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Research Writing Skill

CommunityPopular
zLanqing
research-writing-skill

Chinese-first research paper writing, revision, polishing, section drafting, rebuttal, peer-review response, thesis prose improvement, and manuscript argument planning. Use when the user asks to write or revise论文正文, abstracts, introductions, methods, results, discussion, conclusions, related work, responses to reviewers, LaTeX/Overleaf text, or academic prose. Preserve formulas, English paper titles, terms, citations, and measured results.

Overview

PublisherzLanqing
Repositorycodex-claude-academic-skills
Skill nameresearch-writing-skill
Stars
4K
Forks
222
Bundled files
20
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.

  • 20 bundled files

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

  • Open source

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

Installation

Install the Research Writing 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.
  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/zLanqing/codex-claude-academic-skills.git /tmp/codex-claude-academic-skills
mkdir -p .claude/skills
cp -r /tmp/codex-claude-academic-skills/research-writing-skill .claude/skills/research-writing-skill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Research Writing 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 Research Writing 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 Research Writing 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.

Research Writing Skill

Scope

Use this skill for research writing and revision when the output is prose, LaTeX, Markdown, or manuscript text. Use office-academic-skill instead when the main deliverable is Word/PPT. Use scientific-toolkit-skill when the task is primarily MATLAB, Python, plotting, statistics, simulation, or literature search.

Writing Principles

  • Default to Chinese academic expression unless the user requests English.
  • Preserve English titles, formulas, variables, methods, software names, citations, and reference entries.
  • Do not invent data, DOI, journal details, experiment settings, results, or author claims.
  • Separate 原文/已有数据, 用户确认内容, 根据上下文推断, and 建议性扩展.
  • Prefer verifiable technical statements over generic claims.
  • When revising, preserve the user's intended meaning and terminology unless a change is clearly needed.

Manuscript Workflow

For a new section or paper draft:

  1. Clarify target: journal/conference/thesis/course report, language, length, audience, and required format.
  2. Identify source material: paper notes, experiment results, figures, tables, MATLAB/Python outputs, references, advisor comments.
  3. Build an argument outline before full prose: problem, gap, method, evidence, contribution, limitation.
  4. Draft in coherent paragraphs, not empty slogan bullets.
  5. Add source or evidence labels for quantitative claims and literature claims.
  6. Revise for logic, specificity, terminology consistency, and citation accuracy.

For revision or polishing:

  • Keep claims tied to evidence.
  • Replace vague words such as "显著", "先进", "有效", "鲁棒" with measured conditions, comparison baselines, or remove them.
  • Check whether each paragraph advances the section's purpose.
  • Keep formulas with variable definitions, units, assumptions, and applicable conditions.
  • For experimental sections, state dataset/sample, hardware/software, parameters, metrics, baselines, and uncertainty when available.

Section Guides

Use these default moves unless the user's school or journal template overrides them:

  • Abstract: problem, method, experiment/data, key result, contribution.
  • Introduction: background, unresolved gap, why it matters, proposed approach, contributions.
  • Related work: organize by technical theme, compare assumptions and limitations, avoid simple paper-by-paper summaries.
  • Methods: model assumptions, variables, workflow, algorithm, implementation details needed for reproduction.
  • Experiments: data/source, platform/software, parameters, metrics, baseline, repeated trials, visualization plan.
  • Results and discussion: claim first, evidence second, mechanism/explanation third, limitation last.
  • Conclusion: answer the research question, summarize evidence, state limitations and next steps.

Bundled References

The folder references/paper-writing/ contains external writing checklists and section patterns adapted as references. Load only the relevant file when needed:

  • brainstorming_guide.md for turning an unclear idea into a paper plan.
  • section_rhetorical_moves/ for section structure.
  • writing_checklists/ for self-diagnosis.
  • figure_templates/ for figure planning.
  • author_profile/ for editorial heuristics.

These references come from an external systems/networking-oriented repository. Treat them as optional craft guidance, not binding rules, and adapt them to光电信息科学与工程, optics, optoelectronics, sensing, communication, signal processing, and MATLAB simulation work.

Final Checks

Before delivery, state:

  • What was drafted or revised.
  • Which source material was used.
  • Which claims still need user-provided data or citation support.
  • Any uncertainty about terminology, parameters, or references.

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 Research Writing Skill AI skill do?

Chinese-first research paper writing, revision, polishing, section drafting, rebuttal, peer-review response, thesis prose improvement, and manuscript argument planning. Use when the user asks to write or revise论文正文, abstracts, introductions, methods, results, discussion, conclusions, related work, responses to reviewers, LaTeX/Overleaf text, or academic prose. Preserve formulas, English paper titles, terms, citations, and measured results.

Why use Research Writing Skill on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zLanqing/codex-claude-academic-skills/tree/main/research-writing-skill. 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 Research Writing 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 Research Writing Skill?

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

Is the Research Writing Skill AI skill free?

Yes. It is published on GitHub by zLanqing 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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