Loading...
Loading...
Found 5,674 Skills
Control Ableton Live with AI agents via MCP - create MIDI clips, insert audio, add tracks/devices, analyze signals, automate mixing
Pull and use curated DESIGN.md and SKILL.md files for AI-powered design systems and agentic workflows
Automated WeChat mini-program security auditing framework using Claude Code Agent Teams with 7 specialized agents for comprehensive static analysis
Build AI agents with in-process agent loops using Anthropic or OpenAI APIs, custom tools, MCP servers, and multi-turn conversations
Research collection of reconstructed prompt patterns and architectures for agentic AI coding assistants
Hermes Labyrinth observability plugin for monitoring autonomous agent journeys, crossings, and execution traces
Implementation guide for 17+ agentic AI architectures using LangChain and LangGraph for building sophisticated AI agents
Curated collection of 1209+ best OpenClaw AI agent skills, weekly updated by MyClaw.ai
Self-referential self-improving AI agents that optimize for any computable task using meta-learning and code generation
Initialize, diagnose, or migrate a project into the LLM wiki pattern with AGENTS/CLAUDE instructions, QMD MCP wiring, Claude/Codex/OpenCode hooks/plugins, guardrails, and QMD doctor checks. Use when the user asks to set up wiki infrastructure, check if a project needs migration, install wiki hooks, or validate QMD.
Create, audit, or consolidate agent skills following the Agent Skills open standard (agentskills.io). Interviews the user relentlessly about intent, scope, and edge cases before drafting. Covers SKILL.md structure, frontmatter, progressive disclosure, description optimization, script bundling, sub-command architecture, setup gates, context systems, and review. Use when the user wants to create a skill, write a skill, build a new skill, make a skill, draft a SKILL.md, or mentions "skill-maker". Also use when asked to review a skill, audit a SKILL.md, check why a skill never triggers, improve an existing skill, or fix a skill. Also use when asked to package expertise, workflows, or domain knowledge into a reusable skill. Also use when asked to consolidate skills, merge skills, combine skills, reduce skill count, or refactor multiple skills into one.
Guides engineering of multi-agent systems—agent roles and specialization, orchestration topologies (supervisor, peer-to-peer, hierarchical, blackboard), task decomposition and routing, inter-agent messaging (A2A-style patterns), shared vs partitioned state, fan-out/fan-in and DAG workflows, synchronization and consensus, conflict resolution, fault tolerance and retries across agents, cost/latency/token budgets, cross-agent observability, testing multi-agent flows, and deployment (queues, durable workflows). Framework-agnostic; high-level LangGraph, Deep Agents, and agenthub—not single-agent loops (agentic-ai-developer), ML training (ai-engineer), strategy-only whiteboard (enterprise-strategist), or PM planning (technical-program-manager). Use for multi-agent system, multi-agent engineer, agent orchestration, supervisor agent, agent topology, fan-out fan-in, agent handoff protocol, multi-agent workflow, agent coordination, blackboard pattern, hierarchical agents, A2A, agent DAG, multi-agent architecture.