菜单
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.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Create standardized metadata for data assets. Use when documenting new datasets, building data catalogs, improving data discoverability, or creating data dictionaries for teams.
Structure analysis approach before starting work. Use when receiving new analysis requests, breaking down complex questions into steps, or planning iterative analysis workflows.
Comprehensive data quality assessment against business rules, schema constraints, and freshness expectations. Activate when validating production pipelines, onboarding new data sources, or when stakeholders report data quality concerns.
Identify distinct customer or user segments based on behavior, attributes, or value. Activate when you need to answer "who are our best customers?" or "what distinct groups exist in our user base?" and need data-driven profiles to inform strategy.
Create effective, publication-ready data visualizations. Use when choosing chart types, designing presentation visuals, building dashboard charts, or applying visual design best practices to data output.
Create a clear, transparent explanation of analytical methodology for any audience level. Activate when you deliver findings that require the audience to trust the method — A/B tests, attribution models, forecasts, statistical analyses, or anything where "how did you get that?" is a likely question.
| 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. |
scripts/sql_lint.py (uses sqlglot) to catch syntax errors, unsupported functions for the target DB, and style violationsreferences/sql_anti_patterns.md; rate each finding by severity (blocking / major / minor)scripts/explain_plan_parser.py; identify full table scans, missing indexes, and sort spillsscripts/cardinality_estimator.py when schema statistics exist; flag joins with unexpected row multiplicationreferences/engine_specific_guide.md for Snowflake, BigQuery, Postgres, or Redshift-specific behaviour (date functions, window frame defaults, QUALIFY support)assets/query_review_template.md with categorised findings; generate assets/optimization_recommendations.md for performance issuesassets/query_review_template.md (filled) — correctness, performance, style findings categorised by severityassets/optimization_recommendations.md (filled) — prioritised optimisation suggestions with projected impact