Historic SQL Patterns
Identify recurring SQL query patterns across database tables for analysis.
Installation
- 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 runclaudein any terminal to verify.One-time setupnpm i -g @anthropic-ai/claude-codeAlready have it? Skip ahead.
- 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)Sign up to copy - Restart Claude Code.
Quit and reopen Claude Code (or any other agent that loads from
~/.claude/skills/). New skills are picked up on startup. - 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
- Read the WorkUnit notes first.
- Find the single pattern input file listed under the WorkUnit
rawFilessection. - Call
read_raw_filefor that exact raw file path. - Identify recurring analytical intents that span at least two tables and have repeated usage signal.
- Emit one
patternevidence object per durable cross-table intent by callingemit_historic_sql_evidence. - 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:
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:
entity_details({connectionId, targets: [{display: "<identifier>"}]})- confirm the identifier resolves; inspect native types, FK/PK, and sampleValues.- For literal values from the source, such as status codes or plan tiers,
check whether they appear in
entity_detailssampleValues for the relevant column. If sampleValues is short or the sample may have missed real values, run asql_executionprobe with the same warehouse connection id:sql_execution({connectionId, sql: "SELECT DISTINCT <col> FROM <ref> LIMIT 50"}). - 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_fallbackwithno_physical_table, include the failing probe error inclarification.
- Use
- 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
executionsBucketat least10-100anddistinctUsersBucketabove 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
definitionSqlto the clearest representative SQL from a constituent template. - Set
slRefsto 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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