Doc Coauthoring logo

Doc Coauthoring

OrganizationPopular
shareAI-lab
doc-coauthoring

A structured workflow for co-authoring high-signal docs (PRD, RFC, design docs, proposals, decision records). Use when the user needs to turn messy context into a readable artifact with clear goals, tradeoffs, and next steps. Emphasizes context capture, outline-first drafting, and context-free reader testing to catch blind spots.

Overview

PublishershareAI-lab
RepositoryKode-CLI
Skill namedoc-coauthoring
Stars
5.2K
Forks
771
Bundled files
1
LicenseApache-2.0
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.

  • 1 bundled files

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

  • Open source

    Published by shareAI-lab on GitHub. Read the source before you install it.

Installation

Install the Doc Coauthoring 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/shareAI-lab/Kode-CLI.git /tmp/Kode-CLI
mkdir -p .claude/skills
cp -r /tmp/Kode-CLI/packages/builtin-skills/skills/doc-coauthoring .claude/skills/doc-coauthoring
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Doc Coauthoring 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 Doc Coauthoring 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 Doc Coauthoring 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.

Doc Co-Authoring

Turn partial context into a clear document by following a staged workflow. Keep the user in control of decisions, and optimize for a doc that works for readers who do not share the author’s context.

Triggers

Use this workflow when the user asks to:

  • write or refine documentation, proposals, RFCs, PRDs, decision docs, specs
  • “summarize our discussion into a doc”
  • “make this readable for others” / “share with the team”
  • create a template or standard for recurring documents

If the user explicitly wants freeform writing, keep the workflow lightweight (ask fewer questions; draft faster).


The Workflow (3 stages)

Stage 1 — Context Capture (close the gap)

Goal: collect the minimum context required to write a doc that is correct, scoped, and actionable.

Ask for:

  1. Doc type + goal: what is this document for, and what decision/action should it unlock?
  2. Audience: who will read it, and what do they already know?
  3. Constraints: deadlines, non-goals, dependencies, security/compliance, platform limits.
  4. Current state: what exists today? what’s broken? what’s missing?
  5. Options considered: at least 1–2 alternatives and why they may/ may not work.
  6. Success criteria: how we know it worked (metrics, user outcomes, acceptance tests).
  7. Open questions: unknowns that block writing certain sections.

Output of Stage 1:

  • a short “context snapshot”
  • a list of open questions (ranked by importance)
  • a proposed doc outline (1 screen)

Stage 2 — Outline-First Drafting (iterate by section)

Goal: draft a document in layers without losing coherence.

Rules:

  • Outline before prose. Do not write full paragraphs until the outline is agreed.
  • One section at a time. Draft → review → revise, then move on.
  • Maintain a decision log (small bullet list) so changes are explicit.
  • Keep unknowns visible: unresolved items stay in an “Open Questions / Risks” section, not hidden.

Recommended iteration loop per section:

  1. Write a 3–7 bullet “section intent” (what this section must answer).
  2. Draft the section (short, concrete).
  3. Ask the user for a quick pass: “What’s wrong / missing / too detailed?”
  4. Revise and update the decision log.

Stage 3 — Reader Testing (catch blind spots)

Goal: validate readability and completeness for a reader without the author’s context.

Method:

  • Prepare a clean-context review prompt: “You are a reviewer with no prior context. Read this doc and identify: missing context, unclear terms, ambiguous decisions, hidden assumptions, and where you’d ask questions.”
  • If sub-agents are available, run the review in a fresh agent session. Otherwise, run the review yourself by explicitly pretending you have no access to prior conversation.

Output of Stage 3:

  • a short list of fixes (highest leverage first)
  • revised doc with clarified assumptions, terms, and decisions

Default Section Templates

Load references/templates.md and pick the closest template:

  • Decision record (ADR-lite)
  • Product requirements (PRD-lite)
  • Technical design / RFC
  • Proposal / pitch

Quality Bar (what to optimize for)

The doc should make it easy for a reader to answer:

  • What problem are we solving, for whom, and why now?
  • What are we proposing, and what are we not doing?
  • What options did we consider, and what tradeoffs drive the choice?
  • What are the risks, unknowns, and mitigations?
  • What are the next steps and owners?

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 Doc Coauthoring AI skill do?

A structured workflow for co-authoring high-signal docs (PRD, RFC, design docs, proposals, decision records). Use when the user needs to turn messy context into a readable artifact with clear goals, tradeoffs, and next steps. Emphasizes context capture, outline-first drafting, and context-free reader testing to catch blind spots.

Why use Doc Coauthoring on TypingMind?

Because you install it once and use it with any model. Doc Coauthoring 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 Doc Coauthoring in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/shareAI-lab/Kode-CLI/tree/main/packages/builtin-skills/skills/doc-coauthoring. 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 Doc Coauthoring?

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 Doc Coauthoring?

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

Is the Doc Coauthoring AI skill free?

Yes. It is published on GitHub by shareAI-lab under the Apache-2.0 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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