self_reflection

When the user asks you to review your memories, check what you know, clean up your knowledge, or when you notice a potential contradiction in recalled memories, apply this process:

Contradiction Detection

1. Flag Conflicts - When recalled memories contradict each other, surface the conflict to the user immediately. Do not silently pick one.
2. Ask, Don't Assume - Say exactly what conflicts you see and ask the user which version is correct. Then update the wrong memory and confirm the fix.
3. Check Dates - When you see a memory that might be outdated, mention it and ask whether it is still current.

Memory Audit

1. Get the Baseline - Start with wiki_stats to see total articles, conversations, and vault health. Then use search_memory with broad terms to scan for coverage gaps.
2. Systematic Sweep - Use search_memory with broad category queries such as person, preference, fact, event, project, and place. Use explore_connections to visualize relationships and spot gaps.
3. Review Quality - Look for duplicates, stale entries, user-only connections, and missing links.
4. Fix With Consent - Update or link_memories during the audit when the user has confirmed the correction. Confirm each change.
5. Rebuild After Cleanup - After bulk updates, run wiki_rebuild to regenerate the wiki vault.
6. Summarize - After the audit, give a brief count of memories reviewed, updated, and linked, and flag anything that needs the user's input.

Ongoing Awareness

1. Correction Logging - When the user corrects you on a fact, update the existing memory and briefly acknowledge the correction.
2. Confidence Signals - If you recall a memory but are not confident it is still accurate, say so and ask.

Insights And Evolution

1. Check Automated Insights - During reflection, use row_bot_status with category insights to see active insights and linked proposals. Use category evolution to inspect proposals, action runs, rejection memory, and curator dry-run summaries.
2. Present Controlled Actions - For each active insight, summarize the category, severity, suggestion, linked proposal type, risk, confidence, and action status. Group related items by category.
3. Use Proposals, Not Direct Edits - Do not edit insights.json, memory files, skills, tool guides, settings, or code directly during reflection. For skill improvements, use row_bot_create_skill or row_bot_patch_skill to create proposals only, then ask the user to preview and approve with row_bot_apply_proposal.
4. Send Feedback Separately - For app bugs, tool/config problems, or system-health issues, create a redacted row_bot_send_feedback proposal instead of turning the issue into a skill. Do not include full logs or diagnostic bundles unless the user explicitly approves; the user can copy the report or submit it through the Row-Bot contact page.
5. Learn From Outcomes - If the user rejects a proposal, record the reason with row_bot_reject_proposal. Mark proposals verified only after explicit validation or user confirmation with row_bot_verify_proposal.