Agent skill · data analytics · datadrivenconstruction
data-quality-check
Assess construction data quality using completeness, accuracy, consistency, timeliness, and validity metrics. Automated validation with regex patterns, thresholds, and reporting.
Why this skill is useful
Provides domain-specific data validation methods and metrics tailored for construction data quality assessment.
What it needs
Requires python3 installed locally. About 6k tokens when loaded. Last updated 2026-02-14. 265 stars on the source repository.
What this skill does
Data Quality Check for Construction Overview Based on DDC methodology (Chapter 2.6), this skill provides comprehensive data quality assessment for construction projects. Poor data quality leads to poor decisions - validate early, validate often. Book Reference: "Требования к качеству данных и его обеспечение" / "Data Quality Requirements" "Качество данных определяется пятью ключевыми метриками: полнота, точность, согласованность, своевременность и достоверность." — DDC Book, Chapter 2.6 Quick Start Data Quality Dimensions The 5 Quality Metrics Validation Rules Builder Custom Validation Rules Automated Quality Pipeline Quality Reporting Export Quality Report Quick Reference Metric Description Threshold -------- ------------- ----------- Completeness % non-null values ≥ 95% Accuracy Values within valid range ≥ 98% Consistency Unique IDs, valid relationships ≥ 99% Validity Match expected patterns ≥ 95% Timeliness Records updated recently ≥ 80% Common Validation Patterns Resources Book: "Data-Driven Construction" by Artem Boiko, Chapter 2.6 Website: https://datadrivenconstruction.io Great Expectations: https://greatexpectations.io Next Steps See bim-validation-pipeline for BIM-specific validation See etl-pipeline for data processing pipelines See data-visualization for quality dashboards
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills datadrivenconstruction/data-quality-check