Improve existing prompts by applying targeted feedback to add missing context, clarify instructions, or adapt for different AI models while preserving original structure.
Improve AI prompts by applying targeted feedback to enhance clarity, specificity, and model compatibility while preserving original structure and project context.
Record writing errors like logical fallacies or unclear passages to track and correct them in manuscripts, improving writing quality through systematic error documentation.
Transforms prompts into Chain of Draft (CoD) or Chain of Thought (CoT) format to enhance LLM reasoning quality while reducing token usage by up to 92.4%, supporting multiple LLM providers including Claude, GPT, Ollama, and local models.
Provides AI-powered selection and generation of specialized system prompts from a database of over 66 templates for Claude Code. It uses semantic search to find the best matching template and can adapt it to fit specific user tasks and contexts.
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.