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.
npx skills add https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep --skill paper-poster-html
> Override for Codex users who want Gemini, not a second Codex/Codex-MCP reviewer, to act as the reviewer. Install this package after skills/skills-codex/*.
> Gemini overlay assurance: review_independence: cross-family and acceptance_status: accepted.
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 in the mainline
skill directory); ARIS adds style discipline gates, figure-provenance gates, the
cross-model review loop, and the anti-patch-loop fix vocabulary.
This overlay is identical to skills/skills-codex/paper-poster-html/ except that the
two cross-model review calls go to Gemini through the local gemini-review MCP
bridge instead of a spawned GPT reviewer agent. Follow the base mirror for everything
not restated here (phases, gates, fix vocabulary, figure provenance, output contract).
gemini-review — Gemini invoked through the localgemini-review MCP bridge.
mcp__gemini-review__review_start; never reuse a prior review job across review
boundaries. Save the returned jobId, poll mcp__gemini-review__review_status with
a bounded waitSeconds until done=true, and treat the completed payload's
response as the reviewer output.
prompt, and pass rendered posters via imagePaths.
gemini-review bridge is unavailable, stop and tell the user what toconfigure. Do not silently degrade the cross-model reviews into self-review.
mcp__gemini-review__review_start:
prompt: |
Audit a conference-poster content plan against its source paper.
## Poster content plan
[PASTE poster_html/POSTER_CONTENT_PLAN.md]
## Paper source (relevant sections)
[PASTE the paper sections backing the plan's claims — abstract, headline
results tables, method equations, theorem statements]
For EVERY claim, number, equation, and attribution in the plan, output one row:
| claim on poster | paper location | paper says (verbatim) | match? |
with match ∈ {OK, NUMERIC-MISMATCH, OVERCLAIM, MISSING-PRECONDITION,
NOT-IN-PAPER, SCOPE-NARROWED}. End with a count per category.
Poll review_status until done=true; save the response to
poster_html/CLAIM_EVIDENCE.md. Fix every non-OK row or record it as a
user-acknowledged tradeoff.
All hard gates PASS + polish warnings zero-or-waived + executor visual score ≥ 9
first. Then:
mcp__gemini-review__review_start:
imagePaths: ["poster_html/poster_preview.png"]
prompt: |
Final print-readiness audit of a conference poster (image attached).
## Final poster text content
[PASTE the text content extracted from poster_html/poster.html]
## Gate report summary
[PASTE the overall/hard_failures/warnings fields of poster_html/GATE_REPORT.json]
## Claim→evidence audit
[PASTE poster_html/CLAIM_EVIDENCE.md]
Check: (1) fidelity & overclaims RE-CHECKED on the 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, two-hue discipline, real paper figures central and inside their cards),
(4) gate-log coherence.
Verdict: PRINT-READY or NEEDS-FIX with a numbered, severity-ordered issue list.
Poll mcp__gemini-review__review_status with a bounded waitSeconds until done=true;
treat the completed payload's response as the reviewer verdict.
The reviewer recommends; it does not edit. Any fix → back through Phase 4/5 gates —
never straight to re-review.
Save both review jobs' raw responses per ../../shared-references/review-tracing.md to
.aris/traces/paper-poster-html/<date>_run<NN>/.
Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section. Transforms your writing process from solo effort to collaborative partnership.
Identifies high-quality leads for your product or service by analyzing your business, searching for target companies, and providing actionable contact strategies. Perfect for sales, business development, and marketing professionals.
Use this skill to query your Google NotebookLM notebooks directly from Claude Code for source-grounded, citation-backed answers from Gemini. Browser automation, library management, persistent auth. Drastically reduced hallucinations through document-only responses.
Efficient database search tool for bioRxiv preprint server. Use this skill when searching for life sciences preprints by keywords, authors, date ranges, or categories, retrieving paper metadata, downloading PDFs, or conducting literature reviews.
Query and analyze scholarly literature using the OpenAlex database. This skill should be used when searching for academic papers, analyzing research trends, finding works by authors or institutions, tracking citations, discovering open access publications, or conducting bibliometric analysis across 240M+ scholarly works. Use for literature searches, research output analysis, citation analysis, and academic database queries.
Access USPTO APIs for patent/trademark searches, examination history (PEDS), assignments, citations, office actions, TSDR, for IP analysis and prior art searches.
Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.
Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use hypothesis-generation; for creative ideation use scientific-brainstorming.
Take wanshuiyin/auto-claude-code-research-in-sleep-paper-poster-html from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
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.