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Found 60 Skills
Fully autonomous epic execution. Runs until ALL children are CLOSED. Local mode uses /swarm with runtime-native spawning (Codex sub-agents or Claude teams). Distributed mode uses /swarm --mode=distributed (tmux + Agent Mail) for persistence and coordination. NO human prompts, NO stopping.
Automated collection process for WeChat Channels search and result traversal (Android), supporting scenarios such as comprehensive page search and personal page search.
Conduct Neovim configuration research using plugin docs and codebase exploration. Invoke for neovim research tasks.
Agent skill for planner - invoke with $agent-planner
Orchestrates task operations. Analyzes Story, builds optimal plan (1-6 implementation tasks), delegates to ln-301-task-creator (CREATE/ADD) or ln-302-task-replanner (REPLAN). Auto-discovers team ID.
Advanced Celery patterns including canvas workflows, priority queues, rate limiting, multi-queue routing, and production monitoring. Use when implementing complex task orchestration, task prioritization, or enterprise-grade background processing.
Routes tasks to skills in skill-db and skill-library using semantic discovery. Triggers on specialized skill requirements, domain-specific tasks, or explicit skill requests. Uses skill-discovery, mcp-skillset, and skill-rag-router for semantic matching.
Expert in background job processing with Bull/BullMQ (Redis), Celery, and cloud queues. Implements retries, scheduling, priority queues, and worker management. Use for async task processing, email campaigns, report generation, batch operations. Activate on "background job", "async task", "queue", "worker", "BullMQ", "Celery". NOT for real-time WebSocket communication, synchronous API calls, or simple setTimeout operations.
Autonomous workflow execution pipeline with CSV wave engine. Session discovery → plan validation → IMPL-*.json → CSV conversion → wave execution via spawn_agents_on_csv → results sync. Task JSONs remain the rich data source; CSV is brief + execution state.
Agent skill for hierarchical-coordinator - invoke with $agent-hierarchical-coordinator
Execute a task with sub-agent implementation and LLM-as-a-judge verification with automatic retry loop
Use when writing or reviewing asyncio code in Jupyter notebooks or '#%%' cell workflows — structuring event-loop ownership, orchestrating async tasks, or choosing compatibility strategies. Also use when hitting RuntimeError: This event loop is already running, asyncio.run() failures in cells, or tasks silently never completing.