iflow-mcp_kordless-gnosis-mystic

# Gnosis Mystic 🔮 **AI-Powered Python Function Analysis and Control** Gnosis Mystic gives AI assistants direct access to your Python functions through runtime hijacking and intelligent analysis. Add minimal decorators, and Claude can inspect, optimize, and control your code in real-time. ## Inspiration and Work Mystic was inspired by [Giantswarm's](https://giantswarm.io) [mcp-debug](https://github.com/giantswarm/mcp-debug). Code by fairly stock Claude Code. Prompts, code sketches, and planning by Claude Desktop using Gnosis Evolve tools. ## ✨ Why Gnosis Mystic? ### The Problem AI assistants are blind to your running code: - They can't see function performance in real-time - No direct access to runtime behavior and state - Can't dynamically test optimizations - Limited to static code analysis - No way to experiment with function modifications safely ### The Solution Gnosis Mystic creates a **direct AI-to-code interface**: - **AI sees everything**: Real-time function calls, performance, and behavior - **Safe experimentation**: Test caching, mocking, and optimizations instantly - **Runtime control**: AI can modify function behavior without code changes - **Intelligent analysis**: AI discovers bottlenecks and suggests improvements - **Live debugging**: AI can inspect function state during execution ## 🚀 Core Capabilities ### 1. AI-Visible Function Monitoring ```python @hijack_function(AnalysisStrategy()) def fetch_user_data(user_id): response = requests.get(f"https://api.example.com/users/{user_id}") return response.json() # Claude can now see: # - Call frequency and patterns # - Performance metrics # - Parameter distributions # - Error rates and types ``` ### 2. AI-Controlled Optimization ```python # You add minimal decoration @hijack_function() def expensive_calculation(data): # Your logic unchanged return complex_math(data) # Claude can experiment with: # - Adding caching strategies # - Performance profiling # - Mock data for testing # …