data-auditor-cleaner

name	data-auditor-cleaner
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.

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.

6. Assess readiness per Qx.

   • ready, ready_with_warnings, or blocked.
   • Name missing fields and risks precisely.
   • Hand the profile to method-selector for method-specific risk probes.

Canonical Outputs

workspace/data/data_report.md
workspace/data/data_profile.json
workspace/data_clean/<cleaned files>
workspace/code/scripts/<cleaning script>   # only when needed

Accept legacy workspace/data/data_clean/ as an input/output location during migration.

Data Profile Contract

data_profile.json contains:

{
  "schema_version": 1,
  "raw_files": [],
  "attachment_mapping": [],
  "fields": [],
  "quality": {
    "missingness": {},
    "duplicates": {},
    "impossible_values": {},
    "outliers": {}
  },
  "coverage": {
    "rows": 0,
    "effective_sample_size": null,
    "time_range": 
     
  
   
     
     
     
     
     
  
   
   
   

Use null with an explanation when a field is not applicable; do not invent a value to fill the schema.

Rules

• Do not select the model.
• Do not overwrite raw data.
• Do not silently delete, impute, winsorize, rescale, or recode.
• Do not produce decorative EDA.
• Reuse one profile downstream instead of regenerating statistics.
• Store detailed row-level change logs only when changes occurred; successful no-op checks need only summary counts.

Verification

• Attachment mapping is unambiguous or human-confirmed.
• Raw files remain untouched.
• Cleaned files trace to raw sources and transformation rules.
• Profile includes effective sample size, imbalance/cardinality, and concentration evidence when applicable.
• Readiness is reported per subquestion.
• Downstream handoff points to paths rather than pasting the full report.