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data-anomaly-detector

Detect anomalies in construction cost, schedule, and productivity data.

Updated Mar 18, 2026
Search Tags:#ml#data-quality#anomaly-detection#statistical-methods#outliers#construction-data
One-click install
npx skills add https://github.com/kaifashraff/jarvis-research --skill data-anomaly-detector
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-anomaly-detector
Source: https://github.com/kaifashraff/jarvis-research/tree/main/skills/data-anomaly-detector
Command: npx skills add https://github.com/kaifashraff/jarvis-research --skill data-anomaly-detector

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Detects anomalies and outliers in construction data across costs, schedules, and productivity to prevent costly misallocations and delays.

Core Features & Use Cases

  • Cost anomaly detection: identify unusual costs and potential data entry errors using statistical methods.
  • Schedule and productivity monitoring: detect delays, duration anomalies, and productivity spikes to keep projects on track.
  • Time-series and data-quality checks: flag irregular patterns over time and potential data quality issues for audit trails.

Quick Start

Load your project data as a DataFrame and run the detector with a configuration dictionary to generate an anomaly report.

Frequently Asked Questions about data-anomaly-detector

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I detect anomalies and outliers in construction project data?▼

Anomaly detection in construction data uses statistical methods and ML to analyze costs, schedules, and productivity. It identifies irregularities or data entry errors to prevent costly misallocations and delays.

What is the best way to identify unusual costs and data entry mistakes in construction schedules?▼

Statistical methods and ML-based detection identify unusual costs and schedule anomalies by analyzing project DataFrames. This flags outliers and irregular patterns over time for actionable audit insights.

Can I use a configuration dictionary to run anomaly detection on my project DataFrame?▼

Yes, you load project data as a DataFrame and run the detector with a configuration dictionary. This flexible configuration generates a report highlighting productivity spikes, delays, and data quality issues.

How do ML and statistical methods monitor time-series data quality in construction?▼

ML and statistical methods monitor time-series data quality by flagging irregular patterns and potential errors over time. This creates audit trails for costs, schedules, and productivity metrics.

Why use ML-based anomaly detection for construction productivity spikes and delays?▼

ML-based anomaly detection is used for construction productivity spikes and delays because it identifies complex outliers across schedules and costs. It provides actionable insights to keep projects on track.

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