Skills your agents can run

Expert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies. Covers agent design with roles and goals, task definition, crew orchestration, process types (sequential, hierarchical, parallel), memory systems, and flows for complex workflows. Essential for building collaborative AI agent teams. Use when: crewai, multi-agent team, agent roles, crew of agents, role-based agents.

Build Model Context Protocol (MCP) servers that expose tools, resources, and prompts to AI agents. Use when asked to create an MCP server, add new tools to an existing MCP, or integrate external APIs as MCP tools.

Multi-agent system for academic writing review and improvement

Manages and orchestrates multi-step, stateful agent workflows; handles task dependencies, persistent state, error recovery, and external rate-limiting. Use for creating new multi-agent systems, improving sequential workflows, or managing time-bound actions.

Access 1,200+ AI agent tools via the Model Context Protocol (MCP)

Summon a council of four voices for ambiguous decisions, trade-offs, and go/no-go judgments. Use when multiple valid paths exist and you need structured dissent before choosing.

Join the first decentralized social network for AI agents. Post once every 15 days.

Build and deploy a paid API that other agents can pay to use via x402. Use when you or the user want to monetize an API, make money, earn money, offer a service, sell a service to other agents, charge for endpoints, create a paid endpoint, or set up a paid service. Covers "make money by offering an endpoint", "sell a service", "monetize your data", "create a paid API".

Hierarchical multi-agent orchestration supervisor that decomposes tasks, delegates to specialized worker agents, tracks state, and employs triumvirate consensus for high-stakes operations

Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies for lean, fast execution.

Master orchestrator, peer-to-peer, and hierarchical multi-agent architectures

AI-native analog frontend design collaboration. Invoke when designing an analog circuit block end-to-end: spec → architecture → netlist → simulation → tape-out. Dispatches architect, designer, and verifier agents with defined roles, convergence loop, and sign-off gate.

Update AGENTS.md with new rules to prevent AI misbehavior or add operational guidelines. Use when the user says "update AGENTS.md", "add this rule", "change AI behavior", or "don't do X automatically".

Context injection for agents running in ClaudeStudio. Explains the multi-agent environment.

Deterministically coordinates autonomous planning and execution across available skills under strict guardrails. Use only when the user explicitly activates this skill by name to run autonomously until a stop command is issued. Trigger keywords include: "use autonomous-skill-orchestrator", "activate autonomous-skill-orchestrator", "start autonomous orchestration".

Guide OpenClaw agents to execute Ralph Wiggum loops using exec and process tools. Agent orchestrates coding agents (Codex, Claude Code, OpenCode, Goose) with proper TTY support via pty:true. Plans/builds code via PROMPT.md + AGENTS.md, SPECS and IMPLEMENTATION_PLAN.md. Includes PLANNING vs BUILDING modes, backpressure, sandboxing, and completion conditions. Users request loops, agents execute using tools.

Use when querying Jira issues, searching Confluence pages, creating tickets, updating documentation, or integrating Atlassian tools via the MCP protocol.

Use when starting any conversation - establishes how to find and use Skills. The Skill tool must be called before any response (including clarification questions). If a Skill might apply (even a 1% chance), you MUST invoke it.

서브에이전트 컨텍스트 문제를 해결하기 위한 점진적 컨텍스트 검색 개선 패턴

Self-governance protocol for autonomous agents: WAL (Write-Ahead Log), VBR (Verify Before Reporting), ADL (Anti-Divergence Limit), VFM (Value-For-Money), and IKL (Infrastructure Knowledge Logging). Use when: (1) receiving a user correction — log it before responding, (2) making an important decision or analysis — log it before continuing, (3) pre-compaction memory flush — flush the working buffer to WAL, (4) session start — replay unapplied WAL entries to restore lost context, (5) any time you want to ensure something survives compaction, (6) before claiming a task is done — verify it, (7) periodic self-check — am I drifting from my persona? (8) cost tracking — was that expensive operation worth it? (9) discovering infrastructure — log hardware/service specs immediately.