Sql analytics
Skill yigityildiz0/universal-ai-skill-library/skills/common/sql-analytics
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.From its SKILL.md
npx -y skills add yigityildiz0/universal-ai-skill-library --skill sql-analyticsAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- 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.
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.
- Confirm dialect, tables, field meaning, row grain, time zone, and access boundaries.
- Write a plain-language query contract: population, filters, joins, grouping, metric, and expected row count.
- Build incrementally: inspect a small sample, validate joins, check duplicated entities, then aggregate.
- Parameterize dates and document assumptions. Prefer safe read-only queries unless writes are explicitly authorized.
- 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.
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.