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GitHub

基于决策树识别影响目标变量的关键规则。通过获取Schema和查询数据确认字段、分布及泄漏风险，执行训练验证后输出性能指标、特征重要性及可解释规则，严禁将相关性表述为因果。

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

Trigger Scenarios

用户要求使用决策树分析数据需要识别影响目标变量的关键规则或特征重要性需要进行分类或回归任务的可解释性分析

Install

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

More Options

Use without installing

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

指定 Agent (Claude Code)

npx skills add Zafer-Liu/Data-Analysis-Agent --skill tree -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": "tree",
    "description": "使用决策树识别影响目标变量的关键规则（decision tree 决策树）",
    "allowedTools": [
        "get_schema",
        "query_data",
        "run_analysis",
        "generate_chart"
    ]
}

决策树分析

确认目标变量和任务类型，排除泄漏字段，合理处理缺失值与类别变量。执行训练验证并报告性能、重要特征和可解释规则；避免把相关性表述为因果。

Tool routing

Use get_schema to identify the target, candidate features, task type, and source table.
Use query_data to verify field names, target distribution, missingness, and leakage risks.
Use run_analysis with analysis_name="Decision_Tree" for the actual tree computation.
Use generate_chart on tree, feature importance, or validation result tables after run_analysis succeeds.