Skill UI

Enables Claude to apply anomaly-detection-system workflows for spotting outliers, fraud, or unusual patterns across datasets, selecting tailored algorithms like Isolation Forest and summarizing deviations for any data monitoring or security use case.

A comprehensive workflow for building and optimizing e-commerce stores using WooCommerce. This guide covers all phases, including store setup, payment gateway integration, shipping configuration, and custom product types. It heavily integrates advanced WordPress 7.0 features, such as AI-powered content generation, fraud detection, and real-time team collaboration tools, ensuring a modern, highly functional online store.

Codified expertise for orchestrating the full returns lifecycle—from authorisations and inspection through disposition, refunds, fraud detection, and warranty claims—to balance customer satisfaction with margin protection across retail, e-commerce, and omnichannel operations.

Standardized expert guidance for return authorization, inspection, grading, disposition decisions, refunds, fraud detection, and vendor recovery in retail and e-commerce operations.

This skill provides comprehensive guidelines for detecting Business Email Compromise (BEC), a sophisticated fraud scheme. It covers detection techniques using email gateway rules, behavioral analytics, and robust financial process controls. Learn to identify red flags like unusual payment requests, domain mismatches, and patterns of social engineering to protect against financial loss.

This system deploys advanced AI, NLP, and behavioral analytics to detect sophisticated Business Email Compromise (BEC) attacks. Unlike traditional filters that rely on malicious links, this solution analyzes writing style, tone, grammatical patterns, and contextual anomalies to identify pure social engineering impersonation, providing a robust defense layer against advanced phishing and fraud.

This skill detects AI-generated deepfake audio used in voice phishing (vishing) attacks. It functions by extracting critical spectral features (including MFCCs, spectral centroid, and zero-crossing rate) and passing them through advanced machine learning models (like Random Forest and Gradient Boosting). The system supports batch forensic analysis, provides confidence scores, and generates detailed reports to verify the authenticity of recorded voice calls, making it essential for cybersecurity incident response and fraud investigation.