菜单
Pre-delivery quality gate for analytical work. Activate before sharing any analysis, dashboard, or query result with stakeholders. Catches logic errors, presentation issues, and assumption gaps before they reach the audience.
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 | analysis-qa-checklist |
| description | Pre-delivery quality gate for analytical work. Activate before sharing any analysis, dashboard, or query result with stakeholders. Catches logic errors, presentation issues, and assumption gaps before they reach the audience. |
scripts/qa_runner.py on the output file; flags numeric anomalies (unexpected nulls, out-of-range values, division results), structural issues, and formatting problemsreferences/qa_checklist_master.md covering: question framing, data sourcing, transformations, statistical validity, finding statements, and presentationreferences/common_analysis_errors.md for analysis-type-specific mistakes (e.g. survivorship bias in cohort analysis, multiple comparison issues in A/B tests)assets/qa_signoff_template.md documenting the reviewer, issues found, resolutions, and delivery decisionassets/qa_signoff_template.md (filled) — formal sign-off confirming the output is ready for delivery