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Thesis Chapter Reconstructor

Community
WILLOSCAR
thesis-chapter-reconstructor

围绕毕业论文主线重构章节:把原论文式叙事改成学位论文式叙事,重写章节目标、内容比重、前后承接与论证方式。

Overview

PublisherWILLOSCAR
Repositoryresearch-units-pipeline-skills
Skill namethesis-chapter-reconstructor
Stars
513
Forks
39
Bundled files
5
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.

  • 5 bundled files

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

  • Open source

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

Installation

Install the Thesis Chapter Reconstructor 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/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/thesis-chapter-reconstructor
  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/WILLOSCAR/research-units-pipeline-skills.git /tmp/research-units-pipeline-skills
mkdir -p .claude/skills
cp -r /tmp/research-units-pipeline-skills/.codex/skills/thesis-chapter-reconstructor .claude/skills/thesis-chapter-reconstructor
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Thesis Chapter Reconstructor 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 Thesis Chapter Reconstructor 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 Thesis Chapter Reconstructor 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.

Thesis Chapter Reconstructor

Triggers & routing

  • Trigger: 章节重构, chapter reconstructor, 毕业论文主线重构, paper 变 thesis, 章节改写.
  • Use when: 你已经知道每份材料在毕业论文中的角色,但当前章节仍然像原论文翻译、拼接或卖点展示。
  • Skip if: 当前只是做局部句子润色,或章节角色尚未确定。
  • Network: none.
  • Guardrail: 不是翻译论文;不是简单删减;必须围绕毕业论文主线重写章节目标与承接关系。

这是 graduate-paper pipeline 的核心重构 skill。

Inputs

  • codex_md/chapter_role_map.md
  • codex_md/question_list.md
  • 各章 Markdown 草稿

Outputs

  • 重构后的章节 Markdown
  • codex_md/chapter_rewrite_rules.md

Load Order

Always read:

  • references/overview.md
  • references/rewrite_rules.md
  • references/examples_good.md
  • references/examples_bad.md

Machine-readable contract:

  • assets/chapter_rewrite_contract.json

Workflow

  1. 先回答本章在毕业论文里要解决什么问题
  2. 判断当前章节是否仍沿用原论文卖点叙事
  3. 明确哪些内容要保留、弱化、上移、下沉
  4. 重写章首导言、章节承接、小节职责
  5. 把重构规则写入 chapter_rewrite_rules.md

Key Rule

任何一章如果还像:

  • paper 翻译
  • 实验报告
  • 方法宣传
  • 多篇论文拼接

都说明这一步还没做完。

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 Thesis Chapter Reconstructor AI skill do?

围绕毕业论文主线重构章节:把原论文式叙事改成学位论文式叙事,重写章节目标、内容比重、前后承接与论证方式。

Why use Thesis Chapter Reconstructor on TypingMind?

Because you install it once and use it with any model. Thesis Chapter Reconstructor 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 Thesis Chapter Reconstructor in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/thesis-chapter-reconstructor. 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 Thesis Chapter Reconstructor?

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 Thesis Chapter Reconstructor?

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

Is the Thesis Chapter Reconstructor AI skill free?

It is published on GitHub by WILLOSCAR. 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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