schema-check

name	schema-check
description	Validate expected columns and coercible dtypes in water quality datasets. Use when checking schema conformance, column presence, or type compatibility before analysis.
user-invocable	true
argument-hint	Path to data file or DataFrame variable name

Schema Check

Validate that a water quality dataset conforms to the expected schema.

When to Use

• Before any analysis pipeline runs
• When a schema mismatch error is reported
• When integrating a new data source

Procedure

1. Load the dataset (CSV, parquet, or DataFrame)
2. Compare columns against the expected schema:
   • Required: site_id, timestamp, purity_score, contamination_flag, technician_id
   • Optional: temperature, ph, turbidity, notes
3. Check dtype coercibility (e.g., purity_score must be numeric, timestamp must be datetime-parseable)
4. Report any missing columns, extra columns, and type mismatches
5. Suggest corrective actions for each issue

Output

• ✅ Pass / ❌ Fail per column
• Summary of dtype issues with suggested casts
• List of unexpected columns