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Found 11,901 Skills
Agent-based declarative testing with YAML test specs. Tests run in sub-agents to preserve main context while executing many tests. Supports MCP servers, APIs, and browser automation. Use when: testing MCP servers, running integration tests, validating tool behavior after changes, or creating regression test suites. Keywords: yaml tests, agent testing, mcp test, integration tests.
Complete AI agent operating system setup with Kanban task management. Use when setting up multi-agent coordination, task tracking, or configuring an agent team. Includes theme selection (DBZ, One Piece, Marvel, etc.), workflow enforcement (all tasks through board), browser setup, GitHub integration, and memory enhancement (Supermemory, QMD).
A meta skill that teaches AI agents how to discover, install, and use skills from the findskill.md ecosystem. Use when you need to extend capabilities by finding specialized skills, when a user asks to perform tasks that would benefit from specialized skills, or when explicitly asked to find or install skills.
Syncs skill metadata to AGENTS.md Auto-invoke sections. Trigger: When updating skill metadata (metadata.scope/metadata.auto_invoke), regenerating Auto-invoke tables, or running ./skills/skill-sync/assets/sync.sh (including --dry-run/--scope).
Run a comprehensive pull request review using multiple specialized agents. Each agent focuses on a different aspect of code quality, such as comments, tests, error handling, type design, and general code review. The skill aggregates results and provides a clear action plan for improvements. Triggers include "review PR", "analyze pull request", "code review", and "PR quality check".
Detects ESLint configuration and available commands in a repository. Returns structured JSON output designed for consumption by the quality-gates-linter agent. Checks for ESLint config files, extracts lint commands from package.json, Makefile, and CLAUDE.md, and provides command sources for the agent to read directly.
Expert in making multi-agent systems resilient. Specializes in detecting loops, hallucinations, and failures, and implementing self-healing workflows. Use when designing error handling for agent systems, implementing retry strategies, or building resilient AI workflows.
Extracts key learnings from conversations, debugging sessions, and failed attempts. Use at session end or after solving complex problems to capture insights. Stores discoveries in memory (via amplihack.memory.discoveries), suggests PATTERNS.md updates, and recommends new agent creation. Ensures knowledge persists across sessions via Kuzu memory backend.
Execute Grimoire spells inside an agent session (VM mode). Use for in-agent prototyping, validation, and best-effort execution.
Review a single file or all files in a folder for data inconsistencies, reference errors, typos, and unclear terminology using parallel sub-agents
Maintains awareness across sessions. Spawns observer agent on start, loads context, notifies of evolution opportunities.
Create Manim animations for demo videos. Use when visualizing agent workflows, skill pipelines, or architecture diagrams as animated MP4 overlays