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query-validation

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Mis à jour14 juin 2026 à 05:06

Review SQL queries for correctness, performance, and best practices before they reach production. Activate when promoting queries to dashboards, investigating unexpected results, or when a query is slow and needs optimisation.

Installation

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Source

ryudkiss-hue

ryudkiss-hue/nyc_data

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Métiers associésSOC

Basé sur la classification professionnelle SOC

Développeurs de logicielsProfessions informatiques et mathématiques·SOC 15-1252

Explorateur de fichiers

8 fichiers

SKILL.md

readonly

name	query-validation
description	Review SQL queries for correctness, performance, and best practices before they reach production. Activate when promoting queries to dashboards, investigating unexpected results, or when a query is slow and needs optimisation.

When to use

Promoting a query to a production dashboard or scheduled report
Results look wrong or unexpected — validating logic before escalating
A query is slow and needs performance investigation
Preventing anti-patterns from causing production incidents
Code review for an analyst's or engineer's SQL

Process

Syntax & style check — run scripts/sql_lint.py (uses sqlglot) to catch syntax errors, unsupported functions for the target DB, and style violations
Anti-pattern detection — compare against references/sql_anti_patterns.md; rate each finding by severity (blocking / major / minor)
Execution plan analysis — parse EXPLAIN or query profile output via scripts/explain_plan_parser.py; identify full table scans, missing indexes, and sort spills
Cardinality estimation — run scripts/cardinality_estimator.py when schema statistics exist; flag joins with unexpected row multiplication
Engine-specific validation — cross-reference references/engine_specific_guide.md for Snowflake, BigQuery, Postgres, or Redshift-specific behaviour (date functions, window frame defaults, QUALIFY support)
Produce deliverables — populate assets/query_review_template.md with categorised findings; generate assets/optimization_recommendations.md for performance issues

Inputs the skill needs

Required: SQL text
Required: target database engine (Snowflake / BigQuery / Postgres / Redshift / other)
Should provide: table schemas and approximate row counts
Should provide: EXPLAIN or query profile output if the query has already run
Optional: business logic the query is meant to implement
Optional: performance target (e.g. must run under 30 s on prod)

Output

assets/query_review_template.md (filled) — correctness, performance, style findings categorised by severity
assets/optimization_recommendations.md (filled) — prioritised optimisation suggestions with projected impact

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name	query-validation
description	Review SQL queries for correctness, performance, and best practices before they reach production. Activate when promoting queries to dashboards, investigating unexpected results, or when a query is slow and needs optimisation.

When to use

Promoting a query to a production dashboard or scheduled report
Results look wrong or unexpected — validating logic before escalating
A query is slow and needs performance investigation
Preventing anti-patterns from causing production incidents
Code review for an analyst's or engineer's SQL

Process

Syntax & style check — run scripts/sql_lint.py (uses sqlglot) to catch syntax errors, unsupported functions for the target DB, and style violations
Anti-pattern detection — compare against references/sql_anti_patterns.md; rate each finding by severity (blocking / major / minor)
Execution plan analysis — parse EXPLAIN or query profile output via scripts/explain_plan_parser.py; identify full table scans, missing indexes, and sort spills
Cardinality estimation — run scripts/cardinality_estimator.py when schema statistics exist; flag joins with unexpected row multiplication
Engine-specific validation — cross-reference references/engine_specific_guide.md for Snowflake, BigQuery, Postgres, or Redshift-specific behaviour (date functions, window frame defaults, QUALIFY support)
Produce deliverables — populate assets/query_review_template.md with categorised findings; generate assets/optimization_recommendations.md for performance issues

Inputs the skill needs

Required: SQL text
Required: target database engine (Snowflake / BigQuery / Postgres / Redshift / other)
Should provide: table schemas and approximate row counts
Should provide: EXPLAIN or query profile output if the query has already run
Optional: business logic the query is meant to implement
Optional: performance target (e.g. must run under 30 s on prod)

Output

assets/query_review_template.md (filled) — correctness, performance, style findings categorised by severity
assets/optimization_recommendations.md (filled) — prioritised optimisation suggestions with projected impact