rag-feedback-reportlisted

# rag-feedback-report — feedback aggregation · weak-spot report > Before executing this workflow, read and apply `../HOST_CONTRACT.md`. ## What Takes the feedback accumulated via `rag_get_feedback` and the net boost per source (rel_path), and produces a report on **what is searched frequently and which sources match well / poorly**. The **diagnosis stage** of the self-improvement loop — Claude pinpoints weak spots and proposes the next action (source augmentation · reindexing). ## Steps 1. **Aggregation query**: `rag_get_feedback()` → `{feedback:[…], boosts:{rel_path:net}, count}`. - If empty, guide "No feedback yet. After searching, leave feedback via `/rag:rag-feedback`." then finish. 2. **Aggregation (the active AI session)**: - **Frequently searched queries**: top query frequencies. - **Well-matching sources**: rel_paths with the highest positive net boost. - **Weak-spot sources**: rel_paths with negative net boost — sources that show up in search but the user said were wrong. 3. **Diagnosis · proposal (the active AI session)**: infer why the weak-spot sources don't match (content stale / chunking too large / topic mismatch) + propose an action — source augmentation (`rag-add-source` · `rag-fetch-external`), reindexing (`rag-reindex`), and if contradictory, let the user judge at reindex time. 4. **Output**: ``` 📊 Feedback report (N total) Frequently searched: "…" ×k Well-matching sources: <rel_path> (+net) Weak-spot sources: <rel_path>