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Found 105 Skills
Orchestrate a comprehensive git workflow from code review through PR creation, leveraging specialized agents for quality assurance, testing, and deployment readiness. This workflow implements modern g
Use this skill when you see `/omo`. Multi-agent orchestration for "code analysis / bug investigation / fix planning / implementation". Choose the minimal agent set and order based on task type + risk; recipes below show common patterns.
Use this skill when orchestrating multi-agent work at scale - research swarms, parallel feature builds, wave-based dispatch, build-review-fix pipelines, or any task requiring 3+ agents. Activates on mentions of swarm, parallel agents, multi-agent, orchestrate, fan-out, wave dispatch, research army, unleash, dispatch agents, or parallel work.
Execute a phased implementation plan using subagents. Use when asked to execute, run, or carry out a plan — especially one created by make-plan.
Use when orchestrating multi-agent teams for parallel work — feature dev, quality audits, research sprints, bug hunts, or any task needing 2+ agents working concurrently
Named Tmux Manager - Multi-agent orchestration for Claude Code, Codex, and Gemini in tiled tmux panes. Visual dashboards, command palette, context rotation, robot mode API, work assignment, safety system. Go CLI.
Use when user has complex multi-agent workflows, needs to coordinate sequential or parallel agent execution, wants workflow visualization and control, or mentions automating repetitive multi-agent processes - guides discovery and usage of the orchestration system
Use when executing multi-task plans where each task can be implemented independently by a subagent. Triggers when a plan has 3+ independent tasks, when speed of execution is important, when tasks have clear acceptance criteria suitable for delegation, or when two-stage review gates (spec compliance and code quality) are needed for iterative fix cycles.
Automated multi-agent orchestrator that spawns CLI subagents in parallel, coordinates via MCP Memory, and monitors progress
LangChain / LangGraph engineering pitfalls and verified fixes. Covers DeepAgents, OpenAI-compatible model integration (including Chinese provider adapters: DeepSeek, Qwen, GLM, etc.), middleware, streaming, multi-agent orchestration, and other common development issues. Use when hitting unexpected behavior, making architecture decisions, or integrating Chinese LLM providers during LangChain development.
Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration. Use when: build agent, AI agent, autonomous agent, tool use, function calling.
Bootstrap lean multi-agent orchestration with beads task tracking. Use for projects needing agent delegation without heavy MCP overhead.