instinct-system

Pattern extraction from conversation history involves identifying successful workflows or tool tricks and saving them as instinct records. These records include structured metadata like triggers, evidence, tags, and confidence scores to ensure proven methods are retained and validated over time.

Confidence scoring for repeatable assistant behaviors uses a 1-5 rating system based on repeated validation. Assign and update scores as patterns are tested, ensuring only proven methods rise over time while preventing untested workflows from dominating your active patterns.

Exporting instinct records between environments uses structured metadata formats containing triggers, evidence, tags, and dates. Move these lightweight records across platforms, search them later, and periodically verify whether captured behaviors still hold up in new contexts.

Session learning can evolve isolated patterns into a full skill through clustering. When high-confidence instincts become stable enough for broader reuse, group them together to form a complete, structured skill that can be applied across broader contexts.

Structured metadata is required to store knowledge from chat history as instinct records. You must provide triggers, evidence, tags, dates, and exportable record formats to ensure captured patterns can be properly searched, verified, and evolved over time.