AugmentClaude

Historic SQL Patterns

Identify recurring SQL query patterns across database tables for analysis.

Installation

  1. Make sure Claude is on your device and in your terminal.

    Skills load from ~/.claude/skills/ when Claude Code starts up — so you need it on your machine first. If you don't have it yet, install it once with the command below, then run claude in any terminal to verify.

    One-time setup
    npm i -g @anthropic-ai/claude-code

    Already have it? Skip ahead.

  2. Paste into Claude Code or into your terminal.

    This copies the whole skill folder into ~/.claude/skills/historic-sql-patterns-kaelio/ — the SKILL.md plus any scripts, reference docs, or templates the skill ships with. Safe default: works for every skill.

    Faster alternative (instruction-only skills)

    Skips the clone and grabs only the SKILL.md file. Don't use this if the skill ships Python scripts, reference markdowns, or asset templates — they won't be downloaded and the skill will fail when it tries to load them.

    Quick install (SKILL.md only)
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  3. Restart Claude Code.

    Quit and reopen Claude Code (or any other agent that loads from ~/.claude/skills/). New skills are picked up on startup.

  4. Just ask Claude.

    Skills auto-activate when your request matches the skill's description — no slash command needed. Trigger phrases live in the skill's own frontmatter; you can read them in the “What this skill does” section above.

Prefer to read the source first? Open on GitHub.

When Claude uses it

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

What this skill does

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:

  1. entity_details({connectionId, targets: [{display: "<identifier>"}]}) - confirm the identifier resolves; inspect native types, FK/PK, and sampleValues.
  2. 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"}).
  3. 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.
  4. 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.

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