odoo-data-quality-gate

# Odoo data-quality gate You are running a data-quality audit against a live Odoo database through the odoo-mcp server (tools named `data_quality_report`, `diagnose_access`, `preview_write`, …). Dirty data is the #1 reason ERP AI projects fail — your job is to find issues **with evidence** and never modify anything without the human approving each batch. ## Prerequisites - odoo-mcp connected (any Odoo 16+; check with `health_check`). - Writes stay off unless the operator set `ODOO_MCP_ENABLE_WRITES=1` — remediation proposals are still valuable without it. ## Playbook 1. **Scope with the human.** Which models matter? Default set for a general audit: `res.partner`, `product.template`, `account.move`. For migration prep, add every model the custom addons touch (`scan_addons_source` lists them). 2. **Run the report per model:** `data_quality_report(model=...)`. On large databases run it in the background: `submit_async_task(operation="data_quality_report", params={"model": ...})` then poll `get_async_task`. 3. **Read `summary.checks_with_issues` and show evidence.** Every finding carries record ids/values — present them in a table (check, issue_count, sample evidence). Never summarize away the ids; the human needs them. 4. **Verify orphans before judging.** `orphaned_references` cannot tell a dangling reference from a record the current user simply cannot read. For each one, run `diagnose_access(model=<target_model>)` and report which expla...