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Found 103 Skills
LangGraph supervisor-worker pattern. Use when building central coordinator agents that route to specialized workers, implementing round-robin or priority-based agent dispatch.
LangGraph framework for building stateful, multi-agent AI applications with cyclical workflows, human-in-the-loop patterns, and persistent checkpointing.
This skill should be used when the user wants to implement features or fix bugs using test-driven development. Enforces the RED-GREEN-REFACTOR cycle with vertical slicing, context isolation between test writing and implementation, human checkpoints, and auto-test feedback loops. Uses multi-agent orchestration with the Task tool for architecturally enforced context isolation. Supports Jest, Vitest, pytest, Go test, cargo test, PHPUnit, and RSpec.
Deep expertise in Hermes Agent architecture, implementation patterns, and extension development
Run team-based orchestration for agent squads using work items, ownership, agent Kanban, merge gates, and control pane handoffs.
Build production-ready AI agents using Google's Agent Development Kit with AI assistant integration, React patterns, multi-agent orchestration, and comprehensive tool libraries. Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.
Create and orchestrate multi-agent clusters to complete complex tasks. Use this skill when users need to break down complex tasks into multiple specialized agents for parallel or serial execution. Applicable scenarios: (1) Complex projects requiring multi-role collaboration (planning, research, coding, writing, design, analysis, review) (2) Need to execute multiple independent sub-tasks in parallel to improve efficiency (3) Need professional division of labor to optimize cost and quality. Keywords: multi-agent, agent cluster, task orchestration, parallel execution, agent team.
Coordinates skills, frameworks, and workflows throughout the project lifecycle using pattern-based sequencing, goal decomposition, phase-gate validation, and multi-agent orchestration. Use when starting multi-phase projects, sequencing frameworks, decomposing goals into capability plans, validating phase-gate readiness, coordinating subagents, or designing MCP-based tool orchestration.
Launch an intelligent sub-agent with automatic model selection based on task complexity, specialized agent matching, Zero-shot CoT reasoning, and mandatory self-critique verification
Execute tasks through competitive multi-agent generation, multi-judge evaluation, and evidence-based synthesis
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.
Multi-agent swarm orchestration where AI agents spawn, coordinate, and self-organize into collaborative teams. Use when running parallel AI agent tasks, orchestrating multi-agent workflows across Claude Code / Codex / Cursor / custom agents, isolating agent workspaces via git worktrees, tracking task dependencies across agents, or running autonomous experiments. Triggers on: clawteam, agent swarm, spawn agents, multi-agent team, agent orchestration, parallel agents, agent coordination, swarm intelligence, agent spawn, clawteam spawn, agent worktree, agentic team, ml agent experiments, autonomous agents, agent team.