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Found 2,169 Skills
Multi-agent management workflow — task delegation, progress monitoring, quality verification with regression testing, feedback delivery, and cross-review orchestration. Use this skill when coordinating multiple agents on a shared task, monitoring delegated work, ensuring quality across agent outputs, or implementing a multi-phase plan (3+ phases or 10+ file changes).
Real-time sports & events data for AI agents via Shipp. Use when the user wants live scores, schedules, or game events for NBA, NFL, NCAA Football, MLB, or Soccer — especially to power prediction market trading strategies on Polymarket or Kalshi using a MoonPay wallet.
Interactive guide to repository workflow system: agents, skills, routing, and execution patterns. Use when user asks how the system works, what commands are available, or how to use brainstorm/plan/execute phases. Use for "how does this work", "what can you do", "explain workflow", "help me understand", or "show me the process". Do NOT use for actually executing workflows (use workflow-orchestrator) or debugging (use systematic-debugging).
Multi-agent swarm coordination for complex tasks. Uses hierarchical topology with specialized agents to break down and execute complex work across multiple files and modules. Use when: 3+ files need changes, new feature implementation, cross-module refactoring, API changes with tests, security-related changes, performance optimization across codebase, database schema changes. Skip when: single file edits, simple bug fixes (1-2 lines), documentation updates, configuration changes, quick exploration.
Agent spawning, lifecycle management, and coordination patterns. Manages 60+ agent types with specialized capabilities. Use when: spawning agents, coordinating multi-agent tasks, managing agent pools. Skip when: single-agent work, no coordination needed.
Create an appropriate git commit from the working tree and session history. Default commit messages are in Japanese unless the repo says otherwise (e.g. AGENTS.md).
Delegate tasks to AI agents via Box0. Use when the user asks to review code, check security, run tests, compare tools, get multiple perspectives, research a topic, analyze data, write docs, or any task that could benefit from specialized or parallel execution. Also use when the user mentions agent names or says "ask", "delegate", "get opinions from", or "have someone".
Analyzes Rails code quality, architecture, and patterns without modifying code. Use when the user wants a code review, quality analysis, architecture audit, or when user mentions review, audit, code quality, anti-patterns, or SOLID principles. WHEN NOT: Actually implementing fixes (use specialist agents), writing new tests (use rspec-agent), or generating new features.
Use this skill to prevent destructive operations when working on production systems or running agents autonomously.
Evaluate the output of a journey-builder run, identify instruction gaps, and edit the project root AGENTS.md (or add pitfalls to the gist) to fix those gaps. Does NOT modify the journey-builder skill itself.
Decentralized git for AI agents and humans. Use when the user wants to create repositories, push code, open pull requests, review and merge PRs, manage issues, create or claim bounties, delegate tasks to other agents, register human-readable names on Base L2, or interact with the gitlawb decentralized git network. Supports cryptographic DID identities, Ed25519-signed pushes, UCAN capability delegation, libp2p networking, and 31+ MCP tools for AI agent integration. Do NOT use for GitHub, GitLab, or other centralized git hosts.
Set up and improve harness engineering (AGENTS.md, docs/, lint rules, eval systems, project-level prompt engineering) for AI-agent-friendly codebases. Triggers on: new/empty project setup for AI agents, AGENTS.md or CLAUDE.md creation, harness engineering questions, making agents work better on a codebase. ALSO triggers when users are frustrated or complaining about agent quality — e.g. 'the agent keeps ignoring conventions', 'it never follows instructions', 'why does it keep doing X', 'the agent is broken' — because poor agent output almost always signals harness gaps, not model problems. Covers: context engineering, architectural constraints, multi-agent coordination, evaluation, long-running agent harness, and diagnosis of agent quality issues.