mcpbeat

Paper Poster Html

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.

116k tokens
context cost
the whole folder, loaded on every use
31
files
ships runnable scripts
0
copies elsewhere
how many repositories repackaged it
14221
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep --skill paper-poster-html

What comes with it

447 314 bytes besides the instruction
DESIGN_FINAL.md
IMPLEMENTATION_CONVENTIONS.md
LICENSES/posterly-MIT.txt
NOTICE.md
scripts/_posterly/__init__.py
scripts/_posterly/canvas.py
scripts/_posterly/measure.py
scripts/_posterly/polish.py
scripts/_posterly/preflight.py
scripts/_posterly/render.py
scripts/_posterly/textutil.py
scripts/_posterly/verify_final.py
scripts/asset_check.py
scripts/extract_pdf_figures.py
scripts/poster_check.py
scripts/preprocess_figures.py
scripts/render_preview.py
scripts/run_gates.py
scripts/style_check.py
templates/COMPONENTS.md
templates/README.md
templates/landscape_4col.html
templates/landscape_hero.html
templates/portrait_2col.html
templates/tokens/acl.json
templates/tokens/cvpr.json
templates/tokens/generic.json
templates/tokens/iclr.json
templates/tokens/icml.json
templates/tokens/neurips.json

What it tells the agent to use

found in the instruction text
Bash runs shell commands — read the instruction before connecting
WebFetch fetches pages from the network
WebSearch reads your files

The instruction itself

20 sections, as written by the author

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:

  • 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-5.6-sol, 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

  • Resume: if poster_html/POSTER_STATE.json exists with status: 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 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.

  • 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

  • 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".

  • 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.

  • 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 via

inkscape/pdf2svg if available, else rasterize ≥ 2× rendered px).

  • PDF-only: extract_pdf_figures.py contact-sheet + auto to list candidate

regions → pick crops (🚦 human confirms crop choices) → crop at 300–450 DPI.

  • 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:

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.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.

How to use it

Copy the folder

Take wanshuiyin/paper-poster-html from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.