{"skill":{"slug":"data-anomaly-detector","displayName":"Data Anomaly Detector","summary":"Detect anomalies and outliers in construction data: unusual costs, schedule variances, productivity spikes. Statistical and ML-based detection methods.","tags":{"latest":"2.1.0"},"stats":{"comments":0,"downloads":3736,"installsAllTime":32,"installsCurrent":30,"stars":1,"versions":2},"createdAt":1770475515502,"updatedAt":1777525033005},"latestVersion":{"version":"2.1.0","createdAt":1771174787782,"changelog":"- Added detailed documentation and usage examples to SKILL.md for construction anomaly detection.\n- Clarified support for statistical (IQR, z-score) and business rule-based anomaly detection for costs and schedules.\n- Described construction-specific thresholds and anomaly types handled (cost, schedule, productivity).\n- Provided technical overview and sample Python implementation in SKILL.md.","license":null},"metadata":{"os":["darwin","linux","win32"],"systems":null},"owner":{"handle":"datadrivenconstruction","userId":"publishers:datadrivenconstruction","displayName":"datadrivenconstruction","image":"https://avatars.githubusercontent.com/u/94158709?v=4"},"moderation":{"isSuspicious":true,"isMalwareBlocked":false,"verdict":"suspicious","reasonCodes":["suspicious.llm_suspicious"],"summary":"Detected: suspicious.llm_suspicious","engineVersion":"v2.4.5","updatedAt":1777525033005}}