Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
142 skills found
Architect production-grade LLM applications using LangChain 1.x and LangGraph. Implement stateful AI agents, multi-step workflows, and custom memory systems for complex conversational and automation tasks.
Scaffold and register new sensor, actuator, or service tools for familiar-ai, automating file creation and boilerplate integration in agent.py and config.py.
A wise conductor of expert agents. It helps you achieve goals by summoning, orchestrating, and creating specialized AI experts. Features intellectual humility, multi-agent debate, and self-learning pattern capture.
Expert skill for building and maintaining AI agents using the Claude Agent SDK, covering architecture, tool integration, MCP servers, and agentic workflows.
A framework for creating reusable Claude Code agent skills, following best practices for directory structure, progressive disclosure, and multi-file patterns.
Implement a full Model Context Protocol (MCP) stack in Rails. Connect to external servers, expose your Rails app as an MCP server, or manage subprocess MCP containers via Docker with OAuth 2.1 PKCE support.
Interactive guide for workspace discovery, providing access to specialist agents, automated workflows, CLI tools, and active lifecycle hooks.
Interactive UI components for Claude Code and AI agents. Create confirmations, checklists, inputs, tables, and views to handle non-blocking interactions and monitoring.
AI-powered browser automation server for web interaction, data extraction, and research using the Model Context Protocol.
Framework for multi-agent collaboration using the Google A2A protocol. Enables messaging, task delegation, and cross-agent coordination for CLI-based AI tools.
Framework for building, registering, and orchestrating Model Context Protocol (MCP) tools and AI agent workflows within the Hive native Rust architecture.
Epistemic safety analysis for JSON data in prompts to prevent LLM hallucinations and reasoning errors when handling incomplete or large-scale datasets.