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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.

Overview

Publisherwanshuiyin
RepositoryAuto-claude-code-research-in-sleep
Skill namepaper-poster-html
Stars
18.3K
Forks
1.5K
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • 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.
    https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/skills-codex-gemini-review/paper-poster-html
  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/skills-codex-gemini-review/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.

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/*.

Paper Poster (HTML): measurement-gated poster generation

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).

Reviewer constants (overlay)

  • REVIEWER_MODEL = gemini-review — Gemini invoked through the local gemini-review MCP bridge.
  • Fresh review job per call — start each review with 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.
  • The Gemini bridge cannot read your local files — paste the relevant content into the prompt, and pass rendered posters via imagePaths.
  • If the gemini-review bridge is unavailable, stop and tell the user what to configure. Do not silently degrade the cross-model reviews into self-review.

Phase 1 step 2 — Cross-model content audit (Gemini)

text
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.

Phase 6 — Final review (Gemini, multimodal)

All hard gates PASS + polish warnings zero-or-waived + executor visual score ≥ 9 first. Then:

text
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.

Review tracing

Save both review jobs' raw responses per ../../shared-references/review-tracing.md to .aris/traces/paper-poster-html/<date>_run<NN>/.

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.

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/skills-codex-gemini-review/paper-poster-html. TypingMind reads its SKILL.md 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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