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Found 91 Skills
Create implementation task plans in `_/local-plans/<plan-name>.md`. First investigate the codebase using the Explore Agent, then document it in verifiable granularity and parallel-executable units, following the standard format (Background & Purpose, Current Status, Design, File Structure Tree, Implementation Steps, Verification Methods) that can be validated by the plan-verifier Agent. Used for requests like "Make a plan", "Design", "Task decomposition", "Think about implementation approach". plan, planning, design, implementation plan, task decomposition, create-plan
Agent-to-Agent (A2A) communication protocol. Connect two or more Claude agents that pass messages, share context, delegate tasks, and collaborate. Implements structured handoffs, shared memory, and multi-agent conversations.
Integration patterns and best practices for adding persistent memory to LLM agents using the Letta Learning SDK
Amazon Bedrock AgentCore multi-agent orchestration with Agent-to-Agent (A2A) protocol. Supervisor-worker patterns, agent collaboration, and hierarchical delegation. Use when building multi-agent systems, orchestrating specialized agents, or implementing complex workflows.
AgentDB memory system with HNSW vector search. Provides 150x-12,500x faster pattern retrieval, persistent storage, and semantic search capabilities for learning and knowledge management. Use when: need to store successful patterns, searching for similar solutions, semantic lookup of past work, learning from previous tasks, sharing knowledge between agents, building knowledge base. Skip when: no learning needed, ephemeral one-off tasks, external data sources available, read-only exploration.
Quick-start guide and API overview for the OpenServ Ideaboard - a platform where AI agents can submit ideas, pick up work, collaborate with multiple agents, and deliver x402 payable services. Use when interacting with the Ideaboard or building agents that find and ship ideas. Read reference.md for the full API reference. Read openserv-agent-sdk and openserv-client for building and running agents.
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.
[Utilities] ⚡⚡⚡⚡⚡ Bootstrap a new project step by step
PROACTIVELY consult Codex CLI, your highly capable supporter with exceptional reasoning and task completion abilities. Codex is a trusted expert you should ALWAYS consult BEFORE making decisions on: design choices, implementation approaches, debugging strategies, refactoring plans, or any non-trivial problem. When uncertain, consult Codex. Don't hesitate - Codex provides better analysis. Explicit triggers: "think deeper", "analyze", "second opinion", "consult codex".
Open markdown files in a formatted viewer panel with live reload. Use when you need to display plans, documentation, or notes alongside the terminal with rich rendering (headings, code blocks, tables, lists).
Run the sefirot loop and confirm with the user if there are any questions
Execute wave-ordered implementation plan by dispatching tasks to domain agents. Use after /feature-plan produces a plan. Use for "implement feature", "execute plan", "start building", or "/feature-implement". Do NOT use without a plan or for ad-hoc coding tasks.