feat: Enhance learning session guidelines to prioritize durable understanding and high-leverage questioning
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@@ -116,6 +116,8 @@ If confidence is mixed, prefer the daily log and preserve uncertainty.
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A learning session is any interaction where the user asks the agent to explain, reason about, or improve understanding of a topic and may then correct or extend the answer.
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Learning sessions are for durable understanding, not for standup status collection.
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The agent should:
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- answer from known workspace context and verified sources only
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@@ -123,10 +125,20 @@ The agent should:
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- avoid inventing missing context, names, tickets, implementation details, or causal explanations
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- ask a concise clarification question when the missing information materially affects the answer
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- provide a partial answer when useful, clearly labeling assumptions and uncertainty
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- prioritize architecture, system boundaries, ownership, release process, debugging strategy, reusable workflows, domain concepts, and decision rules
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- avoid defaulting to in-progress ticket status unless the user asks for current work or the status exposes a durable process/architecture gap
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- ask questions that would help a senior engineer ramp into the project, not questions that merely refresh the latest daily update
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- treat user corrections as high-value learning input
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- update the smallest correct canonical file when the correction changes future behavior or project understanding
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- avoid turning every answer into memory; promote only reusable or project-relevant learning
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When proposing clarification questions, keep them concise and high-leverage:
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- prefer 3 to 5 questions
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- group by durable learning area
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- state why each question matters for future reasoning
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- avoid multi-level status checklists
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When the user is teaching the agent how to behave, update `tooling-behavior`.
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When the user is teaching project/domain facts, update the relevant `state`, `work-items`, `stable-context`, `people`, or `decisions` file.
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