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Paper Formal Writer

Community
yushui2022
paper-formal-writer

正式论文的唯一撰写路径,制定写作计划、按章节写作与审计、全文统一改写,并生成原生公式 Word 与 PDF 渲染检查。当运行证据已通过校验、需要撰写或续写完整竞赛论文、或需要产出 final_paper.docx 与渲染 PDF 时使用。

Overview

Publisheryushui2022
RepositoryMathModel-Skill
Skill namepaper-formal-writer
Stars
455
Forks
28
Bundled files
17
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.

  • 17 bundled files

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

  • Open source

    Published by yushui2022 on GitHub. Read the source before you install it.

Installation

Install the Paper Formal Writer 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/yushui2022/MathModel-Skill/tree/standard/skills/paper-formal-writer
  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/yushui2022/MathModel-Skill.git /tmp/MathModel-Skill
mkdir -p .claude/skills
cp -r /tmp/MathModel-Skill/skills/paper-formal-writer .claude/skills/paper-formal-writer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Paper Formal Writer 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 Formal Writer 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 Formal Writer 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 Formal Writer

Resolve <SKILL_ROOT> to the parent directory of this loaded skill folder, using its actual absolute path. Run commands from the contest project root, never from the installed skill directory. The placeholder is not a shell environment variable.

This skill is the sole formal author and final manuscript producer in Standard. It turns a fresh S6 evidence chain into an audited Markdown source and native-equation Word document.

Entry

Run before formal work:

bash
python "<SKILL_ROOT>/paper-workflow-orchestrator/scripts/workflow_guard.py" --skill paper-formal-writer

S0-S6 must pass. If workflow state is uncertain, run workflow_guard.py --status and follow its current artifact report rather than memory.

Single Formal Path

text
S6 evidence PASS
-> writing plan
-> chapter drafts
-> chapter audits
-> queued local repair when required
-> deterministic assembly
-> Agent global revision
-> final Markdown audit
-> formal DOCX
-> S8 format/render gate

Do not promote quickstart, legacy, or mechanically merged micro-unit output into a formal filename.

Prepare

  1. Build the evidence-aware outline:
bash
python "<SKILL_ROOT>/paper-formal-writer/scripts/build_paper_outline.py"
  1. Prepare adaptive authoring:
bash
python "<SKILL_ROOT>/paper-formal-writer/scripts/prepare_authoring.py" --mode auto

Default delivery is competition: plan at least 14000 effective characters, with a main-paper floor of 8000 and at least 18 rendered pages before appendices. These are adjustable project defaults, not contest rules. Verify the actual contest's page cap and counting rules before writing. auto therefore selects section; a short report needs --delivery short-report --scope-reason "user-requested scope". Custom floors use --min-pages / --min-body-chars with a reason; never lower them merely to turn a failed check green.

Read references/competition-scope.md when preparing scope or repairing a short manuscript. Several authoring turns may produce one coherent chapter; preserve evidence and notation between turns instead of compressing the paper into one response.

The authoritative S7 contracts are:

  • paper_output/plan/writing_plan.json
  • paper_output/context/authoring_state.json
  • paper_output/qa/draft_audit.json
  • paper_output/qa/repair_queue.json

Draft And Audit

For numerical results, read references/metric-claims.md. Formal preparation binds each required metric to its unit, sample/split, CSV row and successful experiment. Missing metadata blocks S7 instead of being guessed from prose. Maintain a sibling .claims.json for each draft and the final source; use an empty claims list for chapters without numbers. Only declared conversions are allowed. Never promote a feasible solution to an optimal one without recorded verification.

Write only to the path recorded for the current unit in authoring_state.json. Each chapter must be a complete, coherent section and must include its required removable marker:

html
<!-- mathmodel-evidence: evidence-id-1, evidence-id-2 -->

Audit every changed draft:

bash
python "<SKILL_ROOT>/paper-formal-writer/scripts/validate_authoring.py" --section <section-id>

Routing is deterministic:

  • global repeats the same blocking category twice: switch to section.
  • A section fails once: rewrite that section.
  • A section repeats the same category twice: paper-micro-unit-generator may repair only queued locations.
  • The third changed attempt with the same category: S7 becomes BLOCKED; report the cause and suggest Lite as a user choice without switching automatically.

Audits block short drafts, missing chapters/evidence/numbers, broken formulas, placeholders, duplicate prose, broken figure/table references, and internal workflow language.

Assemble And Revise

After all active units pass:

bash
python "<SKILL_ROOT>/paper-formal-writer/scripts/assemble_sections.py"
python "<SKILL_ROOT>/paper-formal-writer/scripts/validate_authoring.py" --assembled

The assembler follows outline order, strips evidence comments, merges claim sidecars, and preserves existing numbering. It writes paper_output/drafts/assembled_draft.md.

Then read the entire assembled draft and evidence chain, and rewrite the paper globally into:

text
paper_output/final_paper_source.md

This pass must unify terminology, notation, transitions, argument order, citations, captions, and conclusions. Copying the assembled draft unchanged is rejected. Update final_paper_source.claims.json with the revised quotes and exact source hash; do not copy stale bindings. Sidecars remain outside the rendered manuscript.

Run the final source audit:

bash
python "<SKILL_ROOT>/paper-formal-writer/scripts/validate_authoring.py" --final

Any change to evidence, writing plan, approved section draft, assembly, or final source invalidates downstream PASS state.

Word And S8

Only after authoring_state.status = PASS:

bash
python "<SKILL_ROOT>/paper-formal-writer/scripts/format_formal_docx.py"
python "<SKILL_ROOT>/paper-formal-writer/scripts/check_paper_format.py" --render required

Formal mode requires fresh S6 and S7 hashes. --allow-draft may create final_paper_draft.docx for layout diagnosis, but never satisfies S7/S8 or overwrites the formal DOCX.

format_formal_docx.py converts LaTeX to editable Word OMML and blocks failed conversion. check_paper_format.py checks dynamic length, required hierarchy, citations, evidence-linked figures/tables, formulas, duplicate prose, DOCX structure, LibreOffice PDF rendering, page count, and extractable text.

Writing Standard

  • Use 1 / 1.1 / 1.1.1 headings and complete CUMCM sections.
  • For each question, connect assumptions, variables, derivation, algorithm, computed values, validation, uncertainty, and conclusion.
  • Define symbols before formulas and explain every displayed result.
  • Cite and interpret every included figure/table.
  • Use only current evidence; never invent runs, values, sources, or validation.
  • Avoid copied or number-swapped paragraphs used only to meet length.
  • Keep implementation language out of body prose; reproducibility paths belong in the appendix.

References

Read only the reference needed for the current writing or repair decision.

Handoff

After each meaningful transition, run workflow status and update paper_output/context/workflow_memory.json:

bash
python "<SKILL_ROOT>/paper-workflow-orchestrator/scripts/workflow_guard.py" --status
python "<SKILL_ROOT>/context-memory-keeper/scripts/update_workflow_memory.py"

S8 is complete only when the machine-readable format report is PASS and fresh.

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 Paper Formal Writer AI skill do?

正式论文的唯一撰写路径,制定写作计划、按章节写作与审计、全文统一改写,并生成原生公式 Word 与 PDF 渲染检查。当运行证据已通过校验、需要撰写或续写完整竞赛论文、或需要产出 final_paper.docx 与渲染 PDF 时使用。

Why use Paper Formal Writer on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yushui2022/MathModel-Skill/tree/standard/skills/paper-formal-writer. 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 Formal Writer?

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 Formal Writer?

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

Is the Paper Formal Writer AI skill free?

Yes. It is published on GitHub by yushui2022 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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