Use when main results pass result-to-claim (claim_supported=yes or partial) and ablation studies are needed for paper submission.
日本語の概要は準備中です。原文の説明を表示しています。
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.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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.
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:
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
${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).gpt-6-astra, reasoning xhigh, fresh thread per review call
(mcp__codex__codex, never codex-reply across review boundaries).templates/tokens/generic.json (slate-blue #2D5F8B accent
#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.poster_html/ in the working directory.poster_html/POSTER_STATE.json exists with status: in_progress
(< 24 h), resume from the saved phase.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.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.{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.
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.
.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".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.🚦 Checkpoint: content plan + audit summary. Wait.
Source preference chain:
figures/ (vector SVG/PDF → convert to SVG via
inkscape/pdf2svg if available, else rasterize ≥ 2× rendered px).extract_pdf_figures.py contact-sheet + auto to list candidate
regions → pick crops (🚦 human confirms crop choices) → crop at 300–450 DPI.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--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.
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.)
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.
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.
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.
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.
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.
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.
run_gates.py output.design_decisions before "improving" anything.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.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).
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}/
/paper-talk / /slides-polish.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Use when main results pass result-to-claim (claim_supported=yes or partial) and ablation studies are needed for paper submission.
日本語の概要は準備中です。原文の説明を表示しています。
Use when main results pass result-to-claim (`claim_supported = yes` or `partial`) and ablation studies are needed for paper submission. A secondary Codex agent designs ablations from a reviewer's perspective; the local executor reviews feasibility and implements.
日本語の概要は準備中です。原文の説明を表示しています。
Quick single-paper lookup via AlphaXiv LLM-optimized summaries with tiered source fallback. Use when user says "explain this paper", "summarize paper", pastes an arXiv/AlphaXiv URL, or provides a bare arXiv ID for quick understanding - not for broad literature search.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze ML experiment results, compute statistics, generate comparison tables and insights. Use when user says "analyze results", "compare", or needs to interpret experimental data.
日本語の概要は準備中です。原文の説明を表示しています。
Search, download, and summarize academic papers from arXiv. Use when user says "search arxiv", "download paper", "fetch arxiv", "arxiv search", "get paper pdf", or wants to find and save papers from arXiv to the local paper library.
日本語の概要は準備中です。原文の説明を表示しています。
Autonomously improve a generated paper via Claude review through claude-review MCP → implement fixes → recompile, for 2 rounds. Use when user says "改论文", "improve paper", "论文润色循环", "auto improve", or wants to iteratively polish a generated paper.
日本語の概要は準備中です。原文の説明を表示しています。