Skill UI

Hypogenic automates LLM-driven hypothesis generation and testing over tabular datasets, blending literature insights with empirical analysis to accelerate discovery in domains like deception detection or AI content evaluation.

Medchem Filtering Toolkit is a Python library offering drug-likeness heuristics, structural alert and PAINS filters, group detection, catalogs, and complexity scoring to triage compound libraries and prioritize leads in drug discovery workflows.

A comprehensive skill for leveraging massive pre-trained transformer models across diverse AI domains, including Natural Language Processing (NLP), Computer Vision (CV), and Audio. Use this skill for advanced tasks such as text generation, classification, question answering, summarization, object detection, and model fine-tuning on proprietary datasets. It provides streamlined pipelines and granular control for deployment.

An AI-driven comprehensive health analysis system that integrates multi-dimensional datasets, including biometrics, lifestyle, and medical history. It excels at anomaly detection, predicting various health risks (e.g., CVD, Diabetes), providing personalized, evidence-based recommendations, and generating detailed, interactive health reports.

Leverages Monte Carlo-based simulation to spot potential software vulnerabilities by feeding randomized inputs into modules, tracking failure likelihoods, and highlighting risky code paths for security testing.

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

Leverage Certificate Transparency (CT) logs via crt.sh and Certstream to proactively monitor for suspicious SSL/TLS certificates. This skill helps detect lookalike domains, unauthorized certificate issuance, and potential phishing infrastructure targeting your organization before attacks launch. Includes querying logic and threat intelligence integration.

This tool detects abnormal access patterns across major cloud storage services (AWS S3, GCS, Azure Blob Storage). By analyzing audit logs like CloudTrail, it uses statistical baselines and time-series anomaly detection to identify suspicious activities such as after-hours bulk downloads, access from new IPs, or unusual API call spikes. It is crucial for investigating potential data exfiltration incidents or building robust cloud security detection rules.

This skill analyzes DNS query logs to detect sophisticated threats such as data exfiltration via DNS tunneling, Domain Generation Algorithm (DGA) usage, and covert Command and Control (C2) channels. It utilizes advanced techniques like entropy analysis, query volume anomaly detection, and subdomain length analysis within SIEM platforms, helping SOC teams identify threats that bypass traditional network security controls.

A powerful toolkit utilizing Objection and Frida for authorized mobile penetration testing. It enables deep runtime analysis of iOS applications, allowing users to inspect sensitive data in the keychain, filesystem, and memory. Key capabilities include bypassing client-side protections such as SSL pinning and jailbreak detection without requiring source code access.

This skill enables deep analysis of malware families using the Malpedia API. It allows users to retrieve canonical aliases, associated threat actors, and YARA detection rules, and map complex relationships like loader-payload chains. Ideal for threat intelligence research, incident response, and building automated detection signatures.

A comprehensive guide for analyzing PCAP files captured during malware execution. It demonstrates advanced techniques using Wireshark, Zeek, Suricata, and Python (scapy) to identify Command and Control (C2) communications, data exfiltration channels, Domain Generation Algorithms (DGA), and periodic beaconing patterns. Essential for malware reverse engineering and network threat detection.