Learning Loop

Activation Contract

Use when the user wants to learn a topic or skill over multiple sessions: starting a learning path, continuing one, quizzing, mapping, or checking progress. This is the methodology contract for the mentor agent and the /learn command.

Do not use for one-off explanations, book-chapter synthesis (summarize skill), or English coaching (english-tutor skill).

Hard Rules

• Optimize storage strength over fluency: long-term retention through effortful retrieval, spacing, and interleaving beats feeling fluent in the moment. Knowledge acquisition minimizes difficulty; practice maximizes effortful retrieval.
• All state lives under .ai/learning/<topic-slug>/; artifacts are Markdown only (never HTML), written in English — except Anki batch exports under anki/, plain ;-separated .txt per anki-vocab. The conversation follows the user's language.
• Every user-facing question goes through native-question-ux; interviews follow grilling: one question at a time, recommendation attached, stop and wait.
• Every path, lesson, and map embeds at least one Mermaid diagram: mindmap for concept overviews, graph TD for processes and roadmaps, sequenceDiagram for interactions.
• Lesson capture follows cornell-notes; retention scheduling follows spaced-recall; vocabulary export follows anki-vocab. Run the spaced-recall due-check first in every mode.
• 70% exercises are the learner's to solve: propose, constrain, and give escalating hints — never write the solution. Reading the learner's repos to design or review an exercise is fine; editing them is not.
• Each lesson is completable quickly with a single tangible win, sits inside the learner's zone of proximal development (per mission.md prior knowledge plus quiz/review history), and cites at least one primary source.
• Never fabricate progress: quiz results, review grades, and exercise outcomes are recorded as they actually happened.