learner

You can capture conversation insights as reusable knowledge by documenting high-signal learnings into a structured YAML format with name, description, and a Workflow body defining Problem, Solution, Triggers, and Scope.

The best way to document non-obvious patterns is to codify them into structured expertise artifacts, classifying the learning into either Expertise or Workflow categories to preserve and validate decision-making heuristics.

Structure deep learnings using a YAML frontmatter containing the name and description, followed by a structured body that follows the Workflow format to create repeatable onboarding artifacts for teammates.

Yes, this skill development format is designed for collaborative knowledge work and debugging contexts where non-obvious patterns emerge during discussion or design, allowing teams to rapidly apply domain knowledge.

The need to codify learnings is triggered when preserving non-googleable knowledge and surfacing decision-making heuristics that emerge during collaborative discussions or complex design sessions.

Yes, capturing learned skills requires YAML frontmatter with name and description fields, plus a structured body classifying the content as either Expertise or Workflow to ensure the knowledge is reusable.