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Found 409 Skills
Use the Orca CLI to coordinate multiple coding agents via inter-agent messaging, task DAGs, dispatch with preamble injection, decision gates, and coordinator loops. Use when an agent needs to send or check inter-agent messages; create, dispatch, or track orchestration tasks; coordinate multi-agent workflows; or act as a coordinator dispatching work across terminals. Triggers include "orchestrate agents", "dispatch task", "send message to agent", "check inbox", "coordinate agents", "multi-agent", "create task DAG", "worker_done", "escalation", or any task involving inter-agent coordination through Orca.
High-performance reinforcement learning framework optimized for speed and scale. Use when you need fast parallel training, vectorized environments, multi-agent systems, or integration with game environments (Atari, Procgen, NetHack). Achieves 2-10x speedups over standard implementations. For quick prototyping or standard algorithm implementations with extensive documentation, use stable-baselines3 instead.
Agno AI agent framework. Use for building multi-agent systems, AgentOS runtime, MCP server integration, and agentic AI development.
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.
Create and manage agent graphs — directed graphs of AI Configs connected by edges with handoff logic. Use when building multi-agent workflows where configs route to each other.
Advanced context engineering techniques for AI agents. Token-efficient plugins improving output quality through structured reasoning, reflection loops, and multi-agent patterns.
Generates comprehensive, workable unit tests for any programming language using a multi-agent pipeline. Use when asked to generate tests, write unit tests, improve test coverage, add test coverage, create test files, or test a codebase. Supports C#, TypeScript, JavaScript, Python, Go, Rust, Java, and more. Orchestrates research, planning, and implementation phases to produce tests that compile, pass, and follow project conventions.
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.
Master TDD orchestrator specializing in red-green-refactor discipline, multi-agent workflow coordination, and comprehensive test-driven development practices. Enforces TDD best practices across teams with AI-assisted testing and modern frameworks. Use PROACTIVELY for TDD implementation and governance.
SPARC (Specification, Pseudocode, Architecture, Refinement, Completion) comprehensive development methodology with multi-agent orchestration
Develop agentic software and multi-agent systems using Google ADK in Python
Invoke for complex multi-step tasks requiring intelligent planning and multi-agent coordination. Use when tasks need decomposition, dependency mapping, parallel/sequential/swarm/iterative execution strategies, or coordination of multiple specialized agents.