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Found 420 Skills
Orchestrate multi-agent coding tasks via Claude DevFleet — plan projects, dispatch parallel agents in isolated worktrees, monitor progress, and read structured reports.
Teneo CLI — query 400+ AI agents on the Teneo Protocol network from the terminal. Discover agents, manage rooms, handle x402 USDC micropayments, and auto-generate encrypted wallets. Use when the user needs real-time data (social media profiles, hotel search, crypto prices, gas fees, Amazon products, news) or multi-agent workflows.
Use when a single agent demonstrably cannot handle the task and multi-agent coordination is justified.
Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases.
Build multiple AI agents that work together. Use when you need a supervisor agent that delegates to specialists, agent handoff, parallel research agents, support escalation (L1 to L2), content pipeline (writer + editor + fact-checker), or any multi-agent system. Powered by DSPy for optimizable agents and LangGraph for orchestration.
Integrate oh-my-ag with MCP for ulw-style multi-agent workflows. Covers install, setup, bridge mode, and verification steps.
Build AI agents with Cloudflare Agents SDK on Workers + Durable Objects. Includes critical guidance on choosing between Agents SDK (infrastructure/state) vs AI SDK (simpler flows). Use when: deciding SDK choice, building WebSocket agents with state, RAG with Vectorize, MCP servers, multi-agent orchestration, or troubleshooting "Agent class must extend", "new_sqlite_classes", binding errors.
Multi-Agent Architecture Design and Intelligent Spawn System. Use this skill when you need to design a multi-agent system, configure specialized agents, implement intelligent task distribution, or optimize concurrent processing capabilities.
One-click initialization of a multi-agent repository from the Antigravity template. Use this skill when users want to scaffold a new project quickly (`quick` mode) or with runtime defaults (`full` mode) including LLM provider profile, MCP toggle, swarm preference context, sandbox type, and optional git init.
Drive development using delegated agent workflows. Coordinates multi-agent task execution with proper supervision and result integration.
Complete AI agent operating system setup with Kanban task management. Use when setting up multi-agent coordination, task tracking, or configuring an agent team. Includes theme selection (DBZ, One Piece, Marvel, etc.), workflow enforcement (all tasks through board), browser setup, GitHub integration, and memory enhancement (mem0, Supermemory, QMD).
Run a structured, adversarial multi-agent bug review pipeline on a codebase. Use this skill whenever the user wants to find bugs, audit code quality, review a codebase for issues, or run any kind of bug-finding or code analysis workflow. Also trigger when the user asks to 'review my code for bugs', 'find all issues in this repo', 'audit this codebase', or any similar request. The pipeline uses three sequential phases: a Bug Finder that maximizes issue discovery, a Bug Adversary that challenges false positives, and an Arbiter that issues final verdicts — producing a clean, high-confidence bug report.