Metabase IngestSAFE

ktx is an executable context layer for data and analytics agents 🐙 Allow Claude Code, Codex, or other AI agents to query analytical databases accurately and with full context of your company

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: metabase_ingest
description: Convert Metabase questions, models, and metrics into ktx Semantic Layer source definitions. Covers result-metadata to KSL column type mapping, FK/PK detection, near-duplicate deduplication, pre-aggregation decomposition, join-graph connectivity, and how to react to priorProvenance from earlier ingest syncs. Load when the WorkUnit contains `cards/<id>.json` files under a Metabase bundle.
callers: [memory_agent]
---

# Metabase to ktx Semantic Layer

Each WorkUnit represents one Metabase collection's cards for one Metabase database (mapped to exactly one ktx connection). Every `cards/<id>.json` file carries the resolved SQL, result_metadata, card type, collection path, and referenced-card ids. The WU's `sync-config.json` tells you which sync mode is active and which selections apply. `databases/<id>.json` tells you the target ktx connection.

## Context format

Each card JSON looks like:
```json
{
  "metabaseId": 7,
  "name": "Daily orders",
  "description": "Orders by day",
  "type": "model",
  "databaseId": 42,
  "collectionId": 5,
  "resolvedSql": "SELECT ...",
  "templateTags": [{"name": "ref", "type": "card", "cardReference": 10}],
  "resultMetadata": [
    {"name": "day", "base_type": "type/DateTime", "semantic_type": "type/CreationTimestamp"},
    {"name": "order_count", "base_type": "type/Integer"}
  ],
  "collectionPath": ["Data", "Orders Team"],
  "referencedCardIds": [10]
}
```

Use `resultMetadata` to:
- Map `base_type` to KSL column type: `type/Integer`, `type/Float`, `type/Decimal`, `type/BigInteger` → `number`; `type/Text`, `type/TextLike` → `string`; `type/DateTime`, `type/Date`, `type/DateTimeWithTZ` → `time`; `type/Boolean` → `boolean`.
- Identify grain candidates: columns with `semantic_type: type/PK`.
- Identify join candidates: columns with `semantic_type: type/FK` plus `fk_target_field_id`.
- Identify time columns: `semantic_type: type/CreationTimestamp` or `type/UpdatedTimestamp` → set `role: time`.
- Use `display_name` for measure descriptions when available.

### Additional card metadata

- `parameters`: list of card-level parameters with widget types and defaults. When SQL resolution fell back to unresolved SQL, use this to drive Step A of the SQL-translation workflow (drop optional clauses): knowing each `{{ var }}` is `type: "date/range"` vs `type: "category"` tells you what kind of clause it is.
- `resultMetadata[i].field_ref`: Metabase's canonical reference to the source warehouse field. Shape `["field", <field_id>, <options>]`. When this is set, the column maps directly to a warehouse field, which is useful for declaring joins from FK metadata without re-parsing SQL.
- `lastRunAt`: ISO timestamp of the card's last execution. If null or very old, the card may be dead; prefer skipping over creating a source.
- `dashboardCount`: number of dashboards referencing the card. Cards with `dashboardCount: 0` and a stale `lastRunAt` are strong skip signals.

Before writing a wiki page derived from a Metabase question SQL, verify each
schema.table.column mentioned with entity_details.

## Identifier Verification Protocol

Before writing a wiki page or SL source on any topic:

1. `discover_data({query: "<topic>"})` - see what wikis, SL sources, and raw
   tables already exist. Prefer updating existing pages over creating new ones.

Before emitting any `schema.table` or `schema.table.column` into a wiki body,
SL source, `tables:` frontmatter, `sl_refs`, or `emit_unmapped_fallback`:

2. `entity_details({connectionId, targets: [{display: "<identifier>"}]})` -
   confirm the identifier resolves; inspect native types, FK/PK, and
   sampleValues.
3. For literal values from the source, such as status codes or plan tiers,
   check whether they appear in `entity_details` sampleValues for the relevant
   column. If sampleValues is short or the sample may have missed real values,
   run a `sql_execution` probe with the same warehouse connection id:
   `sql_execution({connectionId, sql: "SELECT DISTINCT <col> FROM <ref> LIMIT 50"})`.
4. If the candidate identifier still does not resolve, do one of:
   - Use `sql_execution({connectionId, sql: "SELECT 1 FROM <ref> LIMIT 0"})`.
     If it errors, the identifier is fictional.
   - Wrap the identifier in `[unverified - from <rawPath>]` in the wiki body,
     citing the exact raw path that mentioned it.
   - When recording `emit_unmapped_fallback` with `no_physical_table`, include
     the failing probe error in `clarification`.
5. Never copy `<schema>.<table>` placeholder strings from these instructions
   into output.

## Decision tree

For each card:
1. Analyze `resolvedSql` + `resultMetadata`: identify base tables, aggregations, joins, filters, column types.
2. **REQUIRED before any write**: call `sl_discover` for every candidate target source name. The response tells you whether the name is manifest-backed (`Type: table` or `Type: sql`). For manifest-backed names you MUST use the overlay shape (`name:` plus overlay fields such as `measures:`, `segments:`, `descriptions:`, `joins:`, `disable_joins:`, `column_overrides:`, and computed-only `columns:` entries with `expr` + `type`; no `sql:`, `table:`, `grain:`, or base-table `columns:`); the tool will reject a standalone write and you'll have wasted the call. If `sl_discover` returns nothing for the name, you can write a standalone source. Also call `sl_read_source` on existing sources you intend to extend so you don't duplicate measures.
3. Include `rawPaths: ["cards/<id>.json"]` on every `sl_write_source`, `sl_edit_source`, and `wiki_write` call. If one artifact generalizes multiple near-duplicate cards, include each contributing card path and no unrelated cards.
4. Decide:
   - Simple aggregation on a table that already has a source → `sl_edit_source` to add a measure.
   - Join between tables that should be linked in the SL graph → `sl_edit_source` to add a join.
   - Complex derived SQL (CTEs, multi-layer aggregation, scoring models) → `sl_writ

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

What the source does

Filesystem

none-observed

Network

none-observed

Shell

none-observed

Dependencies

pinned

Secrets in source

none-found

Findings (3)

CLAUDE.md

GEMINI.md

python/ktx-sl/CLAUDE.md

Gates applied: no_behavioural_pass.