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Found 11,817 Skills
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.
Build LLM applications with LangChain and LangGraph. Use when creating RAG pipelines, agent workflows, chains, or complex LLM orchestration. Triggers on LangChain, LangGraph, LCEL, RAG, retrieval, agent chain.
Wallets for AI agents with x402 payment signing, referral rewards, and policy-controlled actions.
Integrate installed skill usage guidance into project CLAUDE.md/AGENTS.md based on project context. Use when skills are installed but agents don't know when to use them, when setting up a new project with skills, or when updating guidance after adding skills.
Guide for setting up AI configuration in your application. Helps you choose between agent vs completion mode, select the right approach for your stack, and create AI Configs that make sense for your use case.
Guide for giving your AI agents capabilities through tools. Helps you identify what your AI needs to do, create tool definitions, and attach them in a way that makes sense for your framework.
Neural web search and content extraction using x402-protected APIs. Better than WebSearch for deep research and WebFetch for blocked sites. USE FOR: - Deep web research and investigation - Finding similar pages to a reference URL - Extracting clean text from web pages - Scraping sites that block standard fetchers - Getting direct answers to factual questions - Research requiring multiple sources TRIGGERS: - "research", "investigate", "deep dive", "find sources" - "similar to", "pages like", "more like this" - "scrape", "extract content from", "get the text from" - "blocked site", "can't access", "paywall" - "what is", "explain", "answer this" Use `npx agentcash fetch` for stableenrich.dev endpoints. Prefer Exa for semantic/neural search, Firecrawl for direct scraping.
AI agent development standards using golanggraph for graph-based workflows, langchaingo for LLM calls, tool integration, MCP, and LLM best practices (context compression, prompt caching, attention raising, tool response trimming).
Desktop automation via native OS accessibility trees using the agent-desktop CLI. Use when an AI agent needs to observe, interact with, or automate desktop applications (click buttons, fill forms, navigate menus, read UI state, toggle checkboxes, scroll, drag, type text, take screenshots, manage windows, use clipboard). Covers 50 commands across observation, interaction, keyboard/mouse, app lifecycle, clipboard, and wait. Triggers on: "click button", "fill form", "open app", "read UI", "automate desktop", "accessibility tree", "snapshot app", "type into field", "navigate menu", "toggle checkbox", "take screenshot", "desktop automation", "agent-desktop", or any desktop GUI interaction task. Supports macOS (Phase 1), with Windows and Linux planned.
Run agent-browser on AWS Bedrock AgentCore cloud browsers. Use when the user wants to use AgentCore, run browser automation on AWS, use a cloud browser with AWS credentials, or needs a managed browser session backed by AWS infrastructure. Triggers include "use agentcore", "run on AWS", "cloud browser with AWS", "bedrock browser", "agentcore session", or any task requiring AWS-hosted browser automation.
Give every AI agent its own computer: a persistent workspace with a filesystem, processes, shells, networking, and agent sessions on a lightweight in-process OS.
Build conversational AI voice agents with ElevenLabs Platform. Configure agents, tools, RAG knowledge bases, agent versioning with A/B testing, and MCP security. React, React Native, or Swift SDKs. Prevents 34 documented errors. Use when: building voice agents, AI phone systems, agent versioning/branching, MCP security, or troubleshooting @11labs deprecated, webhook errors, CSP violations, localhost allowlist, tool parsing errors.