Skill UI

This CLI alias implements the PUA Loop protocol, designed for running structured, iterative verification cycles. It helps developers set up and manage complex coding tasks that require explicit verification commands and defined stop conditions. It serves as a powerful wrapper for AI coding assistants like Codex/Claude, ensuring robust and traceable development workflows.

FAF Expert is an advanced tool for creating standardized, persistent context for complex software projects. It uses the IANA-registered FAF format to transform codebases into AI-intelligent assets. Use it for multi-AI workflow management (Claude, Gemini, etc.), enterprise-grade deployment, and standardizing project context across development lifecycles.

Build a Python Model Context Protocol (MCP) server with the official MCP Python SDK v2: uv project setup, typed tools and error handling, in-memory pytest tests, and client configuration for VS Code or Claude Desktop.

Analyzes and generates a self-contained, explorable HTML report detailing usage metrics for Claude Code sessions. It tracks token consumption (input/output), subagent usage, skill calls, and cache efficiency across specified time ranges. This tool helps developers visualize resource waste and pinpoint areas for workflow optimization, ensuring cost-effective and efficient AI development cycles.

A comprehensive guide for developers encountering complex issues with the Claude API, including context window overflow management, debugging tool use failures, handling streaming corruption, and diagnosing unexpected stop reasons. Provides Python code snippets and diagnostic commands to ensure robust AI integration.

A comprehensive guide detailing five robust architectural patterns for building applications powered by Claude, including Chatbot, RAG, Agent, Content Pipeline, and Evaluation. It provides complete code examples and decision matrices to help developers select the optimal architecture based on project requirements, cost, and complexity.

A comprehensive tool designed to help developers gather all necessary evidence when facing persistent issues or API errors with Anthropic's Claude models. It collects vital data points such as the request ID, full error payloads, status codes, and provides reproducible cURL commands, ensuring developers can file high-quality support tickets.

This guide provides a deep dive into migrating applications from OpenAI's GPT models to Anthropic's Claude API. It covers crucial API differences, including endpoint changes, response structure updates, handling the system prompt, tool use restructuring, and necessary SDK code swaps (e.g., TypeScript examples). Essential resource for developers transitioning AI backends.

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.

A comprehensive guide for developers on upgrading the Anthropic SDK versions and migrating between different Claude model generations. It covers best practices for version checking, reviewing breaking changes, updating model IDs across the codebase, running output comparison tests, and implementing gradual rollouts using environment variables to ensure stable deployment.

A comprehensive guide for diagnosing and fixing common errors encountered when integrating with the Anthropic API (Claude). It covers critical issues such as authentication failures (401), rate limiting (429), server overload (529), invalid requests, and context window limits. Includes practical code solutions and best practices for robust API integration.

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.