缺失值处理

SkillAI & models

Lets your agent detect and fix missing values in a dataset before analysis.

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 缺失值处理 skill

About this capability

Diagnose and handle missing values (missing value handling)

What this skill tells your AI

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

先量化字段和行级缺失，判断缺失机制及业务含义，再选择删除、常数、统计量或分组插补。修改前说明影响，保留可追溯结果，并在处理后验证缺失率和分布变化。

Use get_schema to identify tables, nullable fields, and candidate columns.
Use profile_data to quantify missingness before any modification.
Use clean_data with the appropriate missing-value operation only when the user intent is clear.
Use query_data after cleaning to verify row counts, remaining nulls, and distribution 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
Missing-value implementation: Function/Clean/missing_handler.py

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