# Data Auditor Cleaner

Map contest attachments to subquestions, audit and clean raw data, and emit one...

- Repository: https://github.com/zhnnky329/MathModeling-skills (1073 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-09-24
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add zhnnky329/MathModeling-skills --skill data-auditor-cleaner -g -a claude-code -y`
Codex: `npx skills add zhnnky329/MathModeling-skills --skill data-auditor-cleaner -g -a codex -y`
Cursor: `npx skills add zhnnky329/MathModeling-skills --skill data-auditor-cleaner -g -a cursor -y`
Gemini CLI: `npx skills add zhnnky329/MathModeling-skills --skill data-auditor-cleaner -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add zhnnky329/MathModeling-skills`

## Description

Map contest attachments to subquestions, audit and clean raw data, and emit one reusable data profile with quality, coverage, imbalance, concentration, and method-readiness evidence for downstream risk screening.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Purpose

Create traceable cleaned data and one reusable profile. Do not repeat the same data inspection separately for every candidate method.

# Preconditions

- Problem parse and subquestion IDs exist.
- Raw files are available under `workspace/data_raw/` or the workspace's documented legacy raw-data path.
- Required outputs and known field needs are available.

Stop rather than fabricate a missing attachment, unit, field meaning, or label.

# Workflow

1. **Map attachments before cleaning.**
   - List each attachment with name, size, sheet names, headers, and a small preview.
   - Map it to Qx or mark it shared.
   - Ask the user only when two mappings remain materially plausible.

2. **Preserve raw data.**
   - Treat raw files as read-only.
   - Record hashes or stable file metadata when practical.
   - Write cleaned copies under `workspace/data_clean/`.

3. **Audit structure and semantics.**
   - Rows, columns, keys, types, units, categories, time granularity, and encoding.
   - Missing values, duplicates, impossible values, outliers, discontinuities, and leakage risks.
   - Field-to-subquestion and field-to-required-output mapping.

4. **Compute reusable risk-profile statistics.**
   - Effective sample size and rows usable per Qx.
   - Missingness by field and row.
   - Numeric distribution summaries and extreme-value rates.
   - Category/class counts, imbalance ratios, rare levels, and cardinality.
   - Time coverage, gaps, sampling interval, and chronological split constraints.
   - Correlation/redundancy warnings where relevant.
   - Target or score concentration indicators when a target exists.
   - Record facts; do not convert them into a final method verdict.

5. **Plan and apply cleaning.**
   - Separate safe normalization of representation from assumption-bearing imputations or removals.
   - Explain and record every assumption-bearing operation.
   - Keep reproducible cleaning code only when transformations are nontrivial.

The rest: https://github.com/zhnnky329/MathModeling-skills/blob/HEAD/.claude/skills/data-auditor-cleaner/SKILL.md