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:

{
  "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.

Source: Kaelio/ktx-ai-data-agents-mcp-context-skills — distributed by TomeVault.