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.
npx skills add https://github.com/tldraw/tldraw --skill take
Find an issue in tldraw/tldraw, implement it, and open a pull request.
The user may reference an issue by number, URL, or description.
gh issue view 123 --repo tldraw/tldraw
gh issue list --repo tldraw/tldraw --search "dark mode" --state open --limit 10
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.
Read the full issue and comments. Identify:
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.
Assign the issue to the current GitHub user. If someone else is already assigned, ask the user whether to proceed.
Create a concise implementation checklist based on:
Create a new branch from main.
Work through the checklist:
Run the smallest relevant checks first. Use broader checks when the change touches shared behavior.
Typical final checks:
yarn typecheck
yarn lint
For focused package changes, prefer the relevant workspace tests before repo-wide checks.
Use the pr skill.
Closes #<issue-number>.End with:
_Awaiting answer._ placeholders.Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation
Use when completing tasks, implementing major features, or before merging to verify work meets requirements
Execute git commit with conventional commit message analysis, intelligent staging, and message generation. Use when user asks to commit changes, create a git commit, or mentions "/commit". Supports: (1) Auto-detecting type and scope from changes, (2) Generating conventional commit messages from diff, (3) Interactive commit with optional type/scope/description overrides, (4) Intelligent file staging for logical grouping
Comprehensive GitHub code review with AI-powered swarm coordination
Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.
Use this skill to review code. It supports both local changes (staged or working tree) and remote Pull Requests (by ID or URL). It focuses on correctness, maintainability, and adherence to project standards.
Refactor bloated AGENTS.md, CLAUDE.md, or similar agent instruction files to follow progressive disclosure principles. Splits monolithic files into organized, linked documentation.
Create high-quality git commits: review/stage intended changes, split into logical commits, and write clear commit messages (including Conventional Commits). Use when the user asks to commit, craft a commit message, stage changes, or split work into multiple commits.
Take tldraw/take from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.