Loading...
Loading...
Found 10,556 Skills
Git worktree management for parallel agent team development. Triggers: 'create worktree', 'worktree setup', or during /delegate dispatch. Do NOT use for branch creation without delegation context.
Multi-perspective adversarial review. 4 Agents are spawned in parallel (full mode), each identifying issues from different perspectives, and the main thread makes a comprehensive ruling. Trigger methods: /story-review, /审查, "审查一下", "帮我审一下"
Build or modify reusable registry content for board designers. Use when fulfilling a librarian request or when asked to add/fix a registry component, symbol, footprint, STEP model, datasheet, family selector, or datasheet-backed Zener reference circuit. Covers artifact import, symbol/footprint cleanup, package structure, sourceability, and validation.
Use when working on Laminar demands via the remote Laminar MCP and you see wrong or empty client/product scope, plans from source-context lists without per-id loads, needless raw transcripts, same-step or same-release story-map peers, anchored ADR conflicts, MCP transitions/assignments, broken or silent MCP, or mentions of Laminar MCP, demands, TAL-* ids, story map, anchored or source context, or Laminar handoff.
Use when implementing a Beat change — requires gherkin or proposal artifact to be done first
Luban - Skill Polishing Workshop. Transform a "usable Skill" into a public Skill asset that is "understandable, installable, shareable, verifiable, and continuously evolvable". The methodology consists of five craftsman-like steps: 1. Material Inspection: First challenge whether the premise of this Skill is valid; directly state if the "material" is not worth polishing. 2. Peer Research: Search for similar Skills online to clarify its position in the ecosystem. 3. Dimension Measurement: Evaluate using three metrics - structure, actual testing, and live verification (live verification means reconciling with real running outputs; a green CI can be deceptive). 4. Iterative Refinement: Freeze the original version as a baseline; only retain changes that pass the verification gate, otherwise revert. Try to institutionalize verification methods as tools and rules in the repository. 5. Post-Release Iteration: Release is not the end; maintain a benchmark observation list, and start the next iteration based on real feedback. This tool is used when users want to upgrade, optimize, polish, productize, or release their self-developed Skills. The final deliverables include a structured Skill Polishing Report, directly replaceable rewritten segments, and a shareable "Graduation Certificate" result card that can be screenshot. Trigger phrases include but are not limited to: "Let Luban take a look at this skill", "Polish at Luban's Workshop", "Polish my skill", "Upgrade my skill", "Optimize this skill", "Skill check-up", "Skill audit", "Productize my skill", "How to release this skill", "Benchmark against similar skills", "Why no one installs my skill", "Help me publish my skill to GitHub/ClawHub", "Improve SKILL.md". Even if users only provide a Skill directory, GitHub repository link, or a segment of SKILL.md saying "Help me figure out how to modify it", it should be triggered as long as the context is about making the Skill more usable and shareable. Do NOT use this for creating a new Skill from scratch (use skill-creator), regular code review (use code-review), or rewriting ordinary prompts unrelated to Skill assets.
Use when adding new error messages to React, or seeing "unknown error code" warnings.
After implementation is complete and tests pass, sync confirmed details back to Intent. Captures finalized interfaces, data structures, naming conventions, and architecture decisions. Use after development is done and user confirms the implementation.
Reviews and improves Claude Code skills against official best practices. Supports three modes - self-review (validate your own skills), external review (evaluate others' skills), and auto-PR (fork, improve, submit). Use when checking skill quality, reviewing skill repositories, or contributing improvements to open-source skills.
Analyze and reclaim macOS disk space through intelligent cleanup recommendations. This skill should be used when users report disk space issues, need to clean up their Mac, or want to understand what's consuming storage. Focus on safe, interactive analysis with user confirmation before any deletions.
Systematically review AI agent work for quality, accuracy, and completeness. Catches bugs, verifies patterns, checks against requirements, and suggests improvements before committing changes.
Creates GitHub Pull Requests with automated validation and task tracking. Use when user wants to create PR, open pull request, submit for review, or check if ready for PR. Analyzes commits, validates task completion, generates Conventional Commits title and description, suggests labels. NOTE - for merging existing PRs, use github-pr-merge instead.