Loading...
Loading...
Found 1,650 Skills
Apply when writing, modifying, or reviewing code. Behavioral guidelines to reduce common LLM coding mistakes. Triggers on implementation tasks, code changes, refactoring, bug fixes, or feature development.
Amazon Bedrock AgentCore Evaluations for testing and monitoring AI agent quality. 13 built-in evaluators plus custom LLM-as-Judge patterns. Use when testing agents, monitoring production quality, setting up alerts, or validating agent behavior.
Build and run evaluators for AI/LLM applications using Phoenix.
Build voice agents with the Cartesia Line SDK. Supports 100+ LLM providers via LiteLLM with tool calling, multi-agent handoffs, and real-time interruption handling.
Interact with Google's Gemini model via CLI. Use when needing a second opinion from another LLM, cross-validation, or leveraging Gemini's Google Search grounding. Supports multi-turn conversations with session management.
Analyzes and improves LLM prompts and agent instructions for token efficiency, determinism, and clarity. Use when (1) writing a new system prompt, skill, or CLAUDE.md file, (2) reviewing or improving an existing prompt for clarity and efficiency, (3) diagnosing why a prompt produces inconsistent or unexpected results, (4) converting natural language instructions into imperative LLM directives, or (5) evaluating prompt anti-patterns and suggesting fixes. Applies to all LLM platforms (Claude, GPT, Gemini, Llama).
Expert in background job processing with Bull/BullMQ (Redis), Celery, and cloud queues. Implements retries, scheduling, priority queues, and worker management. Use for async task processing, email campaigns, report generation, batch operations. Activate on "background job", "async task", "queue", "worker", "BullMQ", "Celery". NOT for real-time WebSocket communication, synchronous API calls, or simple setTimeout operations.
Performance optimization patterns covering Core Web Vitals, React render optimization, lazy loading, image optimization, backend profiling, and LLM inference. Use when improving page speed, debugging slow renders, optimizing bundles, reducing image payload, profiling backend, or deploying LLMs efficiently.
Build MCP servers in Python with FastMCP. Workflow: define tools and resources, build server, test locally, deploy to FastMCP Cloud or Docker. Use when creating MCP servers, exposing tools/resources/prompts to LLMs, building Claude integrations, or troubleshooting FastMCP module-level server, storage, lifespan, middleware, OAuth, or deployment errors.
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate exte...
This skill should be used when the user asks to "build an MCP server", "create an MCP tool", "expose resources with MCP", "write an MCP client", or needs guidance on the Model Context Protocol Python SDK best practices, transports, server primitives, or LLM context integration.
Evaluate and rank agent results by metric or LLM judge for an AgentHub session.