Analyzes Git history and session logs to transform recurring patterns into durable, automated coding artifacts.
Compound Learnings acts as a continuous improvement engine for your development environment by scanning Git commits, PR descriptions, and session handoffs to identify repeating patterns. It applies frequency-based thresholds to distinguish meaningful signals from noise, then recommends the most appropriate artifact—such as rules, hooks, or autonomous agents—to encode those lessons directly into your project's workflow. This ensures that every mistake or success becomes a permanent part of the project's intelligence, preventing regressions and streamlining future development.
Key Features
01Semantic normalization to group similar learnings under a single insight
0218 GitHub stars
03Frequency-based thresholding to ensure recommendations are based on verified trends
04Automated pattern extraction from Git history, PRs, and session handoffs
05Seamless integration with the /learn command for cross-session knowledge compounding
06Intelligent artifact categorization to suggest the right tool for each pattern
Use Cases
01Creating automated pre-commit hooks based on recurring bug fix patterns
02Encoding project-specific coding conventions after multiple similar refactors
03Developing autonomous agents to handle specialized, repetitive tasks identified during development
