context-prep
Prepare optimal context package before delegating tasks to sub-agents
agent-select
Analyze tasks and recommend optimal sub-agent(s) for execution
ds-delegate
Subagent delegation for data analysis. Dispatches fresh Task agents with output-first verification.
openai-agents
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cloudflare-agents
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yajña
Coordinated multi-agent execution through Vedic ritual patterns. Use when a plan requires multiple agents working on different tasks toward a unified goal. Spawns specialized agents (research, implement, validate) that communicate through shared memory.
multi-agent-composition
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dispatching-parallel-agents
Use when facing 3+ logically independent failures (different features, different root causes) that can be investigated concurrently - dispatches multiple agents to investigate in parallel; requires either parallel-safe test infrastructure OR sequential fix implementation
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.
agent-creator
Create custom Task subagents in ~/.claude/agents/. Use when defining new agents for Task tool delegation.
subagent-driven-development
Use when executing implementation plans with independent tasks in the current session or facing 3+ independent issues that can be investigated without shared state or dependencies - dispatches fresh subagent for each task with code review between tasks, enabling fast iteration with quality gates
debate-hall
Wind/Wall/Door multi-perspective debate orchestration using debate-hall-mcp tools. Use when facilitating structured debates, architectural decisions, or multi-perspective analysis.
multi-model-research
Orchestrate multiple frontier LLMs (Claude, GPT-5.1, Gemini 3.0 Pro, Perplexity Sonar, Grok 4.1) for comprehensive research using LLM Council pattern with peer review and synthesis
thinking-framework
Use this when complex problem-solving, root cause analysis, strategic decision-making, or systematic thinking is needed. Applies 15 thinking methods with multi-agent orchestration and Clear-Thought MCP integration for enhanced analysis quality.
tools
Use when implementing function calling, tool use, or agents with LLMs - unified tool API works across OpenAI, Anthropic, Google, and Ollama with consistent tool definition and execution patterns
prompt-engineering
Expert skill for prompt engineering and task routing/orchestration. Covers secure prompt construction, injection prevention, multi-step task orchestration, and LLM output validation for JARVIS AI assistant.
two-agent-harness
This skill sets up a complete two-agent development system based on the "Effective Harnesses for Long-Running Agents" research. It creates initializer-agent (for project planning) and coding-agent (for incremental implementation), along with enforcement hooks and progress tracking infrastructure. Use when users ask to "set up two-agent system", "install agent harness", "configure Opus delegation", or want to implement the two-agent architecture pattern.
orchestrator
Use when managing agent state transitions (START/INIT/IMPLEMENT/TEST/COMPLETE), triggering context compression at 80% capacity, or handling session lifecycle. Load at session start, on state change, or when context exceeds threshold. Core skill for single-orchestrator architecture.
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