菜单
Validate expected columns and coercible dtypes in water quality datasets. Use when checking schema conformance, column presence, or type compatibility before analysis.
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
Flag water quality records that exceed contamination thresholds. Use when checking purity_score < 75, contamination_flag = 1, or identifying at-risk sites and records.
Standardize water purity, alert-rate, and site-risk metrics. Use when defining KPI calculations, ensuring metric consistency across reports, or setting up a new KPI pipeline.
Report null percentages and recommended imputations for water quality data. Use when profiling missing data, deciding imputation strategy, or assessing data completeness.
Convert water quality metrics into concise operations guidance. Use when writing executive summaries, translating KPIs into plain-language recommendations, or producing briefing narratives.
| name | schema-check |
| description | Validate expected columns and coercible dtypes in water quality datasets. Use when checking schema conformance, column presence, or type compatibility before analysis. |
| user-invocable | true |
| argument-hint | Path to data file or DataFrame variable name |
Validate that a water quality dataset conforms to the expected schema.
site_id, timestamp, purity_score, contamination_flag, technician_idtemperature, ph, turbidity, notespurity_score must be numeric, timestamp must be datetime-parseable)