instinct-extractor

To extract behavioral patterns from raw interaction logs, this Skill processes JSONL observation files to identify recurring sequences, automatically generating structured instincts with confidence scores and updating YAML definitions based on the observed data.

An atomic instinct in AI behavioral analysis is a structured rule generated from recurring interaction patterns, assigned a confidence score to formalize successful tool usage sequences for improving AI decision-making pipelines and contradiction detection.

Processing JSONL observation files to update YAML instinct definitions requires parsing raw session data to detect repeatable behavioral sequences, evaluating contradictions, and adjusting confidence scores within a defined schema.

Yes, you can use this pattern recognition approach for AI learning pipelines without external dependencies, as it processes session data internally to generate instincts and update confidence scores directly within your environment.

Contradiction detection works by identifying conflicting behavioral patterns within JSONL observations, automatically adjusting the confidence scores of associated instincts to ensure accurate AI decision-making formalization.

Limitations of automated instinct generation include reliance on the quality of JSONL observation data and the defined YAML schema, requiring sufficient recurring patterns to establish meaningful confidence scores for behavioral analysis.