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/historic_sql_patterns

Identify recurring cross-table historic-SQL analytical intents from a bounded pattern shard and emit typed pattern evidence for deterministic wiki projection.

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ktx
1.6k17 skills
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$ npx -y skills add Kaelio/ktx --skill historic_sql_patterns --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/historic_sql_patterns

Context 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.

SKILL.md

historic_sql_patterns.SKILL.md
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]

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:

{
  "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.
Read more
Ships withktx

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

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Repo: Kaelio/ktx

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