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Paper Skill

Community
cLin-c
paper-skill

End-to-end, Chinese-first academic research and publication workflow for reading papers, literature reviews, research framing, manuscript planning and writing, the eight-step paper-system workflow, Chinese-English translation, LaTeX, figures, citation verification, reporting-guideline checks, journal selection, peer review, rebuttals, revisions, and submission packages. Use for SCI, IEEE, Nature-family, IJCAI, TRO, theses, systematic reviews, clinical/observational/animal studies, and any task where claims, evidence, references, manuscript files, or journal requirements must be checked rather than invented.

Overview

PublishercLin-c
Repositorypaper-skill
Skill namepaper-skill
Stars
135
Forks
10
Bundled files
96
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.

  • 96 bundled files

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

  • Open source

    Published by cLin-c on GitHub. Read the source before you install it.

Installation

Install the Paper Skill 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/cLin-c/paper-skill/tree/main
  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/cLin-c/paper-skill.git \
  .claude/skills/paper-skill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Paper Skill 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 Skill 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 Skill 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.

paper-skill

Turn research materials into evidence-calibrated academic outputs. Treat the manuscript as a traceable system of claims, evidence, citations, figures, and submission requirements—not as a free-form writing task.

Non-negotiable rules

  1. Never invent data, experiments, statistics, quotations, references, DOI values, journal policies, author contributions, ethics approvals, funding, or revision locations.
  2. Separate three states explicitly: VERIFIED, UNVERIFIED, and AUTHOR_INPUT_NEEDED.
  3. Preserve scientific meaning before improving style. Flag contradictions instead of silently repairing them.
  4. Map every material claim to evidence. Downgrade or remove unsupported claims.
  5. Verify time-sensitive journal policies and bibliographic metadata against primary sources when tools permit. Record source and access date.
  6. Never promise acceptance, impact factor outcomes, or detector evasion. Quality scores are internal triage, not publication predictions.
  7. Keep a change ledger for manuscript edits: location, original, revision, reason, evidence affected.

Read references/verification-policy.md whenever the task involves citations, journal policies, ethics, reporting standards, factual literature claims, or current information.

Route the task

First classify the request, then load only the indicated resources. For “论文体系8步”, “八步论文流程”, or continuing a numbered stage of that workflow, select paper-system; ordinary paragraph edits remain scoped to write.

RouteTypical requestsRequired resources
paper-system (eight-step)论文体系八步:故事线、核心实验、完整实验、中文稿、文献核查、翻译排版、绘图、精简定稿;start or resume any stageworkflows/paper-system-eight-steps.md, then only the selected stage's prompts
readRead, summarize, critique, bilingual notesreferences/paper-writing-prompts.md
literatureSearch strategy, review, gap map, related workworkflows/literature-review.md, citation-integrity.md
write (plan-write)Storyline, outline, draft, rewrite, polish, translate; personal writing style and full-manuscript structureApply the writing profile below, the matching section guide, and references/output-contracts.md
figuresMethod diagram, result figure, caption, figure auditreferences/figure-prompts.md, workflows/figure-audit.md
reviewPre-submission review, rejection risks, adversarial auditquality-gates.md, references/output-contracts.md
rebuttalReviewer response, R&R, rebuttal, revision verificationreferences/output-contracts.md, quality-gates.md
citationsDOI audit, hallucination scan, claim-source matchcitation-integrity.md, workflows/reference-tools.md
journalVenue selection, transfer, policy, formattingjournal-strategy.md, references/verification-policy.md
submit (submission)Cover letter, highlights, declarations, final packageworkflows/submission-package.md, references/output-contracts.md
reportingCONSORT, PRISMA, STROBE, ARRIVE, CARE, SPIRITreferences/reporting-guidelines.md, references/verification-policy.md
full-verifyDOI lookup, journal policy evidence, manuscript/rebuttal consistencyworkflows/full-verification.md

For IEEE Transactions on Robotics, also load references/venues/ieee-tro.md. Treat every venue profile as a dated starting point and re-check its official links before submission.

For a multi-route request, load the smallest combination that covers it. Do not load the general prompt bank unless the routed resources are insufficient; then use references/prompt-bank.md.

Personal writing style and manuscript structure

For write, use references/personal-writing-style.md as the default writing profile. Requests such as “按我的论文风格”, “个人写作风格”, or “全文结构规范” select this profile explicitly; a structural review can use it without rewriting the manuscript. Read its application rules and the sections relevant to the requested scope.

Build the argument from task/scientific question → capability needed → concrete obstacle and consequences → design principle → mechanism → evidence → scoped conclusion. Explain requirements backward from the goal, then describe the method forward along its actual dependencies. Keep claims, contributions, methods, figures, and evidence aligned.

Explicit author preferences and verified venue requirements take precedence over these stylistic defaults. Preserve scientific meaning and evidence limits. Adapt to method, discovery, or theory papers; do not impose fixed counts of sections, contributions, modules, experiments, or sentences. A paragraph edit stays a paragraph edit. Deliver the requested prose first, with proportional verification notes and author questions outside the manuscript.

Section-writing selector

For worked patterns, load examples/section-rewrites.md. The examples are synthetic; reuse reasoning structure, never their facts.

Core workflow

1. Intake and provenance

Create a compact intake record:

  • task and intended artifact;
  • paper type and discipline;
  • target venue and deadline, if supplied;
  • authoritative source files and version order;
  • available evidence: datasets, experiments, figures, tables, code, references;
  • required output format;
  • unresolved author decisions.

If several manuscript versions exist, ask which is authoritative only when it cannot be inferred safely. Never merge conflicting values silently.

2. Build the argument model

Before drafting or reviewing, state:

  • research question;
  • one-sentence central claim;
  • supporting claims warranted by the research and requested scope;
  • evidence supporting each claim;
  • scope conditions and limitations;
  • novelty relative to named prior work.

Use IDs (C1, E1, R1, F1) so claims, evidence, references, and figures can be traced.

3. Choose the operation

  • Draft: build from the argument model, not from generic section templates.
  • Rewrite: diagnose structure first; preserve facts, equations, citations, and numbers.
  • Translate: translate meaning and rhetorical function; keep a terminology table and ambiguity log.
  • Review: separate fatal validity issues, major evidence gaps, minor clarity issues, and optional improvements.
  • Rebuttal: map every reviewer point to response, manuscript change, exact location, and verification status.
  • Submission: reconcile title, abstract, highlights, figures, declarations, and cover letter against the manuscript.

4. Verify

Run checks proportional to the task:

  • claim ↔ evidence;
  • claim ↔ citation;
  • text ↔ figure/table;
  • response letter ↔ revised manuscript;
  • journal policy ↔ official current source;
  • reporting checklist ↔ study design;
  • terminology, symbols, units, sample sizes, and metric consistency.

When a submission-package JSON is available, run:

bash
python tools/paper_skill_gate.py path/to/package.json

Use --format json for machine-readable output and --strict to treat warnings as a nonzero exit.

For a complete online and cross-file verification run, use:

bash
python tools/run_full_workflow.py path/to/workflow-config.json --output full-report.json

5. Deliver with uncertainty visible

Every substantial output must include:

  1. the requested artifact;
  2. evidence/traceability notes appropriate to the task;
  3. unresolved items labeled AUTHOR_INPUT_NEEDED;
  4. verification summary—what was checked, what was not, and why;
  5. next gate, if the artifact is part of a longer workflow.

Follow the schemas in references/output-contracts.md.

File and artifact handling

  • Preserve the original; write revisions to a new file unless replacement is explicitly requested.
  • For DOCX, PDF, spreadsheets, or slides, use the relevant artifact skill and render/inspect the final output.
  • For LaTeX, compile when a TeX toolchain is available; inspect warnings, references, floats, and rendered pages.
  • For figures, inspect axes, units, legends, panel labels, sample sizes, uncertainty, and caption agreement.
  • Never claim a file was modified, compiled, or visually checked unless it actually was.

Completion gates

Do not call work “submission-ready” while any critical item remains:

  • unsupported central claim;
  • unresolved contradictory data;
  • fabricated or unverified high-risk citation;
  • missing ethics/registration information when applicable;
  • reviewer response without a matching manuscript change;
  • figure/table inconsistent with the text;
  • journal requirement not checked against a current official source.

Use READY, READY_WITH_WARNINGS, or NOT_READY; always state the blocking evidence.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

and 36 more files.

Frequently asked questions

What does the Paper Skill AI skill do?

End-to-end, Chinese-first academic research and publication workflow for reading papers, literature reviews, research framing, manuscript planning and writing, the eight-step paper-system workflow, Chinese-English translation, LaTeX, figures, citation verification, reporting-guideline checks, journal selection, peer review, rebuttals, revisions, and submission packages. Use for SCI, IEEE, Nature-family, IJCAI, TRO, theses, systematic reviews, clinical/observational/animal studies, and any task where claims, evidence, references, manuscript files, or journal requirements must be checked rath...

Why use Paper Skill on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cLin-c/paper-skill/tree/main. 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 Paper Skill?

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 Skill?

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

Is the Paper Skill AI skill free?

Yes. It is published on GitHub by cLin-c 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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