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data-validation-reporter Generate interactive validation reports with quality scoring, missing data analysis, and type checking. Combines Pandas validation, Plotly visualization, and YAML configuration for comprehensive data quality reporting.
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Save/publish analysis or computation results from ANY ecosystem repo to Hugging Face as a queryable, viewer-renderable dataset. Use when the user wants to "save results to hugging face", "publish dataset to HF", "hugging face data saving", "save analysis results", "hf dataset", "make results queryable", or "render via datasets-server API". Reshapes nested results into flat parquet tables, writes a dataset card with a viewer `configs:` block and provenance, applies license/public-vs-private routing, enforces a domain data-quality gate (faithful-to-source != correct), publishes to `aceengineer/<repo>-<projection>`, and verifies via the datasets-server API.
config_template.yaml 2.1 KB validator_template.py 12.3 KB name data-validation-reporter description Generate interactive validation reports with quality scoring, missing data analysis, and type checking. Combines Pandas validation, Plotly visualization, and YAML configuration for comprehensive data quality reporting. version 1.0.0 category workspace-hub type skill tags ["data-validation","plotly","reporting","quality-assurance","pandas"] discovered "2026-01-07T00:00:00.000Z" source_commit 47b64945 reusability_score 80
Data Validation Reporter Skill
Overview
This skill provides a complete data validation and reporting workflow:
Data validation with configurable quality rulesInteractive Plotly reports with 4-panel dashboardsYAML configuration for validation parametersQuality scoring (0-100 scale)Missing data analysis with visualizationsType checking with automated detection
Pattern Analysis
Discovered from commit : 47b64945 (digitalmodel)
Original file : src/data_procurement/validators/data_validator.py
Reusability score : 80/100
Patterns used :
plotly_viz (interactive dashboards)
pandas_processing (DataFrame validation)
data_validation (quality scoring)
yaml_config (configuration loading)
logging (structured logging)
Core Capabilities
1. Data Validation
validator = DataValidator(config_path="config/validation.yaml" )
results = validator.validate_dataframe(
df=data,
required_fields=["id" , "value" , "timestamp" ],
unique_field="id"
)
Validation checks :
Empty DataFrame detection
Required field verification
Missing data analysis (per-column percentages)
Duplicate detection
Data type validation
Numeric field validation
2. Quality Scoring Algorithm
Score calculation (0-100 scale):
Base score: 100
Missing required fields: -20
High missing data (>50%): -30
Moderate missing data (>20%): -15
Duplicate records: -2 per duplicate (max -20)
Type issues: -5 per issue (max -15)
Status thresholds :
✅ PASS: score ≥ 60
❌ FAIL: score < 60
3. Interactive Reporting
4-Panel Plotly Dashboard :
Quality Score Gauge
Missing Data Chart - Bar chart showing missing % per column
Type Issues Chart - Bar chart of validation errors
Summary Table - Key metrics overview
Responsive design
Interactive hover tooltips
Zoom and pan controls
Export to PNG/SVG
CDN-based Plotly (no local dependencies)
4. YAML Configuration
validation:
required_fields:
- id
- timestamp
- value
unique_fields:
- id
numeric_fields:
- year_built
- length_m
- displacement_tonnes
thresholds:
max_missing_pct: 0.2
min_quality_score: 60
max_duplicates: 0
Usage
Basic Validation from data_validator import DataValidator
import pandas as pd
validator = DataValidator(config_path="config/validation.yaml" )
df = pd.read_csv("data/input.csv" )
results = validator.validate_dataframe(
df=df,
required_fields=["id" , "name" , "value" ],
unique_field="id"
)
if results['valid' ]:
print (f"✅ PASS - Quality Score: {results['quality_score' ]:.1 f} /100" )
else :
print (f"❌ FAIL - Issues: {len (results['issues' ])} " )
for issue in results['issues' ]:
print (f" - {issue} " )
Generate Interactive Report from pathlib import Path
validator.generate_interactive_report(
validation_results=results,
output_path=Path("reports/validation_report.html" )
)
print ("📊 Interactive report saved to reports/validation_report.html" )
Text Report
text_report = validator.generate_report(results)
print (text_report)
Files Included data-validation-reporter/
├── SKILL.md # This file
├── validator_template.py # Validator class template
├── config_template.yaml # YAML configuration template
├── example_usage.py # Example implementation
└── README.md # Quick reference
Integration
Add to Existing Project cp validator_template.py src/validators/data_validator.py
cp config_template.yaml config/validation.yaml
uv pip install pandas plotly pyyaml
from src.validators.data_validator import DataValidator
validator = DataValidator(config_path="config/validation.yaml" )
results = validator.validate_dataframe(df)
validator.generate_interactive_report(results, Path("reports/output.html" ))
Customization
Extend Validation Rules class CustomValidator (DataValidator ):
def _check_business_rules (self, df: pd.DataFrame ) -> List [str ]:
"""Add custom business logic validation."""
issues = []
if 'start_date' in df.columns and 'end_date' in df.columns:
invalid_dates = (df['end_date' ] < df['start_date' ]).sum ()
if invalid_dates > 0 :
issues.append(f'{invalid_dates} records with end_date before start_date' )
return issues
Custom Visualizations
fig = make_subplots(
rows=3 , cols=2 ,
specs=[
[{'type' : 'indicator' }, {'type' : 'bar' }],
[{'type' : 'bar' }, {'type' : 'table' }],
[{'type' : 'scatter' , 'colspan' : 2 }, None ]
]
)
fig.add_trace(
go.Scatter(x=df['date' ], y=df['quality_score' ], name='Quality Trend' ),
row=3 , col=1
)
Performance Benchmarks (tested on 100,000 row dataset):
Validation: ~2.5 seconds
Report generation: ~1.2 seconds
Total: ~3.7 seconds
Memory usage : ~150MB for 100k rows
Tested up to 1M rows
Linear scaling for validation
Report generation optimized with sampling for large datasets
Best Practices
Configuration Management :
Store validation rules in YAML (version controlled)
Use environment-specific configs (dev/staging/prod)
Document validation thresholds
Logging :
Enable DEBUG level during development
Use INFO level in production
Log all validation failures
Reporting :
Generate reports for all production data loads
Archive reports with timestamps
Include reports in data lineage
Quality Gates :
Set minimum quality score thresholds
Block pipelines on validation failures
Alert on quality degradation
Dependencies pandas>=1.5.0
plotly>=5.14.0
pyyaml>=6.0
Related Skills
csv-data-loader - Load and preprocess CSV dataplotly-dashboard - Advanced dashboard creationdata-quality-monitor - Continuous quality monitoring
Examples See example_usage.py for complete working examples:
Basic validation workflow
Custom validation rules
Batch validation (multiple files)
Quality trend analysis
Integration with data pipelines
Change Log
Initial skill creation from production code
4-panel Plotly dashboard
YAML configuration support
Quality scoring algorithm
Missing data and type validation
License Part of workspace-hub skill library. See root LICENSE.
Support For issues or enhancements, see workspace-hub issue tracker.
