mit einem Klick
claude-team-orchestration
claude-team-orchestration enthält 10 gesammelte Skills von zircote, mit Repository-Berufsabdeckung und Skill-Detailseiten auf SkillsMP.
Skills in diesem Repository
Choose the right agent type for each task including built-in agents (Bash, Explore, Plan, general-purpose) and plugin agents (review, research, refactoring, SDLC). Use when selecting agent types, understanding agent capabilities, or matching agents to tasks.
Debug and recover from agent team errors including common errors, hooks for quality gates, known limitations, and recovery strategies. Use when encountering team errors, enforcing quality gates with hooks, understanding limitations, or debugging agent issues.
Send messages between agents using SendMessage including direct messages, broadcasts, shutdown requests/responses, and plan approvals. Use when communicating between agents, understanding message formats, or handling structured protocol messages.
Master multi-agent orchestration using Claude Code's agent teams and task system. Use when coordinating multiple agents, running parallel code reviews, creating pipeline workflows with dependencies, building self-organizing task queues, or any task benefiting from divide-and-conquer patterns. Routes to specialized sub-skills for team management, tasks, messaging, patterns, backends, and error handling.
Apply proven orchestration patterns for agent teams including parallel specialists, pipelines, swarms, research+implementation, plan approval, and multi-file refactoring. Use when choosing a team structure, designing workflows, or implementing specific coordination patterns.
Manage shared task lists for agent teams including creating tasks, setting dependencies, claiming work, and tracking progress. Use when creating work items, building task pipelines, coordinating task ownership, or managing task dependencies.
Analyze large JSONL log files using schema-aware partitioned analysis. Discovers field schema, generates tailored jq extraction recipes, and orchestrates parallel chunk analysts with synthesis. Use when processing JSONL logs exceeding context limits, performing log analytics, or investigating incident logs.
Process files exceeding context limits using the RLM (Recursive Language Model) pattern with agent teams. Use when you need to process large files, analyze documents exceeding context, apply RLM chunking, chunk and analyze large content, or handle long context documents.
Configure how teammate agents run including in-process, tmux, and iTerm2 backends. Use when choosing a display mode, setting up split panes, troubleshooting backend issues, or configuring teammateMode.
Create, configure, and manage agent teams including spawning teammates, delegate mode, permissions, shutdown, and cleanup. Use when setting up a new team, spawning workers, configuring team modes, or shutting down a completed team.