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Take

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tldraw
take

Find a GitHub issue in tldraw/tldraw, assign it, implement it, verify it, and open a pull request. Use when the user invokes take, asks to take an issue, implement an issue, work on an issue number or URL, or pick up an issue from a description.

Overview

Publishertldraw
Repositorytldraw
Skill nametake
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 Take 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/take .claude/skills/take
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Take 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 Take 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 Take 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.

Take

Find an issue in tldraw/tldraw, implement it, and open a pull request.

Workflow

1. Find the issue

The user may reference an issue by number, URL, or description.

  • For a number or URL, fetch the issue directly:
bash
gh issue view 123 --repo tldraw/tldraw
  • For a description, search open issues first:
bash
gh issue list --repo tldraw/tldraw --search "dark mode" --state open --limit 10
  • If no open issue matches, search all issues:
bash
gh issue list --repo tldraw/tldraw --search "dark mode" --state all --limit 10

If there is one clear match, proceed. If several issues match, ask the user to choose from issue numbers and titles. If none match, ask whether to create a new issue using the issue skill.

2. Understand the issue

Read the full issue and comments. Identify:

  • Issue type: bug, feature, enhancement, cleanup, docs, or task.
  • Requested behavior and acceptance criteria.
  • Technical notes and affected files.
  • Relevant discussion or clarifications.

Agent-drafted issues may include ## Confidence and ## Open questions sections. Treat any unanswered Critical: question, _Awaiting answer._ entry, or More Info Needed label as a blocker unless codebase exploration proves the intended behavior is unambiguous. Resolve blockers with the user before implementing; non-critical questions marked _Deferred by user; not blocking implementation._ may remain open.

If the issue lacks detail, explore the codebase before deciding whether implementation is safe.

3. Assign the issue

Assign the issue to the current GitHub user. If someone else is already assigned, ask the user whether to proceed.

4. Plan the implementation

Create a concise implementation checklist based on:

  • The issue description.
  • Acceptance criteria.
  • Codebase exploration.
  • Existing repo patterns.

5. Implement

Create a new branch from main.

Work through the checklist:

  • Read files before editing them.
  • Follow existing patterns.
  • Keep changes focused on the issue.
  • Avoid speculative improvements.
  • Update docs, examples, tests, or API reports when the issue requires it.

6. Verify

Run the smallest relevant checks first. Use broader checks when the change touches shared behavior.

Typical final checks:

bash
yarn typecheck
yarn lint

For focused package changes, prefer the relevant workspace tests before repo-wide checks.

7. Create the PR

Use the pr skill.

  • Link the issue with Closes #<issue-number>.
  • Include relevant context from the issue discussion.
  • Include a clear test plan.

8. Summarize

End with:

  • Issue implemented.
  • Key changes and files modified.
  • Verification performed.
  • PR link.
  • Manual testing steps, if relevant.
  • Any acceptance criteria that could not be met and why.

Rules

  • Ask the user when requirements are unclear. Do not implement past unanswered critical questions or unresolved _Awaiting answer._ placeholders.
  • Do not guess at unspecified product behavior.
  • Keep the implementation scoped to the issue.
  • Never include AI attribution in commits, issues, or PRs.

Frequently asked questions

What does the Take AI skill do?

Find a GitHub issue in tldraw/tldraw, assign it, implement it, verify it, and open a pull request. Use when the user invokes take, asks to take an issue, implement an issue, work on an issue number or URL, or pick up an issue from a description.

Why use Take on TypingMind?

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

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

Which AI models can use Take?

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 Take?

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

Is the Take 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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