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/sql-to-business-logic

@fcb0454
by Nimrod Fishernimrodfisher/data-analytics-skills456 stars
82

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.

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Use this Skill: https://skilld.dev/gh/nimrodfisher/data-analytics-skills/sql-to-business-logic

This session only. Nothing lands on disk.

SKILL.md

≈56 tokens always: the name and description. ≈578 when used: this file. ≈1.4k more on demand in 2 files.

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.
  5. Summarise output columns — for each output column, state its business meaning and any edge cases (nulls, rounding, currency units).
  6. Flag issues and write validation questions — identify potential problems (implicit null propagation, unexpected fan-out, hardcoded dates). Generate 3–5 questions the query author should confirm. Use assets/query_documentation_template.md to record the full translation.

Inputs the skill needs

  • The complete SQL query (SELECT through ORDER BY)
  • The business question the query is intended to answer
  • Table and column descriptions (or a data catalog entry)
  • Any business rules for key status values, date handling, or currency
  • The intended output: who reads the result and for what decision

Output

  • scripts/sql_explainer.py — parses a SQL query and generates a structured plain-language explanation
  • assets/query_documentation_template.md — completed translation covering purpose, step-by-step logic, output columns, business rules, and validation questions
  • Optionally: a flowchart representation of the query logic

Source: SKILL.md on GitHub

Third-party checks

No alerts16d4 checks · Risk SAFE
  • Gen Agent Trust Hub16d

    The skill is safe to use. It consists of markdown documentation templates and a Python script designed to parse and explain SQL queries using static regular expressions. No suspicious behaviors, external dependencies, or security risks were identified.

  • Socket16d

    No alerts

  • Snyk16d

    Risk: LOW · No issues

  • ZeroLeaks5mo

    Score: 93/100 · 2 sections analyzed

Provenance

Signed by skilld at fcb0454. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 5 days ago.

Activeupdated 5 months ago

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