원클릭으로 Manus에서 모든 스킬 실행

query-validation

스타226

포크40

업데이트2026년 4월 25일 15:33

SQL query review for correctness, performance, and best practices. Activate when a query needs review before production use, shows unexpected results, or runs too slowly.

설치

Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.

Manus에서 실행

출처

nimrodfisher

nimrodfisher/data-analytics-skills

GitHub 저장소 열기 Creator 저장소 보기

다운로드

Manus에서 실행

When to use

A SQL query is about to be promoted to a production dashboard or report
A query is returning surprising or incorrect results
A query is running slowly and needs performance review
You want to catch anti-patterns (implicit conversions, SELECT *, unbounded CTEs) before they cause incidents

Process

Lint the query — run scripts/sql_lint.py (sqlglot-based) to catch syntax errors, unsupported functions for the target engine, and style violations. Fix hard errors before continuing.
Review anti-patterns — compare the query structure against references/sql_anti_patterns.md. Flag any present anti-patterns with a severity rating.
Parse the explain plan — if an EXPLAIN or query profile output is available, run scripts/explain_plan_parser.py to extract slow steps (full table scans, missing indexes, high row estimates).
Estimate cardinality — run scripts/cardinality_estimator.py if schema stats are available to flag joins that might fan-out unexpectedly.
Check engine-specific behaviour — consult references/engine_specific_guide.md for the target engine (Snowflake / BigQuery / Postgres / Redshift) to verify date functions, window behaviour, and clustering assumptions.
Produce review output — fill in assets/query_review_template.md with findings; for any performance issues found, complete assets/optimization_recommendations.md.

Inputs the skill needs

Required: the SQL query text
Required: target database engine (Snowflake / BigQuery / Postgres / Redshift / other)
Optional: relevant table schemas (column names, types, approximate row counts)
Optional: EXPLAIN / query profile output
Optional: expected business logic — what should the query calculate?

Output

assets/query_review_template.md (filled) — categorised findings: correctness, performance, style
assets/optimization_recommendations.md (filled, if issues found) — ranked rewrite suggestions with expected impact

이 저장소의 다른 Skills

같은 저장소

analysis-assumptions-log

nimrodfisher/data-analytics-skills

Track and document analytical assumptions and decisions. Use when making analytical choices, documenting trade-offs, ensuring transparency, or creating audit trails for analytical work.

2026-04-26257

analysis-documentation

nimrodfisher/data-analytics-skills

Structured, reproducible analysis documentation. Use when documenting analysis findings, creating analysis notebooks, ensuring reproducibility, or building analysis archives for future reference.

2026-04-26257

data-catalog-entry

nimrodfisher/data-analytics-skills

Create standardized metadata for data assets. Use when documenting new datasets, building data catalogs, improving data discoverability, or creating data dictionaries for teams.

2026-04-26257

metric-reconciliation

nimrodfisher/data-analytics-skills

Trace and resolve discrepancies when the same metric shows different values in two or more sources. Use before reporting, after pipeline changes, or when stakeholders question a number.

2026-04-26257

schema-mapper

nimrodfisher/data-analytics-skills

Document column-level mappings between source and target schemas. Use when integrating data from multiple systems, designing ETL transformations, or documenting how raw fields become analytical assets.

2026-04-26257

sql-to-business-logic

nimrodfisher/data-analytics-skills

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.

2026-04-26257

name	query-validation
description	SQL query review for correctness, performance, and best practices. Activate when a query needs review before production use, shows unexpected results, or runs too slowly.

When to use

A SQL query is about to be promoted to a production dashboard or report
A query is returning surprising or incorrect results
A query is running slowly and needs performance review
You want to catch anti-patterns (implicit conversions, SELECT *, unbounded CTEs) before they cause incidents

Process

Lint the query — run scripts/sql_lint.py (sqlglot-based) to catch syntax errors, unsupported functions for the target engine, and style violations. Fix hard errors before continuing.
Review anti-patterns — compare the query structure against references/sql_anti_patterns.md. Flag any present anti-patterns with a severity rating.
Parse the explain plan — if an EXPLAIN or query profile output is available, run scripts/explain_plan_parser.py to extract slow steps (full table scans, missing indexes, high row estimates).
Estimate cardinality — run scripts/cardinality_estimator.py if schema stats are available to flag joins that might fan-out unexpectedly.
Check engine-specific behaviour — consult references/engine_specific_guide.md for the target engine (Snowflake / BigQuery / Postgres / Redshift) to verify date functions, window behaviour, and clustering assumptions.
Produce review output — fill in assets/query_review_template.md with findings; for any performance issues found, complete assets/optimization_recommendations.md.

Inputs the skill needs

Required: the SQL query text
Required: target database engine (Snowflake / BigQuery / Postgres / Redshift / other)
Optional: relevant table schemas (column names, types, approximate row counts)
Optional: EXPLAIN / query profile output
Optional: expected business logic — what should the query calculate?

Output

assets/query_review_template.md (filled) — categorised findings: correctness, performance, style
assets/optimization_recommendations.md (filled, if issues found) — ranked rewrite suggestions with expected impact