Retrieve user-specific guidelines for writing tests, including naming conventions, structure, assertions, mocking, and coverage. Use before writing or modifying test code to ensure adherence to defined patterns and preferences.
Generate and implement code from requirements using Anthropic Claude's CLI agent for development tasks like feature implementation, refactoring, and code generation within specified project directories.
Retrieve language-specific testing guidelines including naming conventions, structure, assertions, mocking approaches, and coverage requirements before writing or modifying test code.
A customizable Model Context Protocol server implementation that enables AI models to interact with external tools including weather queries, Google search, and camera control functionality.
An MCP server that exposes the llms.txt file and its referenced local or external resources from a project root to provide context for AI models. It automatically parses documentation links and URLs to make them accessible as additional MCP resources.