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

trimming

GitHub

该技能用于对数据异常值执行截尾处理。通过定义判据和范围，量化并删除异常样本，仅在用户确认规则后执行，确保保留原始数据、可追溯输出，并报告样本损失及潜在选择偏差。

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

Trigger Scenarios

需要处理数据中的异常值或离群点用户明确要求对特定列进行截尾清洗

Install

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

More Options

Use without installing

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

指定 Agent (Claude Code)

npx skills add Zafer-Liu/Data-Analysis-Agent --skill trimming -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": "trimming",
    "description": "对异常样本执行截尾处理并评估偏差（outlier 异常值）",
    "allowedTools": [
        "get_schema",
        "profile_data",
        "clean_data",
        "query_data"
    ]
}

截尾处理

先定义异常判据和业务合理范围，量化拟删除样本及其特征。仅在用户意图明确时执行，保留原始数据和可追溯输出；处理后报告样本损失及潜在选择偏差。

Tool routing

Use get_schema to identify the target table and candidate numeric columns.
Use profile_data to quantify outliers and candidate trim boundaries before modification.
Use clean_data with the trimming operation only when the user has confirmed the rule or bounds.
Use query_data after cleaning to verify row loss, boundary effects, 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
Trimming implementation: Function/Clean/trimming.py