Loading...
Loading...
Found 422 Skills
Manages parent/child agent relationships with task delegation and result aggregation. Supports sequential chains, parallel fans, conditional routing, retry logic, timeout handling, and YAML-based visual workflow definition.
Delegate subtasks to specialized AI agents. Use when: complex workflows need multi-agent collaboration or specialization.
Run a structured multi-perspective council on a hard decision, design choice, debugging question, strategy problem, or tradeoff. Use when the user wants multiple viewpoints, explicit cross-examination, and a compact final verdict.
3-에이전트(Architect→Builder→Reviewer) 루프로 단일 기능을 설계·구현·검증하는 팀 스킬. "3a로 만들어줘", "3에이전트", "설계-구현-검토", "team-3a" 키워드로 트리거. peach-team보다 가벼운 단일 기능·소규모 수정에 적합.
Run the evo optimization loop with parallel subagents until interrupted.
Collection of 130+ specialized Claude Code subagents for development tasks across languages, frameworks, infrastructure, and security
AI Agent Orchestration Dashboard for managing AI agents, tasks, and multi-agent collaboration via OpenClaw Gateway
Use when the user asks to research a topic in depth, map a competitive/market landscape, run a multi-source investigation, or "fan out" parallel research agents — anything where many findings must be gathered and then NOT lost. Enforces durable, detail-preserving research (write full findings to disk; keep a full appendix beside the synthesis).
Plan implementation skill. Executes approved technical plans phase by phase with verification checkpoints.
Remote-control tmux sessions for interactive CLIs by sending keystrokes and scraping pane output.
Uncertainty-aware non-linear reasoning system with recursive subagent orchestration. Triggers for complex reasoning, research, multi-domain synthesis, or when explicit commands `/nlr`, `/reason`, `/think-deep` are used. Integrates think skill (reasoning), agent-core skill (acting), and MCP tools (infranodus, exa, scholar-gateway) in recursive think→act→observe loops. Uses coding sandbox for execution validation and maintains deliberate noisiness via NoisyGraph scaffold. Supports `/compact` mode for abbreviated outputs and `/semantic` mode for rich exploration.
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"