Individual skill

sql-to-business-logic

Translate SQL queries into plain language business logic. Use when documenting queries, explaining analysis to non-technical stakeholders, code reviewing for correctness, or building a query catalog.

02-documentation-knowledge/sql-to-business-logic/SKILL.md

Read pinned source

Source commit 9449d363e1ae43cf1706c73bebc16774e339c427

What was checked

Matches the observed repository HEAD: Yes
Scanner rules are current: No
Repository scan complete: No
Skill scan complete: Yes
Observed behavior: Not checked yet
Last checked: Nov 6, 2026

These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.

Install this skill

Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.

An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.

Repository context

Repository stars (not skill ratings): 521
Repository last updated: Nov 8, 2026
License: MIT

Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.

claude-code Repository topic
codex Repository topic
data-analytics Repository topic
skills Repository topic
data Derived category
data-databases Derived category

Files and excerpt

File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.

Browse files

SKILL.mdcore · 2,561 bytes
assets/query_documentation_template.mddoc · 1,976 bytes
references/sql_pattern_library.mddoc · 3,616 bytes
scripts/sql_explainer.pyruns · 6,372 bytes

Read excerpt


# SQL to Business Logic Translator

# When to use
- A stakeholder asks "what exactly does this query calculate?"
- Documenting a query library or a dbt model for non-technical readers
- Reviewing a query for correctness by comparing its logic to the business requirement
- Onboarding new analysts to existing SQL patterns
- Translating legacy undocumented queries before refactoring

# Process
1. **Receive the query and context** — obtain the SQL and the business question it answers. Also collect any schema notes (what the key tables and columns represent in business terms).
2. **Translate the FROM/JOIN structure** — describe in plain language which data sources are combined and what type of join is used (inner keeps only matches; left keeps all rows from the left side). Note if the join type seems inconsistent with the stated purpose.
3. **Translate WHERE filters** — list each filter condition as a business rule in plain language (e.g., `status = 'completed'` → "only includes orders that have been paid and fulfilled").
4. **Explain GROUP BY and aggregations** — describe what each aggregation computes and at what grain. Use `scripts/sql_explainer.py` to automate a first-pass structural parse.