截尾处理

SkillAI & models

Lets your agent trim outlier samples in a dataset and measure how much the results shift.

Available today. Use it from your connected AI after setup.

No other account needed.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the 截尾处理 skill

About this capability

Performs trimming on outlier samples and evaluates bias (outlier)

What this skill tells your AI

The instructions your AI receives, as published by zafer-liu/data-analysis-agent in skills/trimming/SKILL.md and read by ahel’s review.

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。

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.

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

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