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Paper Poster Html

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wanshuiyin
paper-poster-html

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", "conference poster", "paper poster", or asks to design/redo a research poster. Supersedes the retired LaTeX /paper-poster.

Overview

Publisherwanshuiyin
RepositoryAuto-claude-code-research-in-sleep
Skill namepaper-poster-html
Stars
16.3K
Forks
1.4K
Bundled files
29
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 29 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by wanshuiyin on GitHub. Read the source before you install it.

Installation

Install the Paper Poster Html AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep.git /tmp/Auto-claude-code-research-in-sleep
mkdir -p .claude/skills
cp -r /tmp/Auto-claude-code-research-in-sleep/skills/paper-poster-html .claude/skills/paper-poster-html
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Paper Poster Html in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Paper Poster Html on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Paper Poster Html is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Paper Poster (HTML): measurement-gated poster generation

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:

  1. Hard gates run before any aesthetic opinion (alignment, style, assets must PASS first — a reviewer never sees an unmeasured poster).
  2. 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.
  3. Two-hue discipline as a machine check, not a style suggestion.
  4. 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, reasoning xhigh, fresh thread per review call (mcp__codex__codex, never codex-reply across 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 #2D5F8B accent
    • gold #C9A24A highlight + 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.
  • 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

  1. Resume: if poster_html/POSTER_STATE.json exists with status: in_progress (< 24 h), resume from the saved phase.
  2. 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 to channel="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.
    • pdfinfo missing → PyMuPDF reads PDF dimensions. At least one of pdftoppm / PyMuPDF must exist for PNG review renders.
    • MathJax: download tex-svg.js once into poster_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.
  3. 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} into POSTER_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

  1. Read the paper source (.tex ideal; PDF otherwise). Extract: title/authors/affils, the 3–5 headline numbers, core method (equations verbatim), main results (tables/figures and what they show), takeaways. Build poster_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".
  2. 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 to poster_html/CLAIM_EVIDENCE.md.
  3. 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:

  1. Paper source figures/ (vector SVG/PDF → convert to SVG via inkscape/pdf2svg if available, else rasterize ≥ 2× rendered px).
  2. PDF-only: extract_pdf_figures.py contact-sheet + auto to list candidate regions → pick crops (🚦 human confirms crop choices) → crop at 300–450 DPI.
  3. Last resort: user supplies explicit page,x0,y0,x1,y1 bboxes.

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--duo combined 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 one figure--duo card, 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:

bash
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:

bash
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

bash
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.jsondone.

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.py output.
  • 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_decisions before "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.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Paper Poster Html AI skill do?

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", "conference poster", "paper poster", or asks to design/redo a research poster. Supersedes the retired LaTeX /paper-poster.

Why use Paper Poster Html on TypingMind?

Because you install it once and use it with any model. Paper Poster Html is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Paper Poster Html in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/paper-poster-html. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Paper Poster Html?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Paper Poster Html?

As many as you like. As long as a model supports skills, you can use Paper Poster Html with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Paper Poster Html AI skill free?

Yes. It is published on GitHub by wanshuiyin under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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