Loading...
Loading...
Found 270 Skills
Build multiple AI agents that work together. Use when you need a supervisor agent that delegates to specialists, agent handoff, parallel research agents, support escalation (L1 to L2), content pipeline (writer + editor + fact-checker), or any multi-agent system. Powered by DSPy for optimizable agents and LangGraph for orchestration.
Automate Flowiseai tasks via Rube MCP (Composio). Always search tools first for current schemas.
Create optimized prompts for Claude-to-Claude pipelines with research, planning, and execution stages. Use when building prompts that produce outputs for other prompts to consume, or when running multi-stage workflows (research -> plan -> implement).
AI team role manager for multi-agent development workflows. Use when the user wants to create/delete team roles, open role sessions in terminal tabs, assign tasks to roles, check team status, or merge role branches. Triggers on /agent-team commands, "create a team role", "open role session", "assign task to role", "show team status", "merge role branch".
Activate orchestrator mode for complex multi-task work using subagents. Use when you need to coordinate multiple independent Task subagents to accomplish work while keeping the main context window clean.
This skill should be used when the user asks to "팀 구성해줘", "team assemble", "전문가 팀으로 해줘", "팀으로 해줘", "swarm", "병렬로 전문가 팀", or wants to decompose a complex task into specialist roles executed via TeamCreate. Also triggers when user describes a task clearly benefiting from parallel expert execution.
Build and deploy parallel execution via subagent waves, agent teams, and multi-wave pipelines. Use when the Decomposition Gate identifies 2+ independent actions or when spawning teams. NOT for single-action tasks or non-parallel work.
Use when the user wants stable structured fields, required keys, reliable machine-readable sections, or downstream-consumable output from one model request, including `.output(...)`, field ordering, and `ensure_keys`.
Post-session retrospective: audits efficiency, proposes skill/memory/CLAUDE.md updates, and generates coaching feedback
Ann — Master Orchestrator for MEL/SRHR work. Use when Ane brings any analytical, evaluation, SRHR, or structured-output task. Ann classifies task complexity, queries the MEL Wiki, retrieves knowledge, creates an implementation plan (verifies with user for complex tasks), delegates to Vi for execution, runs a 5-point quality gate, and delivers. General-purpose — not tied to any specific project.
Create a workflow command that orchestrates multi-step execution through sub-agents with file-based task prompts
Initialize the memory system in the current directory, generating CLAUDE.md (optional AGENT.md for Cursor), MEMORY.md, and the memory/ directory. Triggered when the user says "initialize memory", "set up memory", "memory init", or "/memory-init".