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Found 59 Skills
Multi-agent communication, task delegation, and coordination patterns. Use when working with multiple agents or complex collaborative workflows.
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.
N coordinated agents on shared task list using Claude Code native teams
Testing and diagnosis workflow, including unit tests and browser tests, with automatic diagnosis when tests fail. Suitable for test execution and troubleshooting after code changes.
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.
Deep Reading Collaborative System: A system leveraging multi-layered AI Agents to help transform articles from "read" to "understood" to "mastered", and convert knowledge into actionable plans. Use this system when you need to deeply understand complex articles/papers, systematically organize reading notes, think critically about content, discover hidden logical issues and assumptions, or turn knowledge into action plans. Trigger keywords: deep reading, critical thinking, reading notes, article analysis, Socratic questioning, action plan
Divide-and-conquer implementation from specs/plans. Decomposes a reference document into independent tasks, assigns each to a builder agent, executes in parallel waves respecting dependencies, then integrates results. Use when you have a spec, PRD, plan, or large feature to implement quickly with parallel execution.
Cross-OpenClaw communication. Let claws on different devices chat, share memories, and learn from each other.
Create handoff documentation for work-in-progress sessions. Use when handing off work to another agent to supplement history compaction and and progress summarization.
BF 워크플로우의 사람-시스템 경계 허브. orchestrate를 모드별로 스폰하고, 에픽 단위 루프를 돌며 사람과 소통하는 유일한 경계이다.
Unified team skill for plan-and-execute pipeline. 2-member team (planner + executor) with wave pipeline for concurrent planning and execution. All roles invoke this skill with --role arg. Triggers on "team planex".