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historic_sql_patterns

This Claude Code skill identifies recurring analytical patterns that join two or more tables within a bounded shard of historic SQL data, then emits typed pattern evidence for wiki documentation. Use it when processing pattern input files from the historic-sql adapter to discover durable cross-table query intents backed by repeated usage signals, ensuring all join columns are verified against actual entity schemas before emission.

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git clone --depth 1 https://github.com/Kaelio/ktx /tmp/historic_sql_patterns && cp -r /tmp/historic_sql_patterns/packages/cli/src/skills/historic_sql_patterns ~/.claude/skills/historic_sql_patterns
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SKILL.md

# Historic SQL Patterns

Use this skill when the WorkUnit raw file is a `patterns-input/part-0001.json` style shard from the `historic-sql` adapter. Older staged bundles may still provide root `patterns-input.json`; when that is the WorkUnit raw file, read it the same way.

## Required Workflow

1. Read the WorkUnit notes first.
2. Find the single pattern input file listed under the WorkUnit `rawFiles` section.
3. Call `read_raw_file` for that exact raw file path.
4. Identify recurring analytical intents that span at least two tables and have repeated usage signal.
5. Emit one `pattern` evidence object per durable cross-table intent by calling `emit_historic_sql_evidence`.
6. Stop after all pattern evidence has been emitted.

Every join column mentioned in pattern descriptions must be verified via
entity_details for both sides of the join.

## 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

Each call to `emit_historic_sql_evidence` must use this shape:

```json
{
  "kind": "pattern",
  "pattern": {
    "slug": "order-lifecycle-analysis",
    "title": "Order Lifecycle Analysis",
    "narrative": "Analysts compare order statuses with customer segments to understand lifecycle movement.",
    "definitionSql": "select o.status, count(*) from public.orders o join public.customers c on c.id = o.customer_id group by o.status",
    "tablesInvolved": ["public.orders", "public.customers"],
    "slRefs": ["orders", "customers"],
    "constituentTemplateIds": ["pg:1", "pg:2"]
  }
}
```

The `pattern` object must match `patternOutputSchema`; multiple calls together must form `patternsArraySchema`.

## Pattern Selection Rules

- Prefer patterns that involve two or more tables.
- Prefer templates with `executionsBucket` at least `10-100` and `distinctUsersBucket` above solo usage.
- Merge templates into one pattern only when the business intent is the same.
- Use a stable kebab-case slug based on intent, not a template id.
- Set `definitionSql` to the clearest representative SQL from a constituent template.
- Set `slRefs` to source names when the source name is obvious from table names; omit uncertain refs rather than guessing.
- Treat each pattern shard independently; do not read peer shard files from `peerFileIndex`.

## 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 create single-table pattern pages.
- Do not copy credentials, tokens, user emails, or unredacted literals into evidence.
ktx-analyticsSkill

Use when answering a question that needs data from a ktx-connected database - investigating, analyzing, "how many", "show me", "what's the breakdown of", finding records by value, exploring tables, comparing periods, explaining metrics, or any data-analysis request. Triggers even when the user does not say "analytics"; if the answer requires querying a configured ktx connection, this skill applies.

dbt_ingestSkill

Map dbt `schema.yml` / `properties.yml` models and sources into ktx semantic-layer overlays and column notes. Covers `sources:` vs `models:`, column `data_tests` (not_null, unique, accepted_values, relationships), and how bundle-time writes complement manifest backfill from git sync. Load when the WorkUnit's `skillNames` includes `dbt_ingest` or when raw files are dbt YAML under `models/` / `sources/`.

historic_sql_table_digestSkill

Convert one changed historic-SQL table usage bucket into typed table usage evidence for deterministic _schema projection.

ingest_triageSkill

Classify and resolve conflicts detected during bundle ingest (structural duplicates, definitional contradictions, near-duplicate clusters, re-ingest changes, evictions).

live_database_ingestSkill

Capture semantic-layer and knowledge updates from a live database schema snapshot.

looker_ingestSkill

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/.

lookml_ingestSkill

Map a LookML view/model/explore into ktx semantic layer sources. Covers the LookML to ktx primitive table, provenance tagging, and three worked examples (overlay, standalone from derived_table, standalone with sql_always_where). Load when the turn contains `.lkml` content.

metabase_ingestSkill

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.