Agent Skills › Zafer-Liu/Data-Analysis-Agent › winsorize

winsorize

GitHub

该技能用于对数值字段执行缩尾处理以消除极端值影响。通过识别分布、设定边界并验证处理前后指标变化，确保数据清洗过程透明且可追溯，不覆盖原始数据。

skills/winsorize/SKILL.md Zafer-Liu/Data-Analysis-Agent

Trigger Scenarios

需要对数值数据进行异常值或极端值处理需要验证数据清洗前后的分布和指标变化

Install

npx skills add Zafer-Liu/Data-Analysis-Agent --skill winsorize -g -y

More Options

Use without installing

npx skills use Zafer-Liu/Data-Analysis-Agent@winsorize

指定 Agent (Claude Code)

npx skills add Zafer-Liu/Data-Analysis-Agent --skill winsorize -a claude-code -g -y

安装 repo 全部 skill

npx skills add Zafer-Liu/Data-Analysis-Agent --all -g -y

预览 repo 内 skill

npx skills add Zafer-Liu/Data-Analysis-Agent --list

SKILL.md

Frontmatter

{
    "icon": "✂️",
    "name": "winsorize",
    "description": "对极端值执行缩尾处理并验证影响（outlier 缩尾）",
    "allowedTools": [
        "get_schema",
        "profile_data",
        "clean_data",
        "query_data"
    ]
}

缩尾处理

识别目标数值字段和异常分布，说明上下界规则后再缩尾。不得默认覆盖原始数据；比较处理前后的分布、均值和关键指标，并记录边界与受影响行数。

Tool routing

Use get_schema to identify the target table and candidate numeric columns.
Use profile_data to quantify distribution tails before modification.
Use clean_data with the winsorize operation only after the percentile bounds are clear.
Use query_data after cleaning to verify clipped values, row counts, and key metric changes.

Implementation reference

Tool entries: agent/tools/business/data.py::_tool_profile_data, agent/tools/business/data.py::_tool_clean_data
Profiling implementation: Function/Clean/data_profile.py
Winsorization implementation: Function/Clean/winsorize.py