✈️ 24-tool MCP server for Claude Code: preflight checks for your prompts, cross-service context, session history search with LanceDB vectors, correction pattern learning, cost estimation

Quantifying prompt quality using information theory: entropy and mutual information analysis of 1,800 LLM generations

Prompt Mirror — catch ambiguity before you hit Enter. 11 rules × live web search × zero storage. | 照妖镜 — 按 Enter 前照一下 prompt，含混处现查行业数据

📊 Explore how Shannon entropy and mutual information can quantify prompt quality in generative AI systems across various temperature settings.

Multi-judge static quality scoring for LLM prompt libraries, with inter-judge agreement as a per-prompt confidence signal.