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Review Docs

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tldraw
review-docs

Review and improve tldraw SDK documentation against the voice and docs guides, scoring each article and iterating until it passes. Use when asked to review docs, audit an article in apps/docs/content, check whether documentation follows the style guide, or improve existing documentation.

Overview

Publishertldraw
Repositorytldraw
Skill namereview-docs
Stars
50.4K
Forks
3.5K
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Review Docs 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/tldraw/tldraw.git /tmp/tldraw
mkdir -p .claude/skills
cp -r /tmp/tldraw/skills/review-docs .claude/skills/review-docs
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Review Docs 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 Review Docs 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 Review Docs 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.

Review documentation

This skill runs an evaluation and improvement loop on a documentation file.

Target: $ARGUMENTS

Relevant skills: write-docs

Workflow overview

┌──────────────────────────────────────────────────────────────┐
│  INITIALIZE: Create state file to track issues               │
└──────────────────────────────────────────────────────────────┘
┌──────────────────────────────────────────────────────────────┐
│  EVALUATE (parallel)                                         │
│  ┌─────────────────────┐    ┌─────────────────────────────┐  │
│  │ Style Agent         │    │ Content Agent               │  │
│  │ (readability+voice) │    │ (completeness+accuracy)     │  │
│  └─────────────────────┘    └─────────────────────────────┘  │
└──────────────────────────────────────────────────────────────┘
┌──────────────────────────────────────────────────────────────┐
│  UPDATE STATE: Add new issues, verify fixed issues           │
└──────────────────────────────────────────────────────────────┘
┌──────────────────────────────────────────────────────────────┐
│  SUMMARIZE: Present findings, ask user for next step         │
└──────────────────────────────────────────────────────────────┘
           ┌──────────────────┼──────────────────┐
           ↓                  ↓                  ↓
    [User: improve]   [User: complete]    [User: done]
           ↓                  ↓                  ↓
┌──────────────────┐  ┌──────────────────┐    EXIT
│  IMPROVE         │  │  COMPLETE        │
│  (fix issues)    │  │  (fix all, exit) │
└──────────────────┘  └──────────────────┘
           ↓                  ↓
  LOOP → EVALUATE          EXIT

State file

Create a state file in the scratchpad directory to track all issues across rounds. This prevents re-discovering the same issues and allows verification of fixes.

Path: <scratchpad>/review-<filename>.md

Format:

markdown
# Review tracker: [filename]

## Issue tracker

Status values: `pending` | `fixed` | `verified-fixed` | `not-fixed` | `wont-fix`

| ID  | Issue         | Type                        | Status         | Round | Notes            |
| --- | ------------- | --------------------------- | -------------- | ----- | ---------------- |
| 1   | [description] | Style/Accuracy/Completeness | pending        | 1     | [details]        |
| 2   | [description] | Accuracy                    | verified-fixed | 1     | Fixed in round 1 |
| 3   | [description] | Completeness                | wont-fix       | 2     | Out of scope     |

## Round history

### Round 1

- Style: X/10, Voice: X/10, Completeness: X/10, Accuracy: X/10
- **Total: X/40**

Status definitions:

  • pending: Issue discovered, not yet addressed
  • fixed: Improvement agent claims to have fixed it, needs verification
  • verified-fixed: Evaluation confirmed the fix was applied correctly
  • not-fixed: Evaluation found the fix wasn't applied correctly
  • wont-fix: False alarm, out of scope, or intentional (e.g., completeness issues that require documentation expansion)

Step 1: Initial evaluation

For the first round, launch two subagents in parallel using the Task tool:

// Single message with two Task tool calls:
Task(subagent_type="general-purpose", model="opus", prompt="Style evaluation...")
Task(subagent_type="general-purpose", model="opus", prompt="Content evaluation...")

Style agent prompt (round 1)

Evaluate documentation style for: $ARGUMENTS

Read the shared guides relative to this skill directory, then read the target document:
1. `../../VOICE.md`
2. `../shared/docs-guide.md`
3. $ARGUMENTS

Score these dimensions (0-10):

READABILITY - How clear and easy to understand is the writing?
- Clear, direct sentences
- Logical flow between sections
- Appropriate use of code snippets and links
- No unnecessary jargon

VOICE - How well does it follow the writing guide?
- Confident assertions (no hedging)
- Active voice, present tense
- No AI writing tells (hollow importance, trailing gerunds, formulaic transitions)
- Appropriate tone (expert-to-developer)
- Sentence case headings

Important! Include as many high-priority fixes as needed.

Return in this exact format:

STYLE REPORT: [filename]

READABILITY: [score]/10
- [specific issue or strength]
- [specific issue or strength]

VOICE: [score]/10
- [specific issue or strength]
- [specific issue or strength]

PRIORITY FIXES:
1. [Most important style issue]
2. [Second most important]
3. [Third most important]
4. ...

Content agent prompt (round 1)

Evaluate documentation content for: $ARGUMENTS

Read $ARGUMENTS, then verify claims against the source code in packages/editor/ and packages/tldraw/.

Score these dimensions (0-10):

COMPLETENESS - How thorough is the coverage?
- Overview establishes purpose before mechanism
- Key concepts explained with enough depth
- Illustrative code snippets where needed
- Links to relevant examples in apps/examples (if applicable)

ACCURACY - Is the technical content correct?
- Code snippets are syntactically correct and use valid APIs
- API references match actual implementation
- Described behavior matches the code
- No outdated information

For accuracy issues, include file:line references to the source code.

Important! Include as many high-priority fixes as needed. Make sure that all accuracy issues are flagged.

Return in this exact format:

CONTENT REPORT: [filename]

COMPLETENESS: [score]/10
- [specific issue or strength]
- [specific issue or strength]

ACCURACY: [score]/10
- [specific issue with file:line reference if inaccurate]
- [specific issue or strength]

PRIORITY FIXES:
1. [Most important content issue]
2. [Second most important]
3. [Third most important]
4. ...

After round 1, create the state file with all discovered issues.

Step 2: Summarize and prompt user

After both agents return, synthesize their reports into a summary:

markdown
## Evaluation: [filename]

| Dimension    | Score | Key issue   |
| ------------ | ----- | ----------- |
| Readability  | X/10  | [one-liner] |
| Voice        | X/10  | [one-liner] |
| Completeness | X/10  | [one-liner] |
| Accuracy     | X/10  | [one-liner] |
| **Total**    | X/40  |             |

### Priority fixes

1. [Combined priority 1 from both reports]
2. [Combined priority 2]
3. [Combined priority 3]
4. [Combined priority 4]
5. [Combined priority 5]
6. ...

Then ask the user using AskUserQuestion:

  • Improve: Make improvements based on findings, then re-evaluate
  • Complete and finish: Fix all remaining issues and exit (no re-evaluation)
  • Done: Exit the loop without making changes

Step 3: Triage (before improvement)

Before running the improvement agent, review the pending issues with the user. Mark completeness issues that require adding new sections as wont-fix - these are documentation expansion, not review fixes.

Per AGENTS.md guidance:

"Do what has been asked; nothing more, nothing less." "Don't add features, refactor code, or make 'improvements' beyond what was asked."

The review skill improves existing content. Adding new sections is a separate task.

Step 4: Improve

Launch a single improvement agent targeting only pending issues:

Task(subagent_type="general-purpose", model="opus", prompt="Improve documentation...")

Improvement agent prompt

Improve documentation based on specific tracked issues: $ARGUMENTS

Fix ONLY these pending issues:

| ID | Issue | Type | Notes |
|----|-------|------|-------|
[paste pending issues from state file]

Instructions:
1. Read `../../VOICE.md` and `../shared/docs-guide.md` relative to this skill directory
2. Read $ARGUMENTS

3. For each accuracy fix:
   - Read the source file referenced in the notes
   - Verify the correct API/behavior from the source
   - Apply the fix based on what the source code actually shows

5. Apply style fixes

6. Run prettier: yarn prettier --write $ARGUMENTS

DO NOT:
- Add new sections
- Expand the document
- Fix issues not in the list above

Return a summary:

CHANGES MADE:

| ID | Fix applied | Verification |
|----|-------------|--------------|
| X | [description] | [source file:line checked] |
| Y | [description] | n/a |

After improvement, update the state file to mark issues as fixed.

Step 4b: Complete and finish (alternative to Step 4)

If the user selects "Complete and finish", fix all remaining pending issues without re-evaluating. This is useful when the evaluation is satisfactory and the user wants to apply fixes and move on.

Workflow:

  1. Run triage (same as Step 3) to mark out-of-scope items as wont-fix
  2. Launch the improvement agent (same prompt as Step 4)
  3. Update state file to mark issues as fixed
  4. Exit the loop - do not re-evaluate

This path trusts the improvement agent to apply fixes correctly and skips the verification cycle. Use when:

  • The issues are straightforward style fixes
  • Time is limited and re-evaluation isn't worth the cost
  • Scores are already acceptable and only minor polish remains

Step 5: Verification evaluation

For subsequent rounds, evaluation agents verify fixes AND find new issues:

Style agent prompt (verification)

Verify fixes and evaluate documentation: $ARGUMENTS

Read the state file first: [path to state file]

Then read the shared guides relative to this skill directory and the target document:
1. `../../VOICE.md`
2. `../shared/docs-guide.md`
3. $ARGUMENTS

Your job:
1. VERIFY fixes marked as "fixed" in the state file - confirm they were actually applied
2. Score style dimensions (do NOT re-flag wont-fix issues)
3. Flag only NEW issues not already in the state file

VERIFY THESE FIXES:
[paste fixed style issues from state file]

Return in this format:

VERIFICATION REPORT:

| ID | Status | Notes |
|----|--------|-------|
| X | verified-fixed / not-fixed | [what you found] |

STYLE SCORES:
READABILITY: [score]/10
VOICE: [score]/10

NEW ISSUES (not already in state file):
- [issue] or "None found"

Content agent prompt (verification)

Verify fixes and evaluate documentation content: $ARGUMENTS

Read the state file first: [path to state file]

Then read $ARGUMENTS and verify claims against source code in packages/tldraw/.

Your job:
1. VERIFY accuracy fixes marked as "fixed" in the state file
2. Score content dimensions (do NOT re-flag wont-fix issues)
3. Flag only NEW accuracy issues not already in the state file

VERIFY THESE FIXES:
[paste fixed accuracy issues from state file]

Return in this format:

VERIFICATION REPORT:

| ID | Status | Notes |
|----|--------|-------|
| X | verified-fixed / not-fixed | [what you found in doc AND source] |

CONTENT SCORES:
COMPLETENESS: [score]/10 (score existing content only, ignore wont-fix items)
ACCURACY: [score]/10

NEW ACCURACY ISSUES (not already in state file):
- [issue with source file:line] or "None found"

After verification, update the state file with new statuses and any new issues.

Step 6: Loop

Continue the loop until:

  • User chooses "Done" (exit without changes)
  • User chooses "Complete and finish" (apply fixes, then exit)
  • Scores reach acceptable levels (32/40 or higher)
  • All issues are verified-fixed or wont-fix

Notes

  • The state file prevents re-discovering the same issues across rounds
  • Evaluation agents verify previous fixes before scoring
  • wont-fix is appropriate for completeness issues requiring new sections
  • Accuracy verification is critical: The improvement agent must read actual source code before applying any accuracy fix
  • Style and content evaluations always run in parallel for efficiency

Frequently asked questions

What does the Review Docs AI skill do?

Review and improve tldraw SDK documentation against the voice and docs guides, scoring each article and iterating until it passes. Use when asked to review docs, audit an article in apps/docs/content, check whether documentation follows the style guide, or improve existing documentation.

Why use Review Docs on TypingMind?

Because you install it once and use it with any model. Review Docs 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 Review Docs in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tldraw/tldraw/tree/main/skills/review-docs. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Review Docs?

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 Review Docs?

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

Is the Review Docs AI skill free?

It is published on GitHub by tldraw. 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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