Coordinate multiple specialized AI agents, build autonomous pipelines, and manage complex multi-step workflows.
Build systems where AI agents collaborate — an orchestrator agent breaks down goals, delegates to specialist sub-agents, and synthesizes results. These skills cover task decomposition, agent communication protocols, parallel execution, and self-improving agent loops.
Coordinate multiple agents, manage task delegation, and build complex agent pipelines.
Orchestrate multi-agent teams with defined roles, task lifecycles, handoff protocols, and review workflows. Use when: (1) Setting up a team of 2+ agents with different specializations, (2) Defining task routing and lifecycle (inbox → spec → build → review → done), (3) Creating handoff protocols between agents, (4) Establishing review and quality gates, (5) Managing async communication and artifact sharing between agents.
Use when executing implementation plans with independent tasks in the current session
Meta-agent skill for orchestrating complex tasks through autonomous sub-agents. Decomposes macro tasks into subtasks, spawns specialized sub-agents with dynamically generated SKILL.md files, coordinates file-based communication, consolidates results, and dissolves agents upon completion. MANDATORY TRIGGERS: orchestrate, multi-agent, decompose task, spawn agents, sub-agents, parallel agents, agent coordination, task breakdown, meta-agent, agent factory, delegate tasks
Chain skills into automated pipelines with conditional logic, error handling, and audit logging. Define workflows in YAML or JSON, then execute them hands-fr...
JARVIS Mission Control v2 — free, self-hosted command center for OpenClaw AI agents. Kanban board, real-time chat, Claude Code session tracking, GitHub Issue...
Multi-Agent group chat collaboration system inspired by DingTalk/Lark. Enables AI agents to chat in groups, @mention each other, assign tasks, make decisions via voting, and collaborate. Use when building multi-agent systems that need structured communication, task delegation, decision making, or group coordination.
Quick install — most popular multi-agent orchestration skill:
clawdbot install pskoett/self-improving-agent1,600 skills found
Page 1 of 67
Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Clau...
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
Self-reflection + Self-criticism + Self-learning + Self-organizing memory. Agent evaluates its own work, catches mistakes, and improves permanently. Use when...
Persistent memory for AI agents to store facts, learn from actions, recall information, and track entities across sessions.
Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Clau...
Orchestrate multi-agent teams with defined roles, task lifecycles, handoff protocols, and review workflows. Use when: (1) Setting up a team of 2+ agents with different specializations, (2) Defining task routing and lifecycle (inbox → spec → build → review → done), (3) Creating handoff protocols between agents, (4) Establishing review and quality gates, (5) Managing async communication and artifact sharing between agents.
AI自我改进与记忆系统 - 解决'同类错误反复犯、用户纠正不长记性'的痛点。自动捕获错误、用户纠正、最佳实践，并转化为长期记忆。
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express...
Stop waiting for prompts. Keep working.
Implements Manus-style file-based planning to organize and track progress on complex tasks. Creates task_plan.md, findings.md, and progress.md. Use when aske...
Anticipates needs, keeps work moving, and improves through use so the agent gets more proactive over time.
Use when executing implementation plans with independent tasks in the current session
Automatically assess task complexity and adjust reasoning level. Triggers on every user message to evaluate whether extended thinking (reasoning mode) would improve response quality. Use this as a pre-processing step before answering complex questions.
AI Agent 設定同優化助手 - Prompt Engineering、Task Decomposition、Agent Loop設計
Turn your AI agent into a business automation architect. Design, document, implement, and monitor automated workflows across sales, ops, finance, HR, and support — no n8n or Zapier required.
Use when: you want a structured audit -> options -> recommended plan to improve an OpenClaw workspace (cost, model routing, context discipline, delegation, r...
Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Clau...
You are a Writing Team Lead managing specialized writers via MCP tools. Please ANALYZE the writing task and then:1. if exist references, create a detailed co...
Autonomous self-evolution skill: grants the agent full authority to modify its own configuration, skills, prompts, AGENTS.md, SOUL.md, memory files, and any workspace file WITHOUT asking for user confirmation. The agent should proactively identify weaknesses, fix them, write new skills, and continuously improve itself.
QVeris is a capability discovery and tool calling engine. Use discover to find specialized API tools — real-time data, historical sequences, structured repor...
Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies
JARVIS Mission Control v2 — free, self-hosted command center for OpenClaw AI agents. Kanban board, real-time chat, Claude Code session tracking, GitHub Issue...
OpenClaw skill for the agent-browser CLI (Rust-based with Node.js fallback) enabling AI-friendly web automation with snapshots, refs, and structured commands.
Complete toolkit for creating autonomous AI agents and managing Discord channels for OpenClaw. Use when setting up multi-agent systems, creating new agents, or managing Discord channel organization.
Multi-agent orchestration is when one "orchestrator" agent breaks a complex goal into subtasks and delegates each to specialized sub-agents — enabling parallel processing and modular capability composition.
Single-agent workflows execute sequentially. Multi-agent systems can run tasks in parallel, use specialized agents for different domains (web search, code writing, data analysis), and recover from individual agent failures.