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Found 535 Skills
This skill should be used when the user asks to "wrap up session", "end session", "session wrap", "/wrap", "document learnings", "what should I commit", or wants to analyze completed work before ending a coding session.
Explore-first wave pipeline. Decomposes requirement into exploration angles, runs wave exploration via spawn_agents_on_csv, synthesizes findings into execution tasks with cross-phase context linking (E*→T*), then wave-executes via spawn_agents_on_csv.
Protocolo de Inteligência Pré-Tarefa — ativa TODOS os agentes relevantes do ecossistema ANTES de executar qualquer tarefa solicitada pelo usuário. Enriquece o contexto com análise paralela...
Use when an approved current phase has 3 or more independent ready tasks and parallel execution will materially reduce cycle time. Orchestrates bounded workers, monitors blockers and file conflicts, coordinates rescues, and hands off to planning or reviewing when the current execution scope is complete. Use for prompts about swarming, parallel workers, launching multiple agents, coordinating a worker pool, or running approved current-phase work at scale.
어떤 주제/과제든 받아서 스스로 팀을 구성하고 조사·분석·검토·결과도출까지 처리하는 범용 에이전트 팀 오케스트레이터. "팀으로 분석해줘", "에이전트 팀으로 조사해줘", "다각도로 검토해줘", "심층 분석 부탁해", "여러 관점으로 봐줘", "think-team", "think team" 키워드로 트리거. 단순 질문이 아닌 복합적 판단, 조사, 전략 결정이 필요한 모든 상황에서 사용.
Refine, parallelize, and verify a draft task specification into a fully planned implementation-ready task
使用 agency-agents-zh 中文 AI 智能体角色库，为 AI 编程工具提供 215 个即插即用的专家角色
Control Room template for managing Hermes agents from one VPS agent to specialist teams and orchestrated workflows
LangGraph parallel execution patterns. Use when implementing fan-out/fan-in workflows, map-reduce over tasks, or running independent agents concurrently.
Build Python agents with Agentica SDK - @agentic decorator, spawn(), persistence, MCP integration
Orchestrates parallel KB generation using spatial analysis and a map-reduce architecture with incremental update support.
Classify user requests and route to the correct agent + skill combination. Use for any user request that needs delegation: code changes, debugging, reviews, content creation, research, or multi-step workflows. Invoked as the primary entry point via "/do [request]". Do NOT handle code changes directly - always route to a domain agent. Do NOT skip routing for anything beyond pure fact lookups or single read commands.