Skill UI

Provides an end-to-end kit for building Microsoft 365 Copilot declarative agents across basic, enterprise, and validation workflows, with TypeSpec definitions, JSON schema validation, and Agents Toolkit integration for planning, testing, deployment, and optimization.

A comprehensive meta-skill template designed for developers to scaffold, structure, and define specialized Agent Skills for AI platforms like GitHub Copilot. It guides users through mandatory requirements, including folder naming conventions, YAML frontmatter best practices (name, description), and structuring the skill body with detailed workflows and documentation sections. Use this when building complex, specialized AI capabilities.

This guide provides proven architectural patterns for building complex, reliable, and scalable workflows using n8n. It covers core patterns like Webhook processing, HTTP API integration, database synchronization, AI agent design, and scheduled tasks. Learn how to select the right pattern and utilize advanced components for robust automation.

The Phoenix CLI is a powerful tool for debugging complex LLM and agent workflows. It allows users to fetch detailed traces, spans, and sessions, analyze errors, and structure findings using open and axial coding techniques. It is essential for investigating complex LLM failures, building robust failure taxonomies, and guiding the next steps in application evaluation after initial instrumentation.

A comprehensive automation toolkit for managing Zendesk operations via Rube MCP. It provides workflows to list, search, create, update, and delete support tickets, manage user accounts (agents and end-users), and handle organization details. Ideal for integrating support workflows and automating customer service processes.

Consult another agent for help, plans, reviews, or decision support in multi-agent workflows. Explains using list_agents() and chat_with_agent(), background submission and status checks, session ID reuse, and loop-avoidance best practices.

Production-ready reinforcement learning library with PPO, SAC, DQN, TD3, DDPG, A2C algorithms. Use for training RL agents, creating custom environments, implementing callbacks, and optimizing training workflows. Best for single-agent RL with Gymnasium environments. Requires Python 3.10+ and PyTorch >= 2.3.

The AI Dev Jobs MCP provides AI agents with real-time access to a massive index of AI and Machine Learning job listings across hundreds of companies. Users can search by role, location, or company, retrieve full job details, analyze salary ranges, list hiring companies, and get aggregate market statistics. It is essential for advanced job searching, talent matching, and labor market analysis workflows.

This comprehensive guide details the full plugin architecture for extending Claude Code. Learn how to build various extension points, including auto-activating skills, slash commands, specialized agents, and Model Context Protocol (MCP) servers. It serves as the definitive reference for integrating custom tools and workflows into the Claude ecosystem.

This guide demonstrates how to build advanced AI agents using Anthropic's Tool Use (Function Calling) feature. It allows Claude to dynamically determine when external actions are needed, such as querying databases, calling APIs, or reading files. The pattern involves sending the user query, receiving a `tool_use` request, executing the corresponding function in your code, and submitting the result back to Claude for a final, coherent response. Essential for building multi-step, agentic workflows.

This guide demonstrates how to implement tool use (function calling) with Claude's Messages API. It covers defining structured tools using JSON Schema, handling tool calls, executing the corresponding code, and feeding the results back to create sophisticated, multi-step AI agents and complex workflows.

This skill provides a comprehensive framework for deploying Apple Notes automation as a persistent local service on macOS. It supports three distinct deployment models: using `launchd` agents for scheduled background tasks, creating Shortcuts for user-triggered actions, and includes a robust installer script. It is essential for building cross-application, system-level automation workflows within the Apple ecosystem.