Skill UI

This skill performs User and Entity Behavior Analytics (UEBA) to identify anomalous user activities such as impossible travel, unusual access patterns, and privilege abuse. It establishes behavioral baselines using SIEM logs and statistical analysis, helping SOC teams detect insider threats and compromised accounts by flagging deviations from normal user behavior. Use when monitoring security hygiene and suspicious activity is critical.

Leverage Eric Zimmerman's EZ Tools suite for comprehensive Windows forensic artifact analysis. This guide details how to parse critical system data, including the Master File Table (MFT), registry hives, prefetch files, and event logs. Essential for digital forensics investigators and incident responders conducting deep system analysis.

This comprehensive guide teaches advanced iOS reverse engineering techniques using Frida dynamic instrumentation. Users can analyze proprietary app logic, extract encryption keys, trace method calls, and bypass anti-tampering or anti-debugging mechanisms without needing source code. It is essential for authorized mobile security testing and protocol analysis.

This skill detects malicious Command and Control (C2) communications tunneled through the DNS protocol. It analyzes DNS query logs for indicators such as DNS tunneling tools (Iodine, dnscat2), Domain Generation Algorithms (DGA), and encoded payloads within TXT/CNAME records. Techniques include Shannon entropy analysis of subdomains, statistical anomaly detection, and machine learning classification. Ideal for network security analysts conducting deep threat hunting and forensic investigations.

This is an advanced, structured workflow designed to conduct deep, multi-source investigations. It synthesizes current public evidence with user-provided context to generate highly structured reports. The workflow explicitly differentiates between verifiable facts, user-supplied evidence, logical inferences, and actionable recommendations, making it ideal for complex comparative analysis or continuous monitoring tasks.

Guides learners through reconnaissance, scanning, vulnerability analysis, exploitation, and reporting phases of authorized penetration testing, pairing command examples with tool choices and professional deliverables.

Performs a systematic and structured investigation to determine the true source of bugs, errors, and unexpected system behavior, rather than merely treating symptoms. This method utilizes techniques like the Five Whys, hypothesis-driven testing, stack trace analysis, and gathering evidence (logs, git diffs) across multiple phases. It is ideal for complex, intermittent, or deeply rooted technical issues in software development.

This advanced workflow automates the process of analyzing the operational health of specific Azure resources. It ingests and analyzes raw logs, telemetry data, and performance metrics from diverse Azure services (e.g., VMs, Web Apps, Databases). The system performs comprehensive root cause analysis, classifies issues (Critical, High, Medium), and generates actionable, step-by-step remediation plans, significantly reducing manual troubleshooting time.

A structured methodology for coordinating multiple, simultaneous investigation threads to determine the root cause of complex system failures, performance regressions, or integration bugs. This skill decomposes a large problem into independent tracks (e.g., database, application code, infrastructure), assigns ownership, and manages structured sync points (Continue/Pivot/Converge) to converge on a solution faster than serial debugging. Essential for high-stakes production incident response.

Biopython is a robust, comprehensive toolkit for computational molecular biology written in Python. It enables users to manipulate biological sequences (DNA, RNA, protein), parse standard formats (FASTA, GenBank, PDB, etc.), and programmatically access major bioinformatics databases like NCBI via Entrez. This skill is ideal for developing complex bioinformatics pipelines, including sequence alignment, structural analysis, and phylogenetic tree construction.

A comprehensive Python package providing a unified interface to over 40 major bioinformatics web services and databases. Use it for complex biological workflows, including cross-database querying (e.g., UniProt, KEGG, ChEMBL), identifier mapping, protein sequence retrieval, pathway analysis, and running sequence similarity searches like BLAST. It streamlines data integration from multiple life science resources into consistent Python code.

COBRApy is a powerful Python library for Constraint-Based Reconstruction and Analysis (COBRA). It enables deep dives into cellular metabolism, allowing users to build, simulate, and analyze complex metabolic models (e.g., genome-scale models). Core functionalities include Flux Balance Analysis (FBA), Flux Variability Analysis (FVA), performing gene/reaction knockouts, and optimizing growth media. It supports standard metabolic model formats like SBML, JSON, and YAML.