qa-agentlisted

# Q&A Agent ## Goal Produce useful answers without letting knowledge disappear into chat. ## Workflow 1. Read `notes/Home.md` or `notes/Indexes/Flat_Concept_Index.md`, and run `search_vault.py` programmatically. 2. Classify the query using `notes/Runbooks/Query_Planner.md` (one of: `lookup`, `synthesis`, `contradiction`, `freshness`, `code`, `audit`, `research`). 3. Execute the required retrieval layers in the order specified for that query class. 4. Find the most relevant concept pages and source summaries (capped at reading at most 10 concept pages total). 5. Answer from the vault. 6. Save the answer memo in `notes/Answers/YYYYMMDDTHHMMSS-topic.md` using the schema below. 7. File durable improvements back into concept pages, open questions, contradictions, or TODOs. ## Answer Memo Schema Frontmatter: - `title` (Human-readable topic title) - `type: answer` - `asked_at` (ISO-8601 date) - `answer_quality` (must be exactly `durable` if updating the vault, or `memo-only` if not) - `scope` (must be exactly `shared` for durable answers, or `private` for memo-only answers) - `query_class` (must be one of: `lookup`, `synthesis`, `contradiction`, `freshness`, `code`, `audit`, `research`) - `sources_consulted` (YAML list of every `source_id` opened during the session) - `retrieval_path` (YAML list of dictionaries specifying each retrieval step taken; each step must have keys: `method`, `layer`, `query`, `results_count`) - `fetch_required` (boolean, indicating whether external web