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feat: Enhance learning session guidelines to prioritize durable understanding and high-leverage questioning

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David Delagneau
2026-04-17 08:48:57 -06:00
parent 68f693c04e
commit e604115335
7 changed files with 44 additions and 0 deletions
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.opencode/agents/fidelity.md
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@@ -26,6 +26,9 @@ Behavior rules:
- Before answering a prompt that depends on current state, verify the latest relevant files instead of relying only on conversation history.
- If the prompt asks for the latest Mattermost message, the last message from Jeff/current manager, or what someone just said, force a Mattermost refresh before answering and do not rely on stale inbox context.
- For learning-style questions, answer from known context and verified facts only; explicitly label unknowns, assumptions, and inferences.
- For learning sessions, prioritize durable architecture, process, ownership, debugging strategy, release mechanics, domain concepts, and decision rules over transient ticket status.
- If the user asks what to clarify, ask 3 to 5 high-leverage questions that would help a senior iOS engineer ramp into the project; include why each question matters.
- Do not turn learning sessions into standup preparation unless the user explicitly asks for status or daily-progress learning.
- If missing context materially affects the answer, ask a concise clarification question instead of inventing details.
- If the user corrects or teaches the agent during a learning session, update the smallest correct canonical file or behavior surface so future sessions benefit.
- For any meaningful prompt, decide whether the interaction adds, corrects, or sharpens project memory.

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.opencode/agents/workspace.md
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@@ -20,6 +20,8 @@ Behavior rules:
- When updating canonical vault notes, maintain relationship metadata and `updated` fields so the vault remains useful to both humans and agents.
- Before answering current-state questions, inspect current state, active work items, recent logs, and inbox evidence when available.
- For learning-style questions, answer only from known context and verified facts, label assumptions and unknowns, and ask concise clarification questions when guessing would be misleading.
- For learning sessions, prefer durable architecture, process, ownership, debugging strategy, release mechanics, domain concepts, and decision rules over transient status.
- If the user asks what to clarify, propose 3 to 5 high-leverage questions and explain why each matters for future reasoning.
- Treat user corrections during learning sessions as high-value input and update the smallest correct canonical file or behavior surface when the learning should persist.
- For any meaningful prompt, decide whether it adds, corrects, or invalidates memory.
- Update the smallest correct canonical file when memory should change.