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Found 357 Skills
Agent spawning, lifecycle management, and coordination patterns. Manages 60+ agent types with specialized capabilities. Use when: spawning agents, coordinating multi-agent tasks, managing agent pools. Skip when: single-agent work, no coordination needed.
Give an AI agent an encrypted inbox with the masumi-agent-messenger CLI. Use when agents need to message other agents, read durable inboxes, manage threads, coordinate async multi-agent workflows, request human approval, or automate inbox operations with JSON output.
Meta-skill for understanding and customizing Mindfold Trellis — the all-in-one AI workflow system for 11 AI coding platforms (Claude Code, Cursor, OpenCode, iFlow, Codex, Kilo, Kiro, Gemini CLI, Antigravity, Qoder, CodeBuddy). Documents the original Trellis system design including architecture, commands, hooks, multi-agent pipelines, monorepo support, and task lifecycle hooks. Use when understanding Trellis architecture, customizing workflows, adding commands or agents, troubleshooting issues, or adapting Trellis to specific projects. Modifications should be recorded in a project-local trellis-local skill, not here.
Operates a shared identity graph that multiple AI agents resolve against. Ensures every agent in a multi-agent system gets the same canonical answer for "who is this entity?" - deterministically, even under concurrent writes.
Designs identity, authentication, and trust verification systems for autonomous AI agents operating in multi-agent environments. Ensures agents can prove who they are, what they're authorized to do, and what they actually did.
You are an **Identity Graph Operator**, the agent that owns the shared identity layer in any multi-agent system. When multiple agents encounter the same real-world entity (a person, company, produc...
Context window coach. Proactive guidance for token-efficient Claude Code projects, multi-agent systems, and skill architecture.
Use when working with AWS Strands Agents SDK or Amazon Bedrock AgentCore platform for building AI agents. Provides architecture guidance, implementation patterns, deployment strategies, observability, quality evaluations, multi-agent orchestration, and MCP server integration.
Develop agentic software and multi-agent systems using Google ADK in Python
AI agents: autonomous agents, multi-agent systems, LangChain, LlamaIndex, MCP.
Use when designing multi-agent systems, implementing supervisor patterns, coordinating multiple agents, or asking about "multi-agent", "supervisor pattern", "swarm", "agent handoffs", "orchestration", "parallel agents"
Build single-agent and multi-agent systems using Google's Agent Development Kit (ADK) in Python, Java, Go, or TypeScript. Use when creating AI agents with ADK, designing multi-agent architectures, implementing agent tools, configuring agent callbacks, managing agent state, orchestrating sequential/parallel/loop agent workflows, or when the user mentions ADK, google-adk, google agent development kit, agentic AI with Gemini, or agent orchestration with Google tools. Also use when setting up ADK projects, writing agent tests, deploying agents, or integrating MCP tools with ADK.