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Sql analytics

Skill yigityildiz0/universal-ai-skill-library/skills/common/sql-analytics

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Install
npx -y skills add yigityildiz0/universal-ai-skill-library --skill sql-analytics

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

3 things to look at

  • 19 days oldThe repository was created 19 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
  • 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Design, review, debug, or explain analytical SQL with correct grain, joins, filters, time logic, performance awareness, and validation. Use for SQL analysis, write a query, debug a query, metric SQL, warehouse query, cohort query, or database reporting.

SKILL.md

1.0 KB, 141 tokens by cl100k_base, as published. Nobody here has run it

SQL Analytics

Make the question and row grain explicit before writing SQL.

  1. Confirm dialect, tables, field meaning, row grain, time zone, and access boundaries.
  2. Write a plain-language query contract: population, filters, joins, grouping, metric, and expected row count.
  3. Build incrementally: inspect a small sample, validate joins, check duplicated entities, then aggregate.
  4. Parameterize dates and document assumptions. Prefer safe read-only queries unless writes are explicitly authorized.
  5. Validate totals against an independent small check and report limitations.

Do not execute destructive SQL, expose secrets, or assume dialect-specific functions. Flag PII and large-scan cost risks before execution.

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