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Research Writing

Community
scholay
research-writing

Draft and maintain academic papers from verified corpus evidence, manage bibliography, and produce output in any target format. Use when outlining chapters, mapping evidence to arguments, drafting or revising sections, managing citations, placing figures, or running integrity checks before building output.

Overview

Publisherscholay
Repositoryskills
Skill nameresearch-writing
Stars
139
Forks
0
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

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

Installation

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

Use it in TypingMind

Enable Research Writing 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 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 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

The core problem

Two failure modes are common in AI-assisted academic writing:

  1. Writing from memory: using AI to draft paragraphs without grounding claims in verified source evidence. Produces fluent but unchecked text.
  2. Leaking the workflow: letting internal process terms (corpus, slice, source card, OCR pipeline) appear in the final document. The paper should read as the researcher's scholarly analysis, not as a description of how it was produced.

This skill addresses both.

Core principle

Write from verified evidence, not from memory. Every claim in the paper should trace back to at least one evidence slice at reviewed or quote_ready level. Every figure should trace back to a reviewed image slice.

The paper is the output of the research system. It should not expose how the system works.

Workflow

1. Build the chapter outline before drafting

For each chapter or section, define:

  • The central question or argument this section answers
  • The evidence slices that support the argument (from your evidence matrix)
  • The figures or tables that illustrate it
  • The citekeys required

Do not start drafting paragraphs before this mapping is clear.

2. Draft from grouped evidence

For each subsection:

  1. Gather the evidence slices assigned to it
  2. Identify what they collectively show
  3. Write synthesis paragraphs in your own analytical voice
  4. Insert citations inline as you draft — do not leave "[CITE]" placeholders

On citations: insert a citation only when the supporting slice is at least verification_status: checked. For direct quotations, quote_ready: true is required.

3. Manage bibliography in parallel

Keep one master bibliography file. One source gets one citekey.

Citekey rules:

  • ASCII only
  • Format: AuthorYYYYKeyword (e.g., Smith2019vernacular)
  • Once a citekey appears in any draft, treat it as frozen — changing it later breaks citations silently

Add to the bibliography as you add citations to the draft, not at the end.

Verify bibliography integrity regularly:

  • Every inline citation resolves to a bibliography entry
  • Every bibliography entry is actually cited somewhere (no phantom references)
  • Every cited slice can be traced to a source object

4. Place figures purposefully

A figure earns its place by serving a specific argumentative role. Place it after the paragraph that establishes why it matters, not before.

For each figure:

  • Use the canonical source page image or a crop of it, not a screenshot
  • Crop exactly one figure element — not a full page, not a region containing multiple independent figures
  • The caption states the scholarly source and page
  • Keep a record of: source image path, crop coordinates, output filename, and why this is a single figure element

5. Run an integrity check before every output build

Before compiling or exporting:

  • Every major claim has a citation
  • Every citation resolves in the bibliography
  • Every figure has a documented source trail
  • No internal process terms appear in the body text

6. Produce output in your target format

The manuscript source (LaTeX, Markdown, Word, etc.) is canonical. All exports (PDF, DOCX, HTML) are derivatives.

If you generate a manuscript snapshot for app-layer reading or collaboration, treat it as read-only — edits belong in the canonical source, not in the snapshot.

Formatting rules for Chinese academic prose

(Skip this section if not writing in Chinese)

  • Page locators: keep them in footnotes or endnotes, not inline in body text
  • Abbreviations: spell out every abbreviation on first use: 中文含义(Full Term, ABBR)
  • Term lists: use a table for more than 3–4 term correspondences, followed by a prose paragraph that interprets the table
  • Alarm vocabulary: avoid "濒危" (endangered), "亟待抢救" (urgently needs rescue) and similar terms unless directly supported by a cited source

Rules

  • Write only from evidence at reviewed level or above. Do not draft claims you cannot immediately trace to a slice.
  • Do not introduce a claim without a citation trace. Unsupported claims belong in notes/ until evidence is found.
  • Do not expose internal workflow terms in the body text: corpus, slice, SLC-, SRC-, source card, OCR, agent. These belong in comments, notes, and working documents.
  • Do not place a whole source page as a figure. Crop one meaningful figure element.
  • Do not change a citekey once it appears in any draft file.
  • The manuscript snapshot is read-only. Canonical edits go into the source file.
  • Run bibliography integrity check before every build.

References

  • Read references/bibliography-management.md for citekey rules, deduplication procedure, and bibliography sync workflow.
  • Read references/figure-workflow.md for the full figure extraction, cropping, and captioning procedure.
  • Read references/proposal-conventions.md for doctoral proposal rhetorical structure and common mistakes to avoid.

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 AI skill do?

Draft and maintain academic papers from verified corpus evidence, manage bibliography, and produce output in any target format. Use when outlining chapters, mapping evidence to arguments, drafting or revising sections, managing citations, placing figures, or running integrity checks before building output.

Why use Research Writing on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/scholay/skills/tree/main/research-writing. 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?

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?

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

Is the Research Writing AI skill free?

Yes. It is published on GitHub by scholay 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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