distilling

Extract structured hypotheses and growth signals from raw user canvas markdown by analyzing the canvas state, growth state, and provenance records to generate structured cognition output. This distillation process surfaces actionable insights by converting raw narratives into a structured YAML format.

A cognition sidecar is a structured YAML document containing fears, steering targets, and synthesis features distilled from user canvases. You need to generate one for product and research handoffs to provide minimal, actionable context that guides decisions and conversations without requiring the full canvas.

Analyze canvas state by consuming inputs like canvas markdown, growth state, provenance records, and configuration to produce structured cognition output. The analysis extracts hypotheses, gaps, and growth signals, returning a YAML document that validates against the cognition sidecar schema with defined fears and steering targets.

Yes, you can use canvas markdown and provenance records to identify user fears and gaps in onboarding feedback. The skill analyzes these inputs to extract hypotheses and growth signals, distilling the onboarding narratives into structured fears and steering targets for research handoffs.

The best way to distill research canvases into structured YAML for growth experiments is to feed the latest user canvas and growth state into an analysis process that outputs a single YAML document. This document includes schema version, user data, fears, steering targets, and synthesis features.

Yes, the cognition sidecar schema requires specific fields including schema version, user, fears, steering targets, and synthesis features. The YAML output must validate against this schema to ensure the structured cognition contains all necessary elements for guiding decisions.