Agent skill · data analytics · datadrivenconstruction
data-profiler
Profile construction data to understand characteristics, distributions, quality metrics, and patterns. Essential for data quality assessment and ETL planning.
Why this skill is useful
Provides domain-specific patterns and quality metrics for construction data profiling that the AI wouldn't reliably generate on its own.
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 Profiler for Construction Overview Analyze construction data to understand its characteristics, distributions, quality, and patterns. Essential for data quality assessment, ETL planning, and identifying data issues before they impact projects. Business Case Before using any construction data, you need to understand: What data types are present Distribution of values Missing data patterns Anomalies and outliers Referential integrity issues This skill profiles data to answer these questions and provides actionable insights. Technical Implementation Quick Start Common Use Cases 1. Pre-ETL Analysis: Profile source data before building pipelines 2. Quality Monitoring: Track data quality over time 3. Schema Validation: Detect unexpected changes in data structure 4. Anomaly Detection: Find outliers and data quality issues Dependencies Resources Data Profiling Best Practices: DAMA DMBOK Construction Data Standards: CSI MasterFormat, UniFormat
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills datadrivenconstruction/data-profiler