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You can capture contextual corrections as persistent methodology notes by using explicit, contextual, or session mining modes to identify friction points. The system stores these actionable notes in ops/methodology with deduplication and provenance metadata.

Tracking learning friction across sessions is done through session mining mode, which identifies correction patterns across conversations. It automatically files these friction points as categorized methodology notes with session source provenance.

Yes, you can automatically deduplicate methodology notes when capturing new friction points. The system links new corrections to existing notes in the ops/methodology directory, preventing redundant entries while preserving original provenance metadata.

You can document workflow friction points without interruption by using contextual review mode, which identifies corrections during interactions. This automatically extracts friction and files it to ops/methodology without requiring explicit manual input.

Explicit mode requires a simple text description of the friction point to create an actionable note. It uses this description to generate a methodology note filed in ops/methodology, complete with categorization and mode provenance metadata.

Use session mining instead of explicit description when you need to identify correction patterns across past conversations rather than a single friction point. Session mining extracts methodology notes from historical interactions, while explicit mode captures immediate user-described corrections.