data-validator

Detect anomalies and validate data quality with ML-powered analysis.

Updated Aug 23, 2026

Search Tags:#data integrity #data quality #data validation #anomaly detection #statistical analysis #ML #NFL data

One-click install

npx skills add https://github.com/VatoCodes/Bet-That_Proof-of-Concept --skill data-validator

Or copy as Structured Prompt for Agent▼

Please help me install this Agent Skill.
Skill: data-validator
Source: https://github.com/VatoCodes/Bet-That_Proof-of-Concept/tree/main/.claude/skills/data-validator
Command: npx skills add https://github.com/VatoCodes/Bet-That_Proof-of-Concept --skill data-validator

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, scikit-learn, and includes scripts (resource) components.

What problem does it solve?

This Skill provides ML-powered anomaly detection and comprehensive data quality insights, moving beyond basic checks to proactively identify statistical outliers, pattern breaks, and inconsistencies. It ensures your data is always reliable and accurate for critical decisions.

Core Features & Use Cases

ML-Powered Anomaly Detection: Identifies outliers and unusual patterns using statistical analysis and algorithms like Isolation Forest.
Comprehensive Quality Scoring: Assesses data completeness, accuracy, consistency, and timeliness with an overall quality grade.
Schema & Business Rule Validation: Verifies data structure, types, ranges, and adherence to NFL-specific business rules.
Use Case: Before running your weekly betting models, use this Skill to automatically validate all incoming data, detect any anomalies in player stats or odds, and generate a detailed quality report, ensuring your models operate on pristine data.

Quick Start

Validate all data for week 7 and generate a quality report.