 2x Target CPA) AND (Conversions == 0)

3.2 Semantic Similarity (Cosine Similarity)

To detect cannibalization, Claude Code should implement a utility in src/calculators/similarity.py using scikit-learn (or a lightweight equivalent) to compare search term vectors between campaigns.

$$Similarity(A, B) = \frac{A \cdot B}{\|A\| \|B\|}$$

• Rule: If Similarity > 0.85 between two campaigns with different product lines, flag for "Cross-Product Negative" proposal.

4. Google Ads API Query (GAQL)

Note: campaign_search_term_insight requires filtering by a single resource. Claude Code must iterate through active PMax campaign IDs.

SELECT
campaign_search_term_insight.category_label,
segments.search_term,
metrics.cost_micros,
metrics.clicks,
metrics.conversions,
metrics.conversions_value
FROM campaign_search_term_insight
WHERE segments.date DURING LAST_30_DAYS
AND campaign_search_term_insight.campaign_id = '{campaign_id}'

5. Claude AI Integration (The Classifier)

Claude Code MUST implement the classification logic in src/agents/classifier.py using this system prompt:

System Prompt:

You are an NLP Search Analyst. Categorize search terms for Moodbook.gr (Premium Personalized Books).

Product Lines: Wedding, Pregnancy, Grandma, Grandpa, Mom, Dad, Baptism.

Categories:

1. irrelevant: General terms (e.g., "free pdf", "cheap notebooks").

2. cross_product: A "Dad" term appearing in a "Mom" campaign.

3. competitor: Specific brand names (e.g., "Public", "Skroutz").

4. relevant: Matches the campaign intent.

Constraint: Return ONLY a JSON object: {"term": "category", "reason": "why"}.

6. Negative Keyword PR Generation

The agent must create a new branch and open a PR with a file state/proposals/negatives_YYYYMMDD.yaml.

Format:

• campaign_id: "12345"
  negatives:
  • term: "δωρεάν"
    match_type: "BROAD"
    reason: "Classified as irrelevant (zero conversions)"
• cross_campaign_negatives:
  • term: "μπαμπάς"
    source_campaign: "Mom_PMax"
    target_campaign: "Dad_PMax"
    reason: "Semantic cannibalization detected"

7. Acceptance Criteria

• AC1: The system processes n-grams locally and filters out terms already in the processed_search_terms table.

• AC2: Claude Haiku 4.5 correctly identifies "cross-product" terms (e.g., Greek terms for 'Grandma' in a 'Baptism' campaign).

• AC3: A Pull Request is successfully opened with clear justifications for each negative keyword.

• AC4: Sparse updates are used to avoid overwriting existing negative keyword lists.

• AC5: DRY_RUN=true logs the proposed YAML to the terminal without opening a PR.

Module 3 Technical Spec_ Customer Match & RFM Audience Refresh

Module 3 — Customer Match & RFM Audience Refresh (Technical Specification)

Project: Moodbook Google Ads Automation Bot

Module: 03 — Predictive Audience Management & Data Manager API Integration

Target Audience: Claude Code (Developer Agent)

Dependencies: Module 00 (Foundation), Module 01 (Database Infrastructure)

1. Overview & Architecture

This module automates the synchronization of Moodbook's first-party customer data with Google Ads to optimize for Incremental Profit. It implements an RFM (Recency, Frequency, Monetary) analysis to segment the Shopify customer base and utilizes the new Data Manager API (mandatory after April 2026) for secure, hashed audience uploads.

Data Flow:

1. Extract: Pull customer transaction data from Shopify API (or processed export).

2. Analyze: Perform RFM segmentation and calculate Propensity Scores for churn and upselling.

3. Hash: Normalize PII (email/phone) and apply SHA-256 hashing locally.

4. Sync: Upload hashed identifiers to Google Ads via the Data Manager API.

5. Apply: Automatically update Exclusion Lists for "Lapsed" customers in acquisition campaigns.

2. Database Extension (SQLite)

Claude Code must add a table to state/metrics.db to track audience synchronization and match rate trends over time.

CREATE TABLE IF NOT EXISTS audience_sync_log (
sync_date TEXT NOT NULL,
segment_name TEXT NOT NULL, -- Champions, Lapsed, At_Risk, Newbies
member_count INTEGER NOT NULL,
upload_status TEXT NOT NULL, -- SUCCESS, FAILED, PENDING
google_list_id TEXT,
PRIMARY KEY (sync_date, segment_name)
);

3. Business Logic & Mathematical Models

3.1 RFM Segmentation Logic

Claude Code MUST implement a scoring system (1-5) for each metric in src/calculators/rfm.py:

• Recency: Days since last purchase. (Lapsed if > 18 months).

• Frequency: Total number of orders.

• Monetary: Total lifetime value (LTV).

Segmentation Rules:

• Champions: High R, High F, High M. (Used for Lookalike signals).

• Lapsed: Low R ( > 540 days). (Used for Exclusion to stop wasted spend).

• At Risk: High F/M but declining R. (Used for "Rescue" campaigns).

3.2 Incrementality & Suppression Logic

If a customer is classified as a "Champion" with a high propensity for brand-organic search, they must be added to a search exclusion list to prevent Brand Cannibalization.

4. Data Manager API Implementation

Claude Code MUST NOT use the legacy OfflineUserDataJobService. The implementation in src/clients/google_data_manager.py must follow these steps:

1. Normalization: Trim whitespace, convert to lowercase, and for Gmail, remove dots before '@'.

2. Hashing: TO_BASE64(SHA256(LOWER(TRIM(email)))).

3. Consent: Ensure the consent field is populated for EEA users (Consent Mode v2 compliance).

4. Asynchronous Upload: Manage the asynchronous audience_members:send call and poll for status.

5. Claude AI Integration (Audience Analyst)

Claude Sonnet 4.6 will monitor list size fluctuations. System prompt in prompts/audience_analyst.md:

System Prompt:

You are an Audience Strategist. Analyze the weekly RFM shift for Moodbook.gr.

Task:

1. Summarize how many customers moved from "Champions" to "At Risk".

2. Report the match rate efficiency from the Data Manager API.

3. Explain the "Profit Saved" by excluding Lapsed customers (Number of Excluded x Avg acquisition CPA).

4. Language: Greek.

6. Acceptance Criteria

• AC1: PII data (plain text emails/phones) is NEVER logged or stored in the SQLite database.

• AC2: The hashing algorithm strictly follows Google's SHA-256 requirements (normalization included).

• AC3: The Data Manager API client correctly handles the mandatory consent fields.

• AC4: Lapsed customers are successfully identified and pushed to a specific "Exclusion UserList".

• AC5: DRY_RUN=true logs the count of hashed members per segment without triggering the API upload.

Module 4 Technical Spec_ Merchant Center Custom Labels & Cannibalization Fix

Module 4 — Structural MC Labels & Cannibalization Cure (Technical Specification)

Project: Moodbook Google Ads Automation Bot

Module: 04 — Merchant Center Custom Labels & Listing Group Subdivision

Target Audience: Claude Code (Developer Agent)

Dependencies: Module 00 (Foundation), Module 01 (Profit Core Data)

1. Overview & Architecture

This module implements the single highest-leverage structural fix for Moodbook: the elimination of cross-campaign cannibalization. By applying Custom Labels to the Merchant Center feed, we force each Performance Max campaign to target a mutually exclusive subset of products, effectively resolving the "Tell-me-Mom/Dad" internal competition at the auction level.

Data Flow:

1. Map: Retrieve SKU data and assign labels based on product lines, profit margins, and seasonality.

2. Sync: Push label updates to Google Merchant Center via the Content API for Shopping.

3. Segment: (Setup Task) Reconfigure PMax asset_group_listing_group_filter to subdivide based on the assigned labels.

4. Audit: Verify that no SKU is targeted by more than one acquisition campaign.

2. Database Extension (SQLite)

Claude Code must add a table to state/metrics.db to track SKU-to-Label mapping and ensure synchronization consistency.

CREATE TABLE IF NOT EXISTS sku_label_mapping (
sku TEXT PRIMARY KEY,
product_line TEXT, -- custom_label_0
seasonality TEXT, -- custom_label_1
margin_band TEXT, -- custom_label_2
last_synced_at TEXT
);

3. Logic & Taxonomy Definition

Claude Code must implement the labeling logic in src/calculators/labeler.py based on the following taxonomy:

4. API Implementations

4.1 Merchant Center Content API

Claude Code must implement a client in src/clients/google_merchant.py using the google-shopping-content library. For recurring updates, use the products.patch method to update only the label fields, minimizing overhead.

4.2 PMax Listing Group Configuration (GAQL/Mutate)

The agent must provide a script to audit or reconfigure listing groups. The relevant resource is asset_group_listing_group_filter. Each campaign's "All products" filter must be replaced with a SUBDIVISION on custom_label_0.

-- GAQL to verify Listing Group subdivisions
SELECT
asset_group.name,
asset_group_listing_group_filter.type,
asset_group_listing_group_filter.case_value.product_custom_attribute.value
FROM asset_group_listing_group_filter
WHERE asset_group_listing_group_filter.case_value.product_custom_attribute.index = 'INDEX0'

5. Claude AI Integration (Feed Audit)

Claude Haiku 4.5 will perform a weekly audit of the feed structure. System prompt in prompts/feed_auditor.md:

System Prompt:

You are a Feed Management Specialist. Review the SKU-to-Label mapping for Moodbook.gr.

Goals:

1. Detect any SKUs with missing custom_label_0 values.

2. Flag "High Margin" products that are stuck in "Mid Margin" bands.

3. Confirm that the taxonomy adheres to the premium brand voice (e.g., no mention of "discount" in labels).

4. Language: Greek (for report), English (for technical logs).

6. Acceptance Criteria

• AC1: The Python client successfully pushes custom_label_X values to Merchant Center via the Content API.

• AC2: Each PMax campaign is verified to target ONLY its specific custom_label_0 value.

• AC3: The system correctly identifies "Zombies" (products with high spend but low margin) via the combination of Module 1 data and Module 4 labels.

• AC4: DRY_RUN=true performs the mapping logic and logs the json_body of the API request without executing the patch call.

• AC5: No PII or business-sensitive margin percentages are leaked into the Merchant Center attributes (use categorical bands: "high", "mid").

Module 5 Technical Spec_ Creative Fatigue & GenAI

Module 5 — Creative Fatigue & GenAI (Technical Specification)

Project: Moodbook Google Ads Automation Bot

Module: 05 — Creative Fatigue Detection & GenAI Copywriting

Target Audience: Claude Code (Developer Agent)

Dependencies: Module 00 (Foundation), Module 01 (Profit Core Data)

1. Overview & Architecture

This module monitors the performance of creative assets (Headlines, Descriptions, Images, Videos) and detects "Creative Fatigue" before ROI is impacted. It uses a mathematical Composite Fatigue Score (CFS) to identify underperforming assets and leverages Claude Sonnet 4.6 to generate brand-compliant, high-performance replacements.

Data Flow:

1. Extract: Pull asset-level performance labels and raw metrics (CTR, CPM, Conversions) from the Google Ads API.

2. Analyze: Compute CTR Decay, CPM Inflation, and Hook Rate (for videos). Calculate the CFS.

3. Diagnose: Identify "Fatigued" assets based on threshold breaches (CFS \< 0.8).

4. Generate: Send fatigued asset data and brand constraints to Claude Sonnet 4.6 to draft 5 new variants.

5. Propose: Open a GitHub Pull Request with a YAML proposal for asset rotation.

2. Database Extension (SQLite)

Claude Code must add a table to state/metrics.db to store the "Honey Period" (baseline) performance for each asset to calculate decay.

CREATE TABLE IF NOT EXISTS asset_baseline_metrics (
asset_id TEXT PRIMARY KEY,
asset_type TEXT NOT NULL, -- HEADLINE, DESCRIPTION, IMAGE, VIDEO
baseline_ctr REAL,
baseline_cpm REAL,
learning_exit_date TEXT,
last_check_date TEXT
);

3. Mathematical Logic & Algorithms

Claude Code must implement the following calculations in src/calculators/fatigue.py:

3.1 CTR Decay

$$CTR\_Decay = \frac{Current\_CTR\ (Last\ 7\ days)}{Baseline\_CTR\ (First\ 7\ days\ post-learning)}$$

3.2 CPM Inflation

$$CPM\_Inflation = \frac{Current\_CPM - Baseline\_CPM}{Baseline\_CPM}$$

3.3 Composite Fatigue Score (CFS)

A weighted index to trigger replacement:

$$CFS = (0.7 \times CTR\_Decay) - (0.3 \times CPM\_Inflation)$$

• Trigger Rule: If CFS \< 0.8 for an asset with ≥ 1,000 impressions, flag as FATIGUED.

3.4 Video Hook Rate

$$Hook\_Rate = \frac{Video\_Views\ at\ 3s}{Impressions}$$

• Rule: If Hook Rate drops by > 20% vs baseline, flag the Video Hook (Intro) for replacement.

4. Google Ads API Query (GAQL)

Fetching performance at the individual asset level within PMax asset groups.

SELECT
asset.id,
asset.name,
asset_group_asset.field_type,
asset_group_asset.performance_label,
metrics.ctr,
metrics.average_cpm,
metrics.video_views,
metrics.conversions
FROM asset_group_asset
WHERE campaign.advertising_channel_type = 'PERFORMANCE_MAX'
AND campaign.status = 'ENABLED'
AND asset_group_asset.status = 'ENABLED'

5. Claude AI Integration (The Copywriter)

Claude Code must implement the copy generation logic in src/agents/copywriter.py using constraints from CLAUDE.md.

System Prompt:

You are a Premium Direct Response Copywriter for Moodbook.gr (Personalized Books).

Brand Rules:

1. Language: Greek (Professional, Emotional, Premium).

2. Tone: Focus on the value of memory/gift. NO discount words (έκπτωση, προσφορά).

3. Grandpa/Grandma Constraint: Use "βιβλίο" (book), never "ημερολόγιο" (diary).

4. Baptism Constraint: Use religious-sensitive and elegant framing.

Input: Current fatigued headline: [Headline]. Product line: [Line].

Task: Provide 5 new headline variants (max 30 chars) and reasoning for each.

Output: JSON format.

6. Acceptance Criteria

• AC1: The system correctly identifies assets with a "LOW" performance label or CFS \< 0.8.

• AC2: Baselines are correctly captured only after the "Learning" status is removed from the asset.

• AC3: Generated Greek copy strictly avoids prohibited keywords (e.g., "έκπτωση") and respects character limits.

• AC4: DRY_RUN=true logs the new copy variants to the terminal without opening a PR.

• AC5: Video assets trigger alerts specifically based on Hook Rate metrics.

Module 6 Technical Spec_ tROAS Calibration & GTO Bidding

Module 6 — tROAS Calibration & GTO Bidding (Technical Specification)

Project: Moodbook Google Ads Automation Bot

Module: 06 — tROAS Optimization & Game Theory Optimal (GTO) Bidding

Target Audience: Claude Code (Developer Agent)

Dependencies: Module 01 (Profit Core), Module 05 (Creative Fatigue)

1. Overview & Architecture

This module implements a rational, unexploitable bidding strategy based on Game Theory Optimal (GTO) principles. It calibrates Target ROAS (tROAS) by calculating the Expected Value of a click and identifying the Nash Equilibrium using Google's Bid Simulators. It also accounts for Conversion Lag (Bake Rate) to ensure decisions aren't made on immature data.

Data Flow:

1. Extract: Pull historical conversion rates, net margins, and bid_simulator data from the Google Ads API.

2. Calculate: Determine the GTO Expected Value and the Bake Rate adjusted ROAS.

3. Analyze: Use simulator data to find the point where Marginal Cost equals Marginal Revenue (Nash Equilibrium).

4. Detect: Identify "Irrational Competition" where market CPCs exceed our GTO Expected Value.

5. Propose: Open a Pull Request with a YAML proposal to adjust tROAS (max ±50bps per week).

2. Database Extension (SQLite)

Claude Code must add a table to state/metrics.db to track tROAS changes and simulator projections.

CREATE TABLE IF NOT EXISTS bidding_log (
campaign_id TEXT NOT NULL,
change_date TEXT NOT NULL,
old_troas REAL,
new_troas REAL,
projected_conversion_value_change REAL,
projected_cost_change REAL,
marginal_poas REAL,
PRIMARY KEY (campaign_id, change_date)
);

3. Mathematical Logic & GTO Algorithms

Claude Code must implement these in src/calculators/bidding.py:

3.1 GTO Expected Value

$$Expected\ Value = Conversion\ Rate \times Net\ Margin$$

• GTO Rule: If Average_CPC > Expected_Value, the agent must propose increasing tROAS (lowering bids) to "fold" against irrational competitors.

3.2 Bake Rate (Conversion Lag) Adjustment

Before analyzing ROAS, adjust "Yesterday's" value based on the historical lag (from Module 1 data):

$$Adjusted\ ROAS = \frac{Current\ Conversion\ Value}{Bake\ Rate}$$

3.3 Nash Equilibrium (Marginal POAS)

Identify the tROAS point where:

$$\Delta Profit / \Delta Spend \approx 1.1$$

4. Google Ads API Query (GAQL)

Fetching the campaign_aggregate_asset_view or campaign_bid_simulator.

SELECT
campaign_bid_simulator.campaign_id,
campaign_bid_simulator.target_roas_points.target_roas_simulations
FROM campaign_bid_simulator
WHERE campaign_bid_simulator.campaign_id = '{campaign_id}'

5. Claude AI Integration (The Game Theorist)

Claude Code must use Sonnet 4.6 to interpret simulation data. System prompt in prompts/bidding_strategist.md:

System Prompt:

You are a Game Theory Expert specializing in VCG Auctions. Analyze the bid simulator results for Moodbook.

Decision Logic:

1. Identify the Nash Equilibrium where we maximize absolute profit, not just ROAS.

2. Flag "Irrational Market Behavior" if competitors have pushed CPCs above our Expected Value.

3. Propose a small adjustment (±5% of current tROAS) to maintain algorithmic stability.

4. Language: Greek (for report).

6. Acceptance Criteria

• AC1: tROAS proposals never exceed ±50 basis points per run to avoid resetting Google's learning phase.

• AC2: The system correctly calculates "Expected Value" using current Conversion Rate and COGS-adjusted Margin.

• AC3: Conversion Lag (Bake Rate) is factored in before any tROAS decrease is proposed.

• AC4: DRY_RUN=true prevents the CampaignService.mutate call and logs the proposed change to YAML.

Module 7 Technical Spec_ Seasonal & Greek-Calendar Automation

Module 7 — Seasonal & Greek-Calendar Automation (Technical Specification)

Project: Moodbook Google Ads Automation Bot

Module: 07 — Seasonal Activations & Event-Driven Automation

Target Audience: Claude Code (Developer Agent)

Dependencies: Module 00 (Foundation), Module 05 (Creative Gen)

1. Overview & Architecture

This module automates campaign adjustments and asset rotations based on the Greek seasonal calendar and Moodbook's specific product peaks (Wedding, Baptism). It ensures Moodbook is ready for key gift-giving events without manual intervention, following the "No-Discount" brand policy.

Data Flow:

1. Check Calendar: Monitor a local JSON configuration for upcoming Greek events.

2. Assess Lead Time: Trigger stages (T-21, T-14, T-7) based on the event date.

3. Draft: Use Claude Sonnet 4.6 to draft seasonal headlines (e.g., "Gift Wrapping included for Mother's Day").

4. Multipliers: Propose budget multipliers for peak weeks.

5. Act: Open a Pull Request with seasonal asset/budget changes.

2. State Management (JSON Config)

Claude Code must create config/seasonal_calendar.json with specific Greek dates:

{
"events": [
{"name": "Orthodox Easter", "date_calc": "variable_easter", "lead_days": 14},
{"name": "Mother's Day", "date_calc": "greek_mother_day", "lead_days": 21},
{"name": "Father's Day", "date_calc": "3rd_sunday_june", "lead_days": 14},
{"name": "Wedding Season", "start": "04-01", "end": "10-31", "multiplier": 1.2}
]
}

3. Logic & Automation Rules

Claude Code must implement the transition logic in src/calculators/seasonal.py:

• Phase 1 (Discovery - T-21): Increase budget for specific product line PMax by 10%. Claude drafts "Planning" copy.

• Phase 2 (Peak - T-7): Increase budget by 25%. Claude drafts "Guaranteed Delivery" copy.

• Phase 3 (Post - T+2): Revert to baseline budget and evergreen assets.

Strict Constraint: No seasonal copy can include "Εκπτώσεις". Focus on "Δωρεάν Συσκευασία Δώρου" and "Προσωποποιημένο".

4. Claude AI Integration (Seasonal Copywriter)

System prompt in prompts/seasonal_copywriter.md:

System Prompt:

You are an expert Greek copywriter for Moodbook. Generate seasonal assets for [Event Name].

Rules:

1. Focus on the emotional connection of the gift.

2. Mention "Δωρεάν Συσκευασία Δώρου" and "Εγγύηση Παράδοσης".

3. Character limits: 30 chars for headlines.

4. Product lines to target: [Line].

5. Language: Elegant Greek.

5. Acceptance Criteria

• AC1: Variable dates like Greek Easter are correctly calculated via a utility function.

• AC2: Seasonal asset proposals are cleanly separated from evergreen assets in the Pull Request.

• AC3: Budget multipliers are applied relative to the campaign's 30-day baseline spend.

• AC4: DRY_RUN=true prevents any asset group mutation.

Module 8 Technical Spec_ Mobile Funnel & GA4 Audit

Module 8 — Mobile Funnel & GA4 Audit (Technical Specification)

Project: Moodbook Google Ads Automation Bot

Module: 08 — Technical Funnel Monitoring & Data Drift Audit

Target Audience: Claude Code (Developer Agent)

Dependencies: Module 01 (Profit Core)

1. Overview & Architecture

This module acts as a technical watchdog for Moodbook's e-commerce funnel. It monitors for discrepancies between Google Ads and GA4 (Conversion Drift) and identifies technical underperformance in the mobile user experience. It does not fix UX but provides the data necessary for Shopify-side intervention.

Data Flow:

1. Extract: Pull device-segmented metrics (Mobile vs Desktop) and fetch GA4 transaction counts via the Google Analytics Data API.

2. Fetch URLs: Scan all active landing_page_view URLs for 404/500 errors.

3. Compare: Calculate the drift percentage between GAds conversions and GA4 purchases.

4. Analyze: Claude Haiku scores ad-to-landing-page message match.

5. Alert: Raise a high-priority GitHub Issue if drift exceeds 15% or Mobile CPA > 1.4x Desktop.

2. Database Extension (SQLite)

Track technical health and drift history in state/metrics.db.

CREATE TABLE IF NOT EXISTS technical_audit (
audit_date TEXT NOT NULL,
campaign_id TEXT NOT NULL,
mobile_cpa REAL,
desktop_cpa REAL,
gads_conversions REAL,
ga4_conversions REAL,
drift_pct REAL,
lp_status_code INTEGER,
PRIMARY KEY (audit_date, campaign_id)
);

3. Mathematical Logic & Drift Rules

Implement in src/calculators/technical_audit.py:

3.1 Conversion Drift Calculation

$$Drift = \frac{|GAds\_Conversions - GA4\_Transactions|}{GA4\_Transactions}$$

• Rule: If Drift > 0.15 for a campaign with ≥ 5 transactions, flag for "Tracking Audit".

3.2 Mobile Efficiency Index (MEI)

$$MEI = \frac{Mobile\ CPA}{Desktop\ CPA}$$

• Rule: If MEI > 1.4, trigger a "Mobile Funnel Performance" alert.

4. Google Ads & GA4 API Queries

GAQL (Device Segment):

SELECT
campaign.id,
device,
metrics.cost_micros,
metrics.conversions
FROM campaign
WHERE segments.date = YESTERDAY

GA4 Query: Fetch totalPurchases dimensioned by sessionSourceMedium.

5. Claude AI Integration (Technical Analyst)

System prompt in prompts/technical_analyst.md:

System Prompt:

You are a Technical SEO and Tracking Expert. Analyze the following drift and mobile performance data for Moodbook.

Instructions:

1. If drift is high, explain potential causes (Cookie consent, Server-side tracking failure).

2. If mobile CPA is high, analyze the Landing Page UX (Speed, configurator difficulty).

3. Explain if the technical failure is wasting more than €50/week.

4. Language: Greek.

6. Acceptance Criteria

• AC1: The script successfully authenticates with BOTH Google Ads and GA4 Data APIs.

• AC2: Headless URL checks are cached to avoid excessive requests to moodbook.gr.

• AC3: High-priority alerts are issued as GitHub Issues with "Critical" tags.

• AC4: Device performance is tracked over time to identify if mobile abandonment is worsening.

Module 9 Technical Spec_ Forecast & Saturation Modeling

Module 9 — Forecast & Saturation Modeling (Technical Specification)

Project: Moodbook Google Ads Automation Bot

Module: 09 — Budget Saturation Modeling & Marginal Profit Forecasting

Target Audience: Claude Code (Developer Agent)

Dependencies: Module 01 (Profit Core), Module 06 (tROAS Calibration)

1. Overview & Architecture

This module identifies the "Scaling Limit" for each Moodbook product line. It uses Non-Linear Programming (NLP) to build saturation curves, identifying the exact point where adding more budget stops generating incremental profit (Nash Equilibrium). It provides a CLI tool for Stelios to run "What-if" scenarios.

Data Flow:

1. Extract: Pull 90 days of budget_simulator and bid_simulator data.

2. Model: Use logarithmic regression to build a saturation curve ($Profit = a \cdot \ln(Spend) + b$).

3. Calculate: Determine the Marginal POAS (Profit on Ad Spend of the next €1).

4. Simulate: Claude Sonnet 4.6 turns numerical scaling projections into business cases.

5. CLI: Provide a terminal command for manual scaling queries.

2. Database Extension (SQLite)

Store regression coefficients to track how market saturation changes seasonally.

CREATE TABLE IF NOT EXISTS saturation_models (
campaign_id TEXT PRIMARY KEY,
last_updated TEXT,
alpha_coefficient REAL,
beta_coefficient REAL,
max_profitable_spend REAL
);

3. Mathematical Modeling & Scaling Rules

Implement in src/calculators/forecaster.py:

3.1 Saturation Curve (Logarithmic)

$$Conversion\ Value = \alpha \cdot \ln(Spend) + \beta$$

• Use scipy.optimize.curve_fit to calculate coefficients from simulator data points.

3.2 Marginal POAS Calculation

$$Marginal\ POAS = \frac{d(Net\ Profit)}{d(Spend)}$$

• Scaling Rule: Propose budget increases ONLY IF Marginal POAS > 1.2.

• Ceiling Rule: If Marginal POAS \< 1.05, the campaign is "Saturated". Stop increasing budget.

4. Google Ads API Query (GAQL)

SELECT
campaign_budget_simulation_view.budget_simulation_points
FROM campaign_budget_simulation_view
WHERE campaign.id = '{campaign_id}'

5. Scaling CLI Utility

Claude Code must create cli/forecast.py:

# Example Usage:
# python cli/forecast.py --campaign "Wedding_PMax" --increase 20

The CLI will output:

"Increasing budget by 20% (€X) is projected to generate €Y in net profit (Marginal POAS: 1.15). Status: SCALE RECOMMENDED."

6. Claude AI Integration (Scaling Strategist)

System prompt in prompts/scaling_strategist.md:

System Prompt:

You are a Financial Analyst for an e-commerce brand. Explain scaling projections to the owner.

Focus:

1. Distinguish between ROAS (which always drops when scaling) and Net Profit (which we want to maximize).

2. Explain the risk of "Hitting the Wall" (Saturation).

3. Language: Greek.

7. Acceptance Criteria

• AC1: Saturation models are updated weekly to account for changes in competition.

• AC2: The scaling logic respects the "20% rule" (no budget jump > 20% in 7 days).

• AC3: Marginal POAS calculation correctly uses the net profit margin from Module 1.

• AC4: CLI tool provides clear uncertainty bands (e.g., ±10% on projections).

Πώς δουλεύει και τι δεν κάνει ποτέ

Ημερολόγιο Παρουσίας

Ένα post την ημέρα στο Instagram (@moodbook.gr) και στη σελίδα Facebook (moodbook.gr), από τα δεδομένα που ήδη έχουμε στο Shopify.

Σχέδιο και σκεπτικό: το artifact «Ημερολόγιο Παρουσίας». Πώς βγήκε το κλειδί της Meta: το artifact «Το Κλειδί της Meta».

Πώς δουλεύει

Δύο στάδια, επίτηδες χωρισμένα.

Κάθε Δευτέρα — ο Claude διαβάζει προϊόντα, κριτικές, όρους αναζήτησης, εποχή και τα στατιστικά της περασμένης εβδομάδας, διαλέγει επτά γωνίες, γράφει επτά κείμενα, και τα γράφει στο out/queue.json.

Κάθε βράδυ 19:00 — το publish_today.py παίρνει τη σημερινή εγγραφή, φτιάχνει τις δύο μορφές εικόνας, τις ανεβάζει στα Αρχεία Shopify για να αποκτήσουν δημόσια διεύθυνση, και δημοσιεύει.

Ο λόγος που τα κείμενα δεν τα γράφει το script: η ποιότητα πρόζας ενός μικρού μοντέλου σε cron δεν φτάνει, και μια εβδομάδα γραμμένη με όλα τα δεδομένα μπροστά βγαίνει καλύτερη από επτά νύχτες τυφλής παραγωγής.

Οι επτά γωνίες

Μία ανά ημέρα, ποτέ επτά προϊόντα — 76 προϊόντα εξαντλούνται σε δυόμισι μήνες και τότε ο λογαριασμός διαβάζεται ως κατάλογος.

Εντολές

Πώς διαλέγει ο σχεδιαστής

Όχι με τη σειρά των ημερών — οι απαιτητικές μέρες διαλέγουν πρώτες:

1. review — απαράβατο. Η κριτική λέει ποιο προϊόν δείχνει η φωτογραφία· αν λέει «ημερολόγιο γάμου», το post δεν μπορεί να δείχνει σχολικό.
2. product — η Δευτέρα ανοίγει με το εποχικά ισχυρότερο.
3. scene — είναι το μόνο post που ξοδεύει credits, άρα δεν παίρνει ό,τι περισσέψει.
4. inside — απαιτεί εσωτερικές φωτογραφίες, που δεν έχουν όλα τα προϊόντα.
5. occasion — δεμένο σε ημερολογιακή αφορμή, αν υπάρχει συγκεκριμένη.

Η βαθμολογία είναι εποχή×2 + πωλήσεις30ημερών μείον ποινή αν το προϊόν εμφανίστηκε τις τελευταίες 21 ημέρες. Οι κριτικές δεν επαναλαμβάνονται για 120 ημέρες. Οι δοκιμές (ΔΟΚΙΜΗ) δεν «καίνε» υλικό — μόνο οι ζωντανές.

⚠️ Μια ονομαστική εορτή έχει ετικέτα «δώρο» και ταιριάζει με οποιοδήποτε προϊόν· γι' αυτό το φίλτρο της Τετάρτης κρατάει μόνο τις συγκεκριμένες οικογένειες. Χωρίς αυτό, «Του Σταυρού» ζευγαρώθηκε κάποτε με ένα βιβλίο για τα Τρίκαλα.

Τα δύο χρονόμετρα

Δευτέρα 08:00 — scheduled task social-week-planner (Claude Desktop → Scheduled). Τρέχει τον σχεδιαστή, γράφει τα επτά κείμενα, φτιάχνει τη σελίδα έγκρισης, και τη στέλνει στον Στέλιο. ⚠️ Τρέχει μόνο όταν η εφαρμογή είναι ανοιχτή· αν ήταν κλειστή, τρέχει στο επόμενο άνοιγμα.

Κάθε βράδυ — GitHub Action .github/workflows/publish.yml, cron 0 16 * * * UTC = 19:00 το καλοκαίρι, 18:00 τον χειμώνα. Χρειάζεται private repo με πέντε Secrets: MOODBOOK_FB_PAGE_ID, MOODBOOK_IG_USER_ID, MOODBOOK_PAGE_TOKEN, SHOPIFY_SHOP_DOMAIN, SHOPIFY_ACCESS_TOKEN. Το workflow_dispatch τρέχει δοκιμή με ένα κλικ.

⛔ Οι φρουροί

Τέσσερις, και όλοι κρατάνε ακόμα και με --live (κλειδωμένοι σε 14 τεστ):

1. Η δοκιμή είναι η προεπιλογή. Χωρίς ρητό --live καμία κλήση δεν φεύγει προς τη Meta. Ίδιο μοτίβο με τους guarded write connectors.
2. Η σιωπή δεν είναι συγκατάθεση. approved: null σημαίνει «δεν το είδε ακόμα» και δεν φεύγει. Μόνο ρητό true περνάει. Μετά τις δύο πρώτες εβδομάδες, το --no-approval παρακάμπτει τη σκόπιμα.
3. Τίποτα δεν φεύγει δύο φορές. Ό,τι είναι published ή skipped μένει.
4. Ελλιπής ανάρτηση δεν ταξιδεύει — χωρίς εικόνα ή χωρίς κείμενο, δεν φεύγει.

Όρια που δεν συζητιούνται

• ⛔ Κανένα πραγματικό όνομα πελάτη. Το πεδίο προσωποποίησης των παραγγελιών δεν διαβάζεται από κανένα κομμάτι. Τα ονόματα του Σαββάτου είναι επινοημένα.
• ⛔ Καμία παραγωγή εξωφύλλου από το μηδέν. Το Magnific δέχεται τη φωτογραφία του υπαρκτού βιβλίου ως αναφορά και αλλάζει μόνο ό,τι υπάρχει γύρω του. ⚠️ Ποτέ μονή παραγωγή: στη δοκιμή της 04/09, δύο παραλλαγές από το ίδιο prompt — η μία κράτησε το εξώφυλλο ακέραιο, η άλλη έσβησε όλο το κείμενό του. Βγάζουμε τέσσερις και διαλέγουμε.
• Δεν απαντά σε σχόλια και μηνύματα. Δεν ανεβάζει τιμές ή προσφορές μόνο του.

Τα κομμάτια

social_studio/
  config.py         .env· ο Shopify token δανείζεται από τον read connector —
                    ένα μυστικό, μία θέση
  meta.py           Graph API. Η ΜΟΝΗ πόρτα προς τα έξω, κλειστή εκτός αν live=True
  shopify_files.py  τοπική εικόνα → μόνιμη διεύθυνση CDN
  imagery.py        4:5 και 9:16 τοπικά με Pillow — χωρίς credits, χωρίς ρίσκο
  queue.py          το συμβόλαιο των δύο σταδίων
publish_today.py    ο δημοσιευτής
check_key.py        ο έλεγχος υγείας

⚠️ Πράγματα που θα σε δαγκώσουν

• Το me/accounts γυρίζει κενό και δεν φταίει τίποτα. Με Facebook Login for Business η πρόσβαση δίνεται ανά αντικείμενο· ρώτα τη σελίδα κατευθείαν: GET /{PAGE_ID}?fields=name,access_token,instagram_business_account.
• Ο Access Token Debugger → Granular Scopes είναι η μόνη αξιόπιστη απόδειξη τι δόθηκε. Η οθόνη έγκρισης του login δείχνει λιγότερα απ' όσα τελικά ισχύουν.
• Το Instagram δεν δέχεται αρχείο — θέλει δημόσια διεύθυνση από την οποία κατεβάζει μόνο του. Οι διευθύνσεις του Magnific είναι υπογεγραμμένες και λήγουν, γι' αυτό όλα περνούν από τα Αρχεία Shopify.
• Το θολό υπόβαθρο στη story ανήκει μόνο στις σκηνές. Πάνω σε φωτογραφία προϊόντος με επίπεδο φόντο αφήνει ορατό λευκό κουτί. Η ανίχνευση γωνιών αποφασίζει μόνη της.
• Data Access Expires: 03/12/2026. Το κλειδί δεν λήγει, αλλά η πρόσβαση στα δεδομένα λήγει μετά ~90 ημέρες αδράνειας και οι πηγές διαφωνούν αν οι κλήσεις API μετράνε. Ο check_key.py προειδοποιεί στις <30 ημέρες.
• Το .bat είναι ASCII-only — ελληνικά μέσα σε .bat κρεμούν το cmd.exe. Τα ελληνικά ζουν στα .py, με chcp 65001 από το .bat.