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Found 5,143 Skills
Build LiveKit Agent backends in Python. Use this skill when creating voice AI agents, voice assistants, or any realtime AI application using LiveKit's Python Agents SDK (livekit-agents). Covers AgentSession, Agent class, function tools, STT/LLM/TTS models, turn detection, and multi-agent workflows.
Discover, create, and validate headless adapters for agent integration. Includes scaffolding tools and schema-driven compliance testing.
An AI Agent Skill that enforces a 'Risk Triage -> Align -> Act' protocol. Triggers when requests are vague, conflict-ridden, or high-impact. Do not activate for low-risk or precise, scoped requests.
Test, validate, and improve agent instructions (CLAUDE.md, system prompts) using sub-agents as experiment subjects. Measures instruction compliance, context decay, and constraint strength. Use for "test prompt", "validate instructions", "prompt effectiveness", "instruction decay", or when designing robust agent behaviors.
Recovery protocols when agent is stuck—escalate to new agent, migrate context to new session, or reset mid-conversation.
Automatically fix ESLint errors by modifying code to comply with linting rules. For small codebases (≤20 errors), fixes directly. For larger codebases (>20 errors), spawns parallel agents per directory for efficient processing. Never disables rules or adds ignore comments.
Generate a plan for how an agent should accomplish a complex coding task. Use when a user asks for a plan, and optionally when they want to save, find, read, update, or delete plan files in $CODEX_HOME/plans (default ~/.codex/plans).
Agent Orchestration Rules
Use xAI's Grok model with agentic tool calling for X (Twitter) search, web search, code execution, and real-time data access. Invoke when user needs Twitter/X insights, current events, alternative perspectives, or complex multi-step research.
Comprehensive guide and utilities for building AI agents using the Agent2Agent (A2A) Protocol. Use when implementing agent-to-agent communication, creating A2A servers/clients, or working with JSON-RPC based agent systems.
Design exploration with parallel agents. Use when brainstorming ideas, exploring solutions, or comparing alternatives.
Amazon Bedrock Agents for building autonomous AI agents with foundation model orchestration, action groups, knowledge bases, and session management. Use when creating AI agents, orchestrating multi-step workflows, integrating tools with LLMs, building conversational agents, implementing RAG patterns, managing agent sessions, deploying production agents, or connecting knowledge bases to agents.