Provides actionable suggestions to improve code quality by analyzing and optimizing for performance, readability, maintainability, accessibility, or type safety based on user-defined focus and priority.
Filter and retrieve AI-generated responses by prompt, message, or conversation ID in Carbon Voice. Narrow results with combined filters to view all responses tied to specific interactions.
Ask targeted questions to clarify user requirements and improve the prompt engineering process, ensuring accurate and optimized outputs for Claude Code.
Analyze files or directories using a prompt-based approach. Customize output formats like text, JSON, or markdown for efficient repository analysis and reduced token usage.
Generate structured prompt templates and context schemas from user requirements or existing prompts, enabling testable and adaptable AI interactions. Integrates with MCP server for efficient prompt management and evaluation.
A simple MCP server implementation in TypeScript that communicates over stdio, allowing users to ask questions that end with 'yes or no' to trigger the MCP tool in Cursor.
Intelligently analyzes codebases to enhance LLM prompts with relevant context, featuring adaptive context management and task detection to produce higher quality AI responses.
A lightweight MCP server that provides a unified interface to various LLM providers including OpenAI, Anthropic, Google Gemini, Groq, DeepSeek, and Ollama.