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Paper Micro Unit Generator

Community
yushui2022
paper-micro-unit-generator

按修复队列重写反复不通过的论文章节,或按明确要求生成旧版与演练草稿脚手架。当章节连续审计失败需要局部重写、或用户明确要求快速草稿而非正式论文时使用。

Overview

Publisheryushui2022
RepositoryMathModel-Skill
Skill namepaper-micro-unit-generator
Stars
455
Forks
28
Bundled files
4
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.

  • 4 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 Micro Unit Generator 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-micro-unit-generator
  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-micro-unit-generator .claude/skills/paper-micro-unit-generator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Paper Micro Unit Generator 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 Micro Unit Generator 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 Micro Unit Generator 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 Micro-Unit Generator

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.

Use this skill only for local repair or an explicitly requested legacy scaffold. paper-formal-writer remains the sole formal author and the only producer of paper_output/final_paper_source.md and paper_output/final_paper.docx.

Entry Check

For formal repair, run:

bash
python "<SKILL_ROOT>/paper-workflow-orchestrator/scripts/workflow_guard.py" --skill paper-micro-unit-generator

Continue only when S0-S6 pass and paper_output/qa/repair_queue.json contains at least one item whose strategy is micro-repair. Do not create a repair queue manually to bypass the two-failure threshold.

Legacy and quickstart modes are installation or old-model scaffolds. They do not satisfy S7 and do not require the formal repair queue.

Formal Repair Mode

  1. Read paper_output/plan/writing_plan.json, paper_output/context/authoring_state.json, paper_output/qa/draft_audit.json, and paper_output/qa/repair_queue.json.
  2. Select only queued micro-repair items. Keep the issue ID, section, failure category, expected evidence, and attempt count intact.
  3. Read the affected section draft and only the evidence needed by that issue. Do not rewrite unrelated sections.
  4. Write repair material to paper_output/drafts/repairs/<issue-id>.md. The material must contain concrete replacement text, its target location, and any required evidence marker.
  5. Apply the replacement to the queued section draft. Preserve valid headings, equations, figure/table identifiers, citations, and evidence markers.
  6. Re-run the section audit:
bash
python "<SKILL_ROOT>/paper-formal-writer/scripts/validate_authoring.py" --section <section-id>
  1. Return to paper-formal-writer or paper-workflow-orchestrator. This skill must not assemble the formal manuscript or generate Word.

If the same blocking category reaches the third changed attempt, respect authoring_state.status = BLOCKED. Report the evidence or requirement causing the failure and suggest Lite as a user decision; never switch editions automatically.

Repair Rules

  • Repair the failed claim or paragraph, not the whole paper.
  • Use only computed values and paths present in the current evidence chain.
  • Preserve <!-- mathmodel-evidence: ... --> markers until deterministic assembly removes them.
  • Do not invent data, citations, completed experiments, or successful validation.
  • Do not expose workflow language in paper prose.
  • Do not renumber existing figure, table, equation, or bibliography references.
  • A repair is complete only after validate_authoring.py --section returns PASS.

Legacy Mode

The old command names remain available. With no arguments they write only to paper_output/drafts/legacy/:

bash
python "<SKILL_ROOT>/paper-micro-unit-generator/scripts/generate_all_offline.py"
python "<SKILL_ROOT>/paper-micro-unit-generator/scripts/merge.py"

Expected outputs:

text
paper_output/drafts/legacy/micro_units/*.txt
paper_output/drafts/legacy/generate_log.json
paper_output/drafts/legacy/legacy_scaffold.md
paper_output/drafts/legacy/legacy_scaffold.docx
paper_output/drafts/legacy/legacy_ref_check.md

tasks.json is required only for this legacy batch path. The merge is deterministic and preserves existing reference numbers.

For the built-in smoke test, the orchestrator passes --output-root paper_output/quickstart --stem quickstart_scaffold; all smoke-test files stay under paper_output/quickstart/.

Never write final_paper.md, final_paper_source.md, final_paper.docx, or similarly formal names from legacy/quickstart mode.

Template Library

Read references/micro-unit-library.md only when a queued repair needs a fine-grained paragraph/sentence pattern or when the user explicitly requests legacy scaffolding. Do not load the full 200+ template library for ordinary section writing.

Handoff

After a formal repair, run workflow status and update persistent context:

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

The guard report is authoritative if memory and current artifacts disagree. Read the persistent snapshot at paper_output/context/workflow_memory.json before resuming a repair.

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 Micro Unit Generator AI skill do?

按修复队列重写反复不通过的论文章节,或按明确要求生成旧版与演练草稿脚手架。当章节连续审计失败需要局部重写、或用户明确要求快速草稿而非正式论文时使用。

Why use Paper Micro Unit Generator on TypingMind?

Because you install it once and use it with any model. Paper Micro Unit Generator 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 Micro Unit Generator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yushui2022/MathModel-Skill/tree/standard/skills/paper-micro-unit-generator. 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 Micro Unit Generator?

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 Micro Unit Generator?

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

Is the Paper Micro Unit Generator 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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