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.
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scripts/sql_lint.py (sqlglot-based) to catch syntax errors, unsupported functions for the target engine, and style violations. Fix hard errors before continuing.references/sql_anti_patterns.md. Flag any present anti-patterns with a severity rating.scripts/explain_plan_parser.py to extract slow steps (full table scans, missing indexes, high row estimates).scripts/cardinality_estimator.py if schema stats are available to flag joins that might fan-out unexpectedly.references/engine_specific_guide.md for the target engine (Snowflake / BigQuery / Postgres / Redshift) to verify date functions, window behaviour, and clustering assumptions.assets/query_review_template.md with findings; for any performance issues found, complete assets/optimization_recommendations.md.assets/query_review_template.md (filled) — categorised findings: correctness, performance, styleassets/optimization_recommendations.md (filled, if issues found) — ranked rewrite suggestions with expected impactNo reviews yet. Tell others what you thought.
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Rigorous A/B test statistical analysis. Use when analyzing experiment results, calculating statistical significance, checking for sample ratio mismatch, or validating test design before launch.
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Track and document analytical assumptions and decisions. Use when making analytical choices, documenting trade-offs, ensuring transparency, or creating audit trails for analytical work.
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Structured, reproducible analysis documentation. Use when documenting analysis findings, creating analysis notebooks, ensuring reproducibility, or building analysis archives for future reference.
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Structure analysis approach before starting work. Use when receiving new analysis requests, breaking down complex questions into steps, or planning iterative analysis workflows.
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Pre-delivery quality assurance for analysis work. Use when reviewing analysis before sharing with stakeholders, checking for completeness, validating assumptions, or ensuring clarity of recommendations.
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Post-analysis learning and process improvement. Use when completing major analysis projects, documenting lessons learned, or improving team analytical practices.
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