Skill UI

Access multi-dimensional cancer genomics data via cBioPortal REST API for mutation, copy-number, expression, and survival insights across TCGA/GENIE studies, enabling target validation and clinical association analyses programmatically with Python clients.

Query InterPro for protein family, domain, and GO annotations via a REST API with Python helpers, enabling function prediction, domain architecture mapping, and evolutionary classification insights for proteomic workflows.

Query the JASPAR REST API and Python helpers to retrieve TF binding profiles, convert PFMs to PWMs, and scan DNA sequences for high-confidence motifs, enabling regulatory variant interpretation, ChIP/ATAC analysis, and TF network construction.

Pandas is a Python data analysis library that simplifies loading, cleaning, transforming, and analyzing structured datasets, making it ideal for data wrangling in analytics or engineering workflows.

PyGraphistry is a Python interface to the Graphistry platform for GPU-accelerated graph visualization, letting analysts render millions of nodes/edges in interactive dashboards to surface anomalies and relationships quickly.

PyMC is a powerful Python library for probabilistic programming and advanced Bayesian inference. This comprehensive guide details the full workflow for building, fitting, and validating complex Bayesian models. Learn to implement hierarchical structures, perform MCMC sampling (NUTS), conduct posterior predictive checks (PPC), and effectively quantify uncertainty using industry best practices.

This skill details the forensic analysis of Windows LNK shortcut files and Jump Lists. These artifacts record critical user activity, including file access and program execution, even after the original source files have been deleted. It covers using specialized tools (LECmd, JLECmd) and Python parsing techniques to reconstruct user timelines, making it essential for incident response and deep digital forensics examinations.

Utilize the Scapy library in Python to perform deep packet inspection, crafting, sending, and sniffing of various network protocols (TCP, UDP, ICMP, DNS). This skill is essential for analyzing pcap files, implementing advanced security techniques like SYN scans, extracting protocol fields, and detecting network anomalies such as fragmentation or tunneling indicators. Ideal for security researchers, SOC analysts, and penetration testers.

This skill parses Windows Prefetch (.pf) files to reconstruct detailed application execution history. It is crucial for digital forensics, allowing analysts to track execution timelines, identify suspicious or renamed binaries, and investigate potential malware activity or lateral movement during incident response. It uses Python to extract run counts and accessed file details.

This comprehensive skill guides the deployment and utilization of MISP, a powerful open-source threat intelligence platform. It covers configuring various threat feeds (TAXII, CSV, etc.) and using the PyMISP Python API to programmatically automate the collection, storage, and correlation of Indicators of Compromise (IOCs) and threat events from diverse community and commercial sources.

This skill provides structured procedures and Python examples for detecting internal data exfiltration. It analyzes endpoint, cloud, and email logs by establishing behavioral baselines and performing statistical anomaly detection. Use cases include investigating insider threats, monitoring DLP policy violations, and building robust User and Entity Behavior Analytics (UEBA) rules for SOC teams.

This skill analyzes proxy access logs, DNS query logs, and network flow data to identify unauthorized SaaS and cloud services (Shadow IT). It uses Python and pandas to aggregate traffic, classify domains against known SaaS categories, and calculate risk scores based on data volume and user count. Ideal for conducting thorough security audits and generating actionable remediation reports.