leo-claude-mktplace/plugins/data-platform/commands/data-quality.md

/data-quality - Data Quality Assessment

Visual Output

When executing this command, display the plugin header:

┌──────────────────────────────────────────────────────────────────┐
│  📊 DATA-PLATFORM · Data Quality                                  │
└──────────────────────────────────────────────────────────────────┘

Then proceed with the assessment.

Comprehensive data quality check for DataFrames with pass/warn/fail scoring.

Usage

/data-quality <data_ref> [--strict]

Workflow

1. Get data reference:

   • If no data_ref provided, use list_data to show available options
   • Validate the data_ref exists

2. Null analysis:

   • Calculate null percentage per column
   • PASS: < 5% nulls
   • WARN: 5-20% nulls
   • FAIL: > 20% nulls

3. Duplicate detection:

   • Check for fully duplicated rows
   • PASS: 0% duplicates
   • WARN: < 1% duplicates
   • FAIL: >= 1% duplicates

4. Type consistency:

   • Identify mixed-type columns (object columns with mixed content)
   • Flag columns that could be numeric but contain strings
   • PASS: All columns have consistent types
   • FAIL: Mixed types detected

5. Outlier detection (numeric columns):

   • Use IQR method (values beyond 1.5 * IQR)
   • Report percentage of outliers per column
   • PASS: < 1% outliers
   • WARN: 1-5% outliers
   • FAIL: > 5% outliers

6. Generate quality report:

   • Overall quality score (0-100)
   • Per-column breakdown
   • Recommendations for remediation

Report Format

=== Data Quality Report ===
Dataset: sales_data
Rows: 10,000 | Columns: 15
Overall Score: 82/100 [PASS]

--- Column Analysis ---
| Column       | Nulls | Dups | Type     | Outliers | Status |
|--------------|-------|------|----------|----------|--------|
| customer_id  | 0.0%  | -    | int64    | 0.2%     | PASS   |
| email        | 2.3%  | -    | object   | -        | PASS   |
| amount       | 15.2% | -    | float64  | 3.1%     | WARN   |
| created_at   | 0.0%  | -    | datetime | -        | PASS   |

--- Issues Found ---
[WARN] Column 'amount': 15.2% null values (threshold: 5%)
[WARN] Column 'amount': 3.1% outliers detected
[FAIL] 1.2% duplicate rows detected (12 rows)

--- Recommendations ---
1. Investigate null values in 'amount' column
2. Review outliers in 'amount' - may be data entry errors
3. Remove or deduplicate 12 duplicate rows

Options

Flag	Description
--strict	Use stricter thresholds (WARN at 1% nulls, FAIL at 5%)

Examples

/data-quality sales_data
/data-quality df_customers --strict

Scoring

Component	Weight	Scoring
Nulls	30%	100 - (avg_null_pct * 2)
Duplicates	20%	100 - (dup_pct * 50)
Type consistency	25%	100 if clean, 0 if mixed
Outliers	25%	100 - (avg_outlier_pct * 10)

Final score: Weighted average, capped at 0-100

Available Tools

Use these MCP tools:

• describe - Get statistical summary (for outlier detection)
• head - Preview data
• list_data - List available DataFrames