analytics
Use when answering a question that needs data from a ktx-connected database - investigating, analyzing, "how many", "show me", "what's the breakdown of",…
Identify recurring cross-table historic-SQL analytical intents from a bounded pattern shard and emit typed pattern evidence for deterministic wiki projection.
$ npx -y skills add Kaelio/ktx --skill historic_sql_patterns --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/historic_sql_patternsContext preview
The summary Claude sees to decide when to auto-load this skill.
Identify recurring cross-table historic-SQL analytical intents from a bounded pattern shard and emit typed pattern evidence for deterministic wiki projection.
name: historic_sql_patterns description: Identify recurring cross-table historic-SQL analytical intents from a bounded pattern shard and emit typed pattern evidence for deterministic wiki projection. callers: [memory_agent]
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.
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.
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:
If it errors, the identifier is fictional.
citing the exact raw path that mentioned it.
the failing probe error in `clarification`. 5. Never copy `<schema>.<table>` placeholder strings from these instructions into output.
Each call to `emit_historic_sql_evidence` must use this shape:
{
"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`.
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
Repo: Kaelio/ktx
Use when answering a question that needs data from a ktx-connected database - investigating, analyzing, "how many", "show me", "what's the breakdown of",…
Map dbt `schema.yml` / `properties.yml` models and sources into ktx semantic-layer overlays and column notes. Covers `sources:` vs `models:`, column…
Synthesize durable KTX wiki pages from staged Google Drive document pulls. Load when a WorkUnit contains Google Doc raw files from `docs/**`.
Convert one changed historic-SQL table usage bucket into typed table usage evidence for deterministic _schema projection.
Classify and resolve conflicts detected during bundle ingest (structural duplicates, definitional contradictions, near-duplicate clusters, re-ingest changes,…
Capture semantic-layer and knowledge updates from a live database schema snapshot.