Looker IngestSAFE

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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: looker_ingest
description: Extract durable ktx knowledge and semantic-layer contribution proposals from staged Looker runtime dashboard, Look, and explore JSON. Load for WorkUnits whose raw files are under explores/, dashboards/, or looks/.
callers: [memory_agent]
---

# Looker Runtime Ingest

Looker runtime ingest turns API-staged dashboards, Looks, and explores into durable ktx memory. Runtime entities are evidence. They are not themselves the final knowledge shape.

## Required Workflow

1. Read every `rawFiles` entry for the WorkUnit.
2. Read relevant `dependencyPaths` before making a decision. For dashboard and Look WUs this usually includes the referenced explore JSON, signal files, `folders/tree.json`, and `users/<id>.json`.
3. Treat `signals/*.json`, owners, folders, schedules, and favorites as prioritization or provenance context only.
4. Extract generalizable metric formulas, segment definitions, field semantics, and domain conventions.
5. Use `wiki_search`, `sl_discover`, and `sl_read_source` before writing so new content merges with existing memory instead of duplicating it.
6. Use `context_evidence_search` or `context_evidence_read` to obtain evidence chunk IDs for any wiki-bound knowledge candidate.
7. Use `context_candidate_write` for durable wiki-bound knowledge. Do not call `wiki_write` from a Looker WorkUnit; Stage 4 reconciliation promotes candidates and writes wiki pages.
8. Use `looker_query_to_sl` for each Look query or dashboard tile query that has a `query` object.
9. Write SL from Looker runtime evidence only through the staged warehouse target contract. For explores and inherited dashboard/Look queries, branch on `targetTable.ok`; when it is true, write on `targetWarehouseConnectionId` and use `targetTable.canonicalTable` as `source.table`. When it is false or missing, write wiki knowledge candidates and record `emit_unmapped_fallback` with the staged reason.
10. Run `sl_validate` after every SL write. If validation fails, fix the source or roll it back before the WorkUnit ends.

For every Looker field reference, call entity_details on the underlying
schema.table.column before promoting it to sl_refs or quoting it in wiki body.

## 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.

## Explore WorkUnits

Explore WUs have raw files like `explores/<model>/<explore>.json` and usually depend on `lookml_models.json`.

Use the deterministic API-derived source key:

```text
looker__<model>__<explore>
```

For example, `modelName: "b2b"` and `exploreName: "sales_pipeline"` map to `looker__b2b__sales_pipeline`.

Mapped explore write shape:

```json
{
  "connectionId": "22222222-2222-4222-8222-222222222222",
  "sourceName": "looker__b2b__sales_pipeline",
  "source": {
    "name": "looker__b2b__sales_pipeline",
    "table": "proj.dataset.opportunities",
    "grain": ["opportunity_id"],
    "columns": [
      {
        "name": "opportunity_id",
        "type": "string"
      },
      {
        "name": "arr",
        "type": "number"
      }
    ],
    "measures": [
      {
        "name": "total_arr",
        "expr": "sum(arr)"
      }
    ]
  }
}
```

Every concrete value in that example must be backed by raw Looker field SQL, `source_tables` preflight, `source_columns`, or existing SL when applied to a real WorkUnit. If the evidence is not present, write wiki candidates and emit `emit_unmapped_fallback`.

The staged explore file carries warehouse target fields populated before the WU starts:

- `connectionName`: the Looker runtime connection name.
- `targetWarehouseConnectionId`: the resolved warehouse connection id, or `null` when the Looker connection is unmapped.
- `rawSqlTableName`: Looker's verbatim `sql_table_name`. Keep it as provenance only.
- `targetTable`: the parsed target-table union. Use this as the sole branch condition.

When `targetTable.ok === true`, the explore has a complete ktx backing target. Before writing:

1. Use `targetTable.catalog`, `targetTable.schema`, and `targetTable.name` for `source_tables` preflight matching through `sl_discover` or `sl_read_source`.
2. Use Looker field `sql`, labels, descriptions, and type metadata to derive source columns, measures, segments, joins, and grain.
3. Call `sl_write_source` or `sl_edit_source` with `connectionId: targetWarehouseConnectionId` and `rawPaths` set to the staged explore path.
4. Set `source.name` to the deterministic API-derived source key, for example `looker__b2b__sales_pipeline`.
5. Set `source.table` to `targetTable.canonicalTable`.
6. Run `sl_validate` after every SL w

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.