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Paper2html

Community
cnfjlhj
paper2html

Use when turning an academic paper PDF/arXiv/OpenReview page/local LaTeX source into a single-file Chinese HTML deep-reading page, especially when the user wants a Cheat-Sheet style explanation, KaTeX formulas, figure/table evidence, validation, or a publishable research page.

Overview

Publishercnfjlhj
Repositoryai-collab-playbook
Skill namepaper2html
Stars
455
Forks
44
Bundled files
10
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.

  • 10 bundled files

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

  • Open source

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

Installation

Install the Paper2html 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/cnfjlhj/ai-collab-playbook/tree/main/skills/full/paper2html
  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/cnfjlhj/ai-collab-playbook.git /tmp/ai-collab-playbook
mkdir -p .claude/skills
cp -r /tmp/ai-collab-playbook/skills/full/paper2html .claude/skills/paper2html
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Paper2html 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 Paper2html 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 Paper2html 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.

Paper2HTML

Overview

Create one self-contained Chinese index.html that lets the reader understand the paper without reopening the PDF. Open with a compact, source-faithful story, then deliver a clear Cheat-Sheet page with evidence boundaries, KaTeX formulas, complete figure coverage, reviewer-level analysis, and no decorative complexity.

Every completed page ends its substantive analysis with all seven MIT Other Discussion Roles. Each role shows the original English prompt verbatim and a source-grounded Chinese response.

Use scripts first. Keep this skill as workflow and quality gates, not a long prompt.

Scripted Path

bash
cd /path/to/paper2html

# 1) Create workspace, notes, and starter index.html
python scripts/bootstrap_paper2html.py \
  --title "Paper Title" \
  --slug paper-slug \
  --out /path/to/output/paper-slug-html-YYYYMMDD \
  --arxiv 2504.07952 \
  --github https://github.com/org/repo \
  --publish-to topic/series/paper-slug

# 2) Inventory TeX/source when available
python scripts/tex_inventory.py \
  /path/to/output/source/unpacked \
  --out /path/to/output/notes/tex-inventory.md \
  --json /path/to/output/notes/tex-inventory.json

# 3) Optimize figures and create data URIs for single-file HTML
python scripts/optimize_paper_images.py \
  paper_figures/*.pdf paper_figures/*.png \
  --out-dir /path/to/output/assets/optimized \
  --width 1600 \
  --data-uri-json /path/to/output/assets/data-uris.json

# 4) Optional: ask Gemini for a visual draft on a copy
scripts/gemini_frontend_pass.sh \
  /path/to/output/index.html \
  --reference /path/to/good-reference.html \
  --timeout 240

# Inspect the Gemini request without making a model call
scripts/gemini_frontend_pass.sh /path/to/output/index.html --dry-run

# 5) Validate the completed page
scripts/validate_paper_html.sh /path/to/output/index.html --public

# 6) Optional publication through a compatible research-page importer
PAPER2HTML_BLOG_ROOT=/path/to/blog \
PAPER2HTML_PUBLIC_BASE_URL=https://example.com/research \
scripts/publish_paper2html.sh \
  /path/to/output/index.html \
  --to topic/series/paper-slug \
  --title "Paper Title" \
  --description "One sentence summary" \
  --tags "paper-reading,agent" \
  --ship

Workflow

  1. Freeze the source boundary

    • Treat user input as a locator for one paper, then look for public adjacent artifacts: arXiv source/PDF, OpenReview, proceedings, project page, GitHub, appendix, dataset cards, and model cards.
    • Run bootstrap_paper2html.py when starting from a known title, arXiv id, or GitHub repository. Otherwise manually create the same notes/source-boundary.md.
    • Separate reading sources from public evidence sources. Public pages must not expose local paths, private filenames, review metadata, private prompts, or run logs.
    • If public publishing is requested and no public paper source exists, generate private HTML only.
  2. Extract the material

    • Prefer TeX/source packages over PDF-only reading. Run tex_inventory.py when source exists.
    • Build a material pack covering metadata, outline, claims, notation, equations, algorithms, experiments, appendix details, and public links.
    • Use PDF rendering for visual verification and figure/table placement. pdftotext is only an auxiliary index.
    • Record every discovered paper figure in notes/figure-table-map.md. For each item, capture its source/page, original caption, what the visual itself shows, and either the exact data-figure basename used in HTML or an explicit waiver reason.
    • Include every paper figure by default. Figures that do not fit the main narrative go in a 完整图谱 / Figure Gallery appendix section.
    • Extract reproducibility details: datasets, splits, models, baselines, metrics, prompts, tool/prover/solver versions, decoding, timeouts, retries, and appendix-only settings.
  3. Ground against the human knowledge boundary

    • Before writing, complete notes/grounding-anchor.md using public sources beyond the focal paper's related-work section.
    • Record: the prior human knowledge boundary with solved / partially solved / unsolved status and public links; the paper's real contribution relative to that boundary; and a short writing anchor showing which sections establish the contribution versus repeat source-faithful background.
    • If a claim rests only on model memory, mark it as unverified. Do not manufacture novelty for incremental work; for surveys, describe the organizational contribution.
    • The grounding anchor is an internal working note. Public links used from it must also appear in Reference / Evidence.
  4. Write the page

    • Use assets/cheatsheet-template.html as the visual skeleton and organize the page around the grounding anchor's real-contribution statement, not the paper's narrative order alone.
    • Use the fixed section order unless the paper requires a small adjustment: 先讲一个故事, 故事与技术如何对应, 先给结论, 研究动机, 数学表示及建模, 算法流程/方法, 实验设计, 实验结果, 我的评论, One More Thing, Other Discussion Roles / 其他讨论角色, Reference / Evidence.
    • Open with a compact protagonist/system, goal, obstacle, insufficient obvious explanation, and the paper's turning point. Prefer a sourced discovery or experiment; otherwise label the opening as a thought experiment or teaching analogy. Never invent author dialogue, motivation, chronology, or results.
    • Follow the story with a short bridge mapping its actors and conflict to the paper's actual concepts, method, and evidence, clearly distinguishing analogy from source fact.
    • Add a short version note near the top with reading basis and retrieval date, without local paths.
    • Bind each major claim to evidence and state boundaries when evidence does not support a stronger conclusion.
    • Use Chinese by default; keep paper terms, model names, datasets, commands, and identifiers in English.
    • Use KaTeX for all math. Inline math uses \(...\) or $...$; block math uses $$...$$ or \[...\].
    • Captions may claim only what the displayed figure/table itself shows. Put broader interpretation in a nearby callout.
    • Mark every included paper figure as <figure data-figure="basename.ext">.
    • One More Thing is for bounded research insight, not unsupported extrapolation.
    • Read references/other-discussion-roles.md and follow its rendering contract. The discussion-role section is mandatory, is the final substantive section, and may be followed only by Reference / Evidence.
    • Include all seven roles exactly once and in source order: Scientific Peer Reviewer, Archaeologist, Academic Researcher, Industry Practitioner, Hacker, Private Investigator, Social Impact Assessor.
    • Preserve every role's complete English prompt and exact data-discussion-role marker. Responses must perform the prompt rather than summarize it, distinguish facts from hypotheses, and never fabricate newer papers, author history, implementation runs, product facts, or impact claims.
    • Do not contact authors or any external person without explicit user approval, even though the preserved Private Investigator prompt mentions contact.
  5. Use the optional Gemini visual pass carefully

    • gemini_frontend_pass.sh writes index.gemini-draft.html; it must never edit the source HTML.
    • Gemini may improve hierarchy, spacing, typography, callouts, responsive behavior, and component composition. It is not a factual source.
    • The script attaches the source boundary, material pack, TeX inventory, figure map, and grounding anchor when present. It requires preservation of role markers, verbatim prompts, figure markers, and evidence boundaries.
    • The primary agent must review and selectively merge useful visual changes back into index.html.
  6. Validate

    • Run scripts/validate_paper_html.sh <index.html> from this skill.
    • It checks placeholders, public-path leaks, all seven role markers and verbatim prompts, response substance, final-section order, figure inventory/waiver coverage, KaTeX rendering, desktop/mobile screenshots, and mobile horizontal overflow.
    • Treat skipped browser checks as partial verification, not a full pass.
    • Deliver user-visible artifacts through the active collaboration channel when supported, and report the validation result.
  7. Publish

    • The optional publisher requires a target repository with a compatible pnpm research:publish command and PAPER2HTML_BLOG_ROOT.
    • Use --check for import plus local validation without commit/push.
    • --ship validates, imports, formats, checks, commits, pushes, watches deployment, and verifies the public URL. Use it only when the user explicitly requested those history and remote changes.
    • The target repository owns staging, commit, and deployment behavior.

Template

  • assets/cheatsheet-template.html is the fixed visual skeleton. Copy it into the output directory, then fill content and embed images.
  • Keep the template restrained and reading-focused. Do not turn paper pages into marketing landing pages.

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 Paper2html AI skill do?

Use when turning an academic paper PDF/arXiv/OpenReview page/local LaTeX source into a single-file Chinese HTML deep-reading page, especially when the user wants a Cheat-Sheet style explanation, KaTeX formulas, figure/table evidence, validation, or a publishable research page.

Why use Paper2html on TypingMind?

Because you install it once and use it with any model. Paper2html 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 Paper2html in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cnfjlhj/ai-collab-playbook/tree/main/skills/full/paper2html. 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 Paper2html?

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 Paper2html?

As many as you like. As long as a model supports skills, you can use Paper2html with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Paper2html AI skill free?

It is published on GitHub by cnfjlhj. Check the repository for licensing terms. 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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