Analyzes raw interaction logs to extract recurring behavioral patterns and refine AI decision-making.
Instinct Extractor serves as a core learning engine for Claude Code, transforming raw tool call observations into structured, actionable 'instincts.' By processing JSONL interaction data, the skill identifies frequent sequences, establishes confidence scores, and detects behavioral contradictions. This allows the AI to develop a specialized understanding of specific project requirements and user habits, essentially creating a self-improving workflow that adapts to the unique needs of a codebase or development environment over time.
Key Features
010 GitHub stars
02Schema-driven data validation to ensure learning integrity
03Contradiction detection to identify and flag inconsistent AI actions
04Structured YAML output for persistent AI memory and evolution
05Automated pattern recognition from raw interaction sequences
06Dynamic confidence scoring and decay for behavioral instincts
Use Cases
01Building a self-evolving coding assistant that learns from developer preferences
02Automating the transition from raw project observations to structured workflow rules
03Refining AI behavior after significant development milestones or project pivots
