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Found 417 Skills
Orchestrates parallel KB generation using spatial analysis and a map-reduce architecture with incremental update support.
Classify user requests and route to the correct agent + skill combination. Use for any user request that needs delegation: code changes, debugging, reviews, content creation, research, or multi-step workflows. Invoked as the primary entry point via "/do [request]". Do NOT handle code changes directly - always route to a domain agent. Do NOT skip routing for anything beyond pure fact lookups or single read commands.
Spawn specialized sub-agents with context handoff for complex multi-phase tasks. Enables expertise delegation within a session with automatic context merging and depth limiting to prevent infinite loops.
Optimize and structure context for agents and LLMs by reducing noise, prioritizing relevance, organizing memory, defining constraints, and managing token budgets.
Expert knowledge of agentic AI design patterns for autonomous agent development
Research collection of reconstructed prompt patterns and architectures for agentic AI coding assistants
Implementation guide for 17+ agentic AI architectures using LangChain and LangGraph for building sophisticated AI agents
Break down a one-sentence idea into a task plan that an AI agent can execute independently. Use this when the user says: "Help me write a goal for the agent", "Help me break down this goal in detail", "Write a task brief for the agent", "Write a goal prompt", "Let the agent run this project on its own", "Split the work among multiple agents for parallel execution". First conduct actual tests in the codebase, conduct online research if necessary, then ask a maximum of 5 questions in one go, and produce a task plan of ≤4000 characters that can be directly pasted into /goal to run, including actual test data, whitelist boundaries, anti-cheating acceptance criteria, and resumable progress. Automatically distinguish between execution-type and exploration-type (research/selection/solution-finding) tasks.
Team composition knowledge for Claude Code Agent Teams - when to suggest teams, optimal sizing, spawn prompt patterns
Full closed-loop QA combining issue discovery and software testing. Scout -> Strategist -> Generator -> Executor -> Analyst with multi-perspective scanning, progressive test layers, GC loops, and quality scoring. Supports discovery, testing, and full QA modes.
Decomposes a spec or architecture into buildable tasks with acceptance criteria, dependencies, and implementation order for AI agents or engineers. Produces `.agents/tasks.md`. Not for clarifying unclear requirements (use discover) or designing architecture (use system-architecture). For code quality checks after building, see review-chain. For packaging and PRs, see ship.
🎰 Monad Casino - An AI-powered casino where OTHER AI agents gamble against each other. You're the house. The house always wins. Built for Moltiverse Hackathon.