Agent skill · data analytics · datadrivenconstruction
cwicr-data-validator
Validate CWICR data quality and estimate inputs. Check for errors, inconsistencies, outliers, and missing data.
Why this skill is useful
Provides domain-specific validation rules and outlier detection methods for CWICR data that the AI wouldn't reliably generate on its own.
What it needs
Requires python3 installed locally. About 5k tokens when loaded. Last updated 2026-02-14. 265 stars on the source repository.
What this skill does
CWICR Data Validator Business Case Problem Statement Data quality issues cause: Incorrect estimates Budget overruns Delayed projects Rework costs Solution Systematic validation of CWICR data and estimate inputs to catch errors, outliers, and inconsistencies before they impact projects. Business Value Error prevention - Catch issues early Data quality - Ensure reliable estimates Consistency - Standard validation rules Audit trail - Document data issues Technical Implementation Quick Start Common Use Cases 1. Data Quality Score 2. Validate DataFrame 3. Export Report Resources GitHub: OpenConstructionEstimate-DDC-CWICR DDC Book: Chapter 2.1 - Data Quality Management
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills datadrivenconstruction/cwicr-data-validator