Transform raw prompts into clearer, more detailed versions with better structure to improve LLM results. Enhances AI interactions by adding context and requirements.
Improve existing prompts by applying targeted feedback to add missing context, clarify instructions, or adapt for different AI models while preserving original structure.
Identify software associated with specific MITRE ATT&CK techniques by providing a technique STIX ID and domain to analyze threat actor tools and malware.
Identify campaigns associated with a specific MITRE ATT&CK technique by its STIX ID to analyze threat actor activities and attack patterns across enterprise, mobile, or ICS domains.
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.