Agent Skills
› aiming-lab/MetaClaw
› data-validation-first
data-validation-first
GitHub强调在进行数据分析、转换或建模前，必须先检查数据形状、类型、缺失值及重复项，避免基于未验证数据得出结论。
Trigger Scenarios
准备进行数据分析
编写数据转换代码
构建数据模型
Install
npx skills add aiming-lab/MetaClaw --skill data-validation-first -g -y
SKILL.md
Frontmatter
{
"name": "data-validation-first",
"category": "data_analysis",
"description": "Use this skill before any data analysis, transformation, or modeling. Always inspect and validate the data before drawing conclusions or writing transformations."
}
Data Validation First
Before writing any analysis code, understand the data:
# Always run these first
df.shape # rows x columns
df.dtypes # column types
df.isnull().sum() # missing values per column
df.describe() # statistics for numeric columns
df.head() # sample rows
Key questions:
- Are there nulls in columns you'll join or filter on?
- Are numeric columns stored as strings? (parse_dates, astype)
- Are there unexpected duplicates (check primary key uniqueness)?
- Does the row count match your expectation from the source?
Anti-pattern: Running .groupby().sum() without first checking for nulls in the groupby key.
Version History
- 922caf3 Current 2026-07-25 11:08
