Agent skill · data analytics · datadrivenconstruction
data-anomaly-detector
Detect anomalies and outliers in construction data: unusual costs, schedule variances, productivity spikes. Statistical and ML-based detection methods.
Why this skill is useful
Provides domain-specific anomaly detection methods and statistical thresholds tailored for construction data.
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 Anomaly Detector for Construction Overview Detect unusual patterns, outliers, and anomalies in construction data. Identify cost overruns, schedule delays, productivity issues, and data quality problems before they impact projects. Business Case Construction data often contains anomalies that indicate: Cost estimate errors or fraud Schedule logic issues Productivity problems Data entry mistakes Equipment or material issues Early detection prevents costly corrections and project delays. Technical Implementation Quick Start Dependencies Resources Statistical Methods: IQR, Z-Score, Modified Z-Score Construction Benchmarks: RSMeans, ENR indices
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills datadrivenconstruction/data-anomaly-detector