agent-select
Analyze tasks and recommend optimal sub-agent(s) for execution
anti-conflict
Prevent file conflicts between multiple AI agents working in parallel
pufferlib
This skill should be used when working with reinforcement learning tasks including high-performance RL training, custom environment development, vectorized parallel simulation, multi-agent systems, or integration with existing RL environments (Gymnasium, PettingZoo, Atari, Procgen, etc.). Use this skill for implementing PPO training, creating PufferEnv environments, optimizing RL performance, or developing policies with CNNs/LSTMs.
gastown
Multi-agent orchestrator for Claude Code. Use when user mentions gastown, gas town, gt commands, bd commands, convoys, polecats, crew, rigs, slinging work, multi-agent coordination, beads, hooks, molecules, workflows, the witness, the mayor, the refinery, the deacon, dogs, escalation, or wants to run multiple AI agents on projects simultaneously. Handles installation, workspace setup, work tracking, agent lifecycle, crash recovery, and all gt/bd CLI operations.
antahkarana
Multi-perspective reasoning through Upanishadic Antahkarana voices. Use for complex problems requiring diverse viewpoints and synthesis. Spawns multiple Claude agents that reason from different perspectives and synthesize wisdom.
multi-agent-composition
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google-adk-python
Google Agent Development Kit (ADK) for Python. Capabilities: AI agent building, multi-agent systems, workflow agents (sequential/parallel/loop), tool integration (Google Search, Code Execution), Vertex AI deployment, agent evaluation, human-in-the-loop flows. Actions: build, create, deploy, evaluate, orchestrate AI agents. Keywords: Google ADK, Agent Development Kit, AI agent, multi-agent system, LlmAgent, SequentialAgent, ParallelAgent, LoopAgent, tool integration, Google Search, Code Execution, Vertex AI, Cloud Run, agent evaluation, human-in-the-loop, agent orchestration, workflow agent, hierarchical coordination. Use when: building AI agents, creating multi-agent systems, implementing workflow pipelines, integrating LLM agents with tools, deploying to Vertex AI, evaluating agent performance, implementing approval flows.
lattice-spatial-collective
Multi-agent distributed context preservation protocol using cryptographic sharding, gossip propagation, and Byzantine fault tolerance to maintain coherent shared memory across dynamic agent networks.
rtc-consensus-synthesis
Execute the Recursive Thought Committee (RTC) protocol by generating and harmonizing inputs from 5 specialized cognitive personas.
agent-task-delegator
Delegate tasks across multi-agent architectures with proper context preservation.
agent-task-conductor
Conduct multi-agent task orchestration and workflow coordination.
subagent-orchestration
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dispatching-parallel-agents
Dispatches one subagent per independent domain to parallelize investigation/fixes. Use when you have 2+ unrelated failures (e.g., separate failing test files, subsystems, bugs) with no shared state or ordering dependencies.
slipstream-protocol
Slipstream Protocol v2 - Semantic Quantization for Multi-Agent Communication
multi-agent
Build multi-agent systems - orchestration, coordination, workflows, and distributed architectures
parallel-agents
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multi-agent-orchestrator
Orchestrate parallel CLI agents (Claude Code, Codex, Gemini) for competitive evaluation. Use when user says "run multi-agent", "compare agents", "launch competitive evaluation", "use parallel agents", or complex tasks (>7/10) where multiple approaches exist and best solution matters.
agent-mail
Multi-agent coordination via agent-mail CLI. Use when communicating with other agents, coordinating file access, sending/receiving messages, checking inbox, or reserving files. Triggers on "send message to agent", "check inbox", "reserve files", "coordinate with other agents", multi-agent workflows, file reservations, acknowledgements, "list agents", "delete agent", "clean up agents", "purge agents".
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