historic_sql_table_digest

# Historic SQL Table Digest

Use this skill when the WorkUnit raw file is one `tables/<schema>.<name>.json` file from the `historic-sql` adapter.

## Required Workflow

1. Read the WorkUnit notes first.
2. Call `read_raw_file` for the single `tables/<schema>.<name>.json` raw file.
3. Read `manifest.json` only if the table JSON omits the dialect or the WorkUnit notes are unclear.
4. Produce one concise usage narrative for this table from the staged table JSON.
5. Call `emit_historic_sql_evidence` exactly once with `kind: "table_usage"`.
6. Stop after the evidence tool succeeds.

## 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.

## Evidence Shape

Call `emit_historic_sql_evidence` with this shape:

```json
{
  "kind": "table_usage",
  "table": "public.orders",
  "usage": {
    "narrative": "Orders are repeatedly queried for paid/refunded lifecycle analysis and customer-level rollups.",
    "frequencyTier": "high",
    "commonFilters": ["status", "created_at"],
    "commonGroupBys": ["status"],
    "commonJoins": [{ "table": "public.customers", "on": ["customer_id"] }],
    "staleSince": null
  }
}
```

The `usage` object must match `tableUsageOutputSchema`.

## Interpretation Rules

- Treat `columnsByClause.where` as common filters.
- Treat `columnsByClause.groupBy` as common group-bys.
- Treat `observedJoins` as common joins.
- Use `stats.executionsBucket`, `stats.distinctUsersBucket`, and `stats.recencyBucket` to choose `frequencyTier`.
- Use `frequencyTier: "high"` only when executions and distinct users are both broad.
- Use `frequencyTier: "mid"` for repeated team usage that is not broad enough for high.
- Use `frequencyTier: "low"` for low-volume but present usage.
- Use `frequencyTier: "unused"` only when the table input explicitly says the table is stale or has no recent templates.
- Keep `narrative` short and concrete.

## Boundaries

- Do not call wiki_write.
- Do not call sl_write_source.
- Do not call sl_edit_source.
- Do not call context_candidate_write.
- Do not emit more than one table usage evidence object.
- Do not invent columns, joins, or tables that are absent from the staged JSON.