Paper Poster (HTML): measurement-gated poster generation
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Then ask your AI: use the Paper Poster (HTML): measurement-gated poster generation skill
About this capability
DEFAULT poster pipeline — build an academic conference poster (ICML/NeurIPS/ICLR/CVPR/...) as a single HTML/CSS file with measurement-driven hard gates, real paper figures, a two-hue design-token system, and print-ready PDF via headless Chromium. Use when the user says \"做海报\", \"poster\", \"confere
What this skill tells your AI
The instructions your AI receives, as published by wanshuiyin/auto-claude-code-research-in-sleep in skills/paper-poster-html/SKILL.md and read by ahel’s review.
One HTML file styled for an exact print canvas (@page { size: W H }), rendered to PDF
via Playwright print emulation. Iterate by measuring, not eyeballing — the screen
preview lies; only print emulation at the correct viewport tells the truth. Core gate
machinery is adapted from posterly (MIT, ©
2026 Ruishuo Chen — see NOTICE.md and LICENSES/posterly-MIT.txt); ARIS adds style
discipline gates, figure-provenance gates, the cross-model review loop, and the
anti-patch-loop fix vocabulary.
Why this skill exists (the failure it prevents)
A predecessor pipeline produced a poster with 30+ colors, zero real paper figures, a screen-pixel canvas, and tiny formulas floating in oversized boxes, then spent 12+ review rounds making it worse — each round added a new badge color or bespoke SVG patch. The cure is structural, not exhortative:
- Hard gates run before any aesthetic opinion (alignment, style, assets must PASS first — a reviewer never sees an unmeasured poster).
- A closed fix vocabulary — visual-review fixes can only touch design tokens, whole catalogued components, content rebalance, assets, or canvas choice. New inline styles / new hex values / bespoke decorations are structurally forbidden.
- Two-hue discipline as a machine check, not a style suggestion.
- Real paper figures with provenance manifest, or the gate fails.
Mental model
paper (.tex / PDF) ──► content plan + claim→evidence audit (codex, fresh)
│
figures extracted ─────────┤ FIGURE_MANIFEST.json (provenance, sha256)
(real paper figures ONLY) ▼
template scaffold ──► fill ──► run_gates.py ◄─── HARD, loop here
preflight → style → asset → measure → polish
│ all hard gates PASS
▼
Claude visual review (≤3 issues × ≤3 rounds, fix-vocabulary only)
│ score ≥ 9
▼
codex final cross-model review (fresh thread, full HTML+PDF)
│ pass
▼
verify-final → poster.pdf + GATE_REPORT.json
Constants
- SKILL_SCRIPTS =
${CLAUDE_SKILL_DIR}/scripts— all helpers are single-owner and ship inside this skill (Arch C). If the directory is missing the install is broken: abort and tell the user to re-install the skill (Policy A — the gates ARE the skill; never improvise replacements). - REVIEWER_MODEL =
gpt-6-astra, reasoningxhigh, fresh thread per review call (mcp__codex__codex, nevercodex-replyacross review boundaries). - CANVAS — from the venue's official spec, looked up live in Phase 0. Never assume. (Known anchor: ICLR 2026 main = 185×90 cm landscape per its official printing service; ICML/NeurIPS commonly 60×36 in landscape; workshop posters often 61×91 cm portrait. Specs change yearly — verify.)
- PALETTE — default =
templates/tokens/generic.json(slate-blue#2D5F8Baccent- gold
#C9A24Ahighlight + neutrals) for all venues. Venue packs are opt-in via— venue-colors: true. Purple-dominant accents (hue 250–285) are banned unless the user passes— allow-purple: true.
- gold
- AUTO_PROCEED = false — wait for explicit confirmation at every 🚦 checkpoint.
- OUTPUT_DIR =
poster_html/in the working directory.
Workflow
Phase 0 — Resume, dependencies, venue spec
- Resume: if
poster_html/POSTER_STATE.jsonexists withstatus: in_progress(< 24 h), resume from the saved phase. - Dependencies (degradation chain, in order):
- Playwright + bundled Chromium → if missing,
python3 -m playwright install chromium→ if install fails but system Chrome exists, scripts fall back tochannel="chrome"→ if all fail: you may produce the content plan and scaffold only, label everything "not print verified", and must NOT emit a final PDF. pdfinfomissing → PyMuPDF reads PDF dimensions. At least one of pdftoppm / PyMuPDF must exist for PNG review renders.- MathJax: download
tex-svg.jsonce intoposter_html/assets/mathjax/and reference it locally in the HTML. CDN is acceptable only for drafts; the measure gate hard-fails on unrendered MathJax either way.
- Playwright + bundled Chromium → if missing,
- Venue spec lookup (live): consult the venue's official poster-instructions page
(search + fetch). Extract dimensions, orientation, font floor, logo policy,
anonymity rules, file format. Record
{spec, source_url, retrieved}intoPOSTER_STATE.json— specs change yearly; never reuse a cached spec silently.
🚦 Checkpoint: echo the venue spec table (canvas, orientation, source URL) and the chosen template. Wait.
Phase 0.5 — Design discovery (one AskUserQuestion batch)
Ask once, ≤4 questions: layout template (from templates/README.md), palette
(default generic pack / venue pack / custom within constraints), logos + venue mark
(paths or "none" — never fabricate; check the venue's logo policy), QR target (paper /
code / project page / none — generate offline with qrencode or python-qrcode;
never a remote QR-service URL). Persist answers in POSTER_STATE.json as
design_decisions — re-read before any later "improvement" so deliberate choices are
never reverted.
Phase 1 — Paper ingest, content plan, claim audit
- Read the paper source (
.texideal; PDF otherwise). Extract: title/authors/affils, the 3–5 headline numbers, core method (equations verbatim), main results (tables/figures and what they show), takeaways. Buildposter_html/POSTER_CONTENT_PLAN.md— what goes in which column, word budget per card. Target density (excluding table cells, captions, author line, footer): standard poster 550–850 words; dense theory+empirical poster 750–1050 words, allowed only when ≥2 compact components are used (eqn-anatomy,flow-strip,derived-col,claim-pills,keybox--4). Warn yourself below 500 words on a 4-column landscape (it will read as sparse next to professionally dense posters) unless the template is hero/visual-first; warn above 1100 unless the user asked for dense mode. Bullets ≤ 8 words when possible — density comes from structure, not long prose. Prefer compact structure over prose: if the paper contains an explicit objective, algorithm, theorem mechanism, or baseline comparison, extract at least two of: (1) empirical objective / loss stack; (2) term-by-term equation anatomy; (3) a method-flow strip grounded in paper variables; (4) a derived-Δ column for method-vs-baseline rows; (5) a 4-up implementation/theory keybox; (6) a claim/evidence pill table for numeric-heavy posters. Do not invent an algorithm. If the paper has only an objective, label the component "objective flow" or "loss anatomy", never "algorithm". - Cross-model content audit (fresh codex thread,
xhigh): give it the content plan path + paper source path(s) — paths only, no summaries — and ask for a claim→evidence table:| claim on poster | paper file:line | paper says (verbatim) | match? |with match ∈ {OK, NUMERIC-MISMATCH, OVERCLAIM, MISSING-PRECONDITION, NOT-IN-PAPER, SCOPE-NARROWED}. Save toposter_html/CLAIM_EVIDENCE.md. - Fix every non-OK row or record it as a user-acknowledged tradeoff.
🚦 Checkpoint: content plan + audit summary. Wait.
Phase 2 — Real paper figures (provenance-gated)
Source preference chain:
- Paper source
figures/(vector SVG/PDF → convert to SVG viainkscape/pdf2svgif available, else rasterize ≥ 2× rendered px). - PDF-only:
extract_pdf_figures.py contact-sheet+autoto list candidate regions → pick crops (🚦 human confirms crop choices) →cropat 300–450 DPI. - Last resort: user supplies explicit
page,x0,y0,x1,y1bboxes.
Then preprocess_figures.py --autocrop every asset. Every paper-derived image gets a
FIGURE_MANIFEST.json entry (source hash, page, bbox, dpi, sha256, natural_px) and is
embedded as <img data-source="paper" data-asset-id="...">.
Hard rule: ≥ 2 paper-derived visuals or the asset gate fails. Theory-only papers
may waive the total-area rule (--waive-total-area) at a human checkpoint — never
silently. Never draw bespoke decorative SVG "figures" as substitutes.
Figure-area bands (asset gate, fractions of body): total target 14–22 % (warn < 12 % / > 24 %, hard < 10 % / > 28 %); per ordinary figure target 4–8 % (warn
10 %, hard > 13 %);
figure--duocombined 8–12 %. Hero templates pass--hero(centerpiece may take 30–40 %). The failure mode is symmetric: too small reads as decoration, too big crowds out content. Sibling figures that share axes or tell a before→after story belong in onefigure--duocard, not two cards.
Phase 3 — Scaffold + tokens
cp templates/<chosen>.html poster_html/poster.html; retarget @page + .poster
dims to the venue canvas (two edits, same values); apply the chosen token pack onto the
:root DESIGN TOKENS block; fill content per the plan; embed manifest figures.
Run preflight + style_check — both must PASS before any layout iteration. (A fresh
scaffold is expected to fail measure — that gate judges a filled poster.)
Phase 4 — Layout hard loop
After every layout change:
python3 "$SKILL_SCRIPTS/run_gates.py" poster_html/poster.html \
--tokens <pack.json> --manifest poster_html/FIGURE_MANIFEST.json \
--report poster_html/GATE_REPORT.json
Canonical order: preflight → style → asset → measure → polish. Targets: column-bottom
spread < 5 px (aim < 3), footer gap ∈ [30, 50] px, intercard gap ∈ [12, 50] px,
canvas-fill ∈ [95, 101] %, poster bbox aligned to page within ±2 px. Fix guidance for
each failure mode lives in the gate output and templates/COMPONENTS.md. Do not
proceed while any hard gate fails. Do not let a reviewer see an unmeasured poster.
Balance under-filled columns with content from the paper (Gate C), never with
whitespace, space-between, or stretched cards.
Phase 5 — Claude visual review (gated aesthetics)
Render and read the result yourself:
python3 "$SKILL_SCRIPTS/render_preview.py" poster_html/poster.html
pdftoppm -r 100 poster_html/poster_preview.pdf poster_html/review_full -png -f 1 -l 1
# plus 2-4 region crops at higher res (header / one column / equations) via PIL
Calibrate first (../shared-references/taste-calibration.md): if
human-curated references/good/ + references/bad/ exist under this skill
dir (or the project supplies its own pair), score those 3+3 reference posters
on the axes below BEFORE the target, anchoring the scale. Never select, search
for, or generate anchors yourself; if no reference sets exist, proceed
uncalibrated and mark CALIBRATION: none — never fabricate anchor scores.
Axes (weights sum 1.0): Design 0.35 · Craft 0.30 · Functionality 0.20 ·
Originality 0.15. Mapping: SCORE = min(round(1 + 9 × COMPOSITE), lowest triggered cap) — caps apply AFTER the mapping, and the loop's Score ≥ 9
threshold below always reads this final capped SCORE, never the raw
composite.
Score strictly 1–10. Critical caps (hard floors — a calibrated composite never overrides them): < 2 real paper figures → ≤ 3; broken canvas / clipped content / unreadable math → ≤ 4; ≥ 4 visible hue families or gradient-heavy header → ≤ 4; large blank cards or columns → ≤ 5; fabricated visual claim → ≤ 3. Checks: posterly-showcase gestalt (would this hang next to a professionally designed poster without looking like a patched dashboard?), single-accent discipline, real figures readable and central, print hierarchy (title → headline stats → figures → detail), column fill, equation prominence (no tiny math in oversized boxes), serif-body/sans-display pairing, no gradient kitsch, component consistency, 60-second narrative. Output format:
SCORE: N/10 (= min(round(1 + 9 × COMPOSITE), lowest cap); drives the loop)
COMPOSITE: 0.xx (weighted; list the four per-axis scores)
CALIBRATION: anchored | none
GAP: <which reference poster the target falls short of / exceeds, on which axis, and why — one paragraph; omit only when CALIBRATION: none>
CAPS_TRIGGERED: ...
TOP_ISSUES: (max 3)
ALLOWED_FIX_TYPE per issue: token | component | rebalance | asset | template/canvas
PATCH_LOOP_RISK: low | medium | high
Loop: fix (fix vocabulary below) → re-run Phase 4 gates → re-score. ≤ 3 issues per round, ≤ 3 rounds. Score ≥ 9 → Phase 6. Still < 9 after 3 rounds → STOP patching; escalate to template / canvas / content re-choice (back to Phase 3) or a human decision. Never enter round 4 of cosmetic patching.
Fix vocabulary (closed set — the anti-patch-loop core)
Allowed: (a) edit a :root token value; (b) swap/remove/add a whole component
instance from templates/COMPONENTS.md; (c) content rebalance (move a card across
columns, trim/grow text from the paper, resize a figure within its AR band);
(d) template/canvas re-choice; (e) global edits to an existing component's CSS
that reference only tokens; (f) switching predefined variants (.eqn--large,
.card--compact, .figure--wide, .nowrap, …); (g) asset fixes (re-crop, swap
for a clearer figure from the same paper, re-preprocess).
Forbidden: new inline styles, new hex values anywhere, bespoke decorative SVG,
per-element font-size overrides. A new component may not be born inside the visual
loop — stop, get a human checkpoint, add it to COMPONENTS.md, re-run from Phase 3.
Phase 6 — Codex final review (fresh thread, cross-model)
All hard gates PASS + polish warnings zero-or-waived + visual ≥ 9 first. Then a fresh
codex thread (xhigh) reviews the final artifacts (not the content plan):
poster.html, the rendered PDF/PNG, the paper source, GATE_REPORT.json,
CLAIM_EVIDENCE.md — paths only, no executor framing. It checks: (1) fidelity &
overclaims re-checked on final text (polish introduces new claims), (2) residue
(\ref{, TODO, raw < in math, missing images, remote URLs), (3) visual rhetoric
(headline numbers prominent, banner readable from 2 m), (4) gate-log coherence. The
reviewer recommends; it does not edit. Any fix → back through Phase 4/5 gates — never
straight to re-review.
Phase 7 — Final verification + report
python3 "$SKILL_SCRIPTS/poster_check.py" verify-final poster_html/poster_preview.pdf \
--from-html poster_html/poster.html --max-size-mb 20
Page count 1, dimensions match @page, size ≤ 20 MB, no TODO/residue, no remote
assets. Report: PDF path, final spread px, footer-gap range, gate summary table,
unresolved waivers, codex verdict. Update POSTER_STATE.json → done.
State persistence
poster_html/POSTER_STATE.json: {phase, venue, canvas{w,h,orientation,source_url, retrieved}, template, token_pack, design_decisions{...}, figures_selected[], visual_rounds, codex_threads{audit, final}, status, timestamp} — written after every
phase; enables compact-recovery resume.
Key rules
- Measure, don't eyeball. No layout claim without
run_gates.pyoutput. - Gates before aesthetics. Claude/codex review only ever sees a poster whose hard gates PASS. This ordering is what kills the patch-loop death spiral.
- Never invent paper numbers or figures. Numbers come from the paper source; visuals carry manifest provenance. Fabrication = critical cap ≤ 3.
- Two hues, one system. Accent + gold + neutrals. The style gate enforces it; don't negotiate with the gate.
- Real figures are the poster. A poster without the paper's own figures is a dashboard, not a poster.
- Fix vocabulary is closed. If a fix isn't expressible as token / component / rebalance / asset / canvas, it's the wrong fix.
- Cross-model verdicts. Claude drives the loop and scores visuals; acceptance of content fidelity comes from the fresh codex thread (a loop can drive, never acquit).
- Preserve user decisions. Re-read
design_decisionsbefore "improving" anything. - Vendor boundary.
poster_check.py,render_preview.py,_posterly/are vendored from posterly — keep diffs minimal; ARIS-side logic goes in the new scripts, not in vendored files.
Review tracing
Save every codex reviewer call's trace per shared-references/review-tracing.md to
.aris/traces/paper-poster-html/<date>_run<NN>/ (audit + final threads, raw responses).
Output contract
poster_html/
├── poster.html # single-file source of truth
├── poster_preview.pdf # print-emulated, verify-final-checked
├── poster_preview.png # thumbnail
├── POSTER_STATE.json # resume state
├── GATE_REPORT.json # canonical gate ledger (schema v1)
├── POSTER_CONTENT_PLAN.md # what-goes-where + word budgets
├── CLAIM_EVIDENCE.md # codex claim→evidence audit
├── FIGURE_MANIFEST.json # figure provenance (sha256, page, bbox, dpi)
└── assets/{paper_figures,logos,qr,mathjax}/
When NOT to use
- Slides, not a poster →
/paper-talk//slides-polish. - The paper's headline isn't stable yet — fix the paper first; a poster amplifies whatever story it's given.
Signals
- GitHub stars
- 16k
- Forks
- 1k
- Last commit
- Sep 2026
ahel review
K1binfo
installs-packages (in scripts/_posterly/measure.py)K1binfo
installs-packages (in scripts/_posterly/polish.py)K1binfo
installs-packages (in scripts/_posterly/verify_final.py)K1binfo
installs-packages (in scripts/asset_check.py)K1binfo
installs-packages (in scripts/extract_pdf_figures.py)K1binfo
installs-packages (in scripts/preprocess_figures.py)K1binfo
installs-packages (in scripts/render_preview.py)K1binfo
installs-packages (in scripts/style_check.py)
Automated review, not a security audit. Ruleset v1+k2.
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paper-poster-html- Source
- github.com/wanshuiyin/auto-claude-code-research-in-sleep