Skill UI

This skill provides a methodology to detect data exfiltration attempts hidden within DNS queries. By analyzing Zeek dns.log files, the agent computes Shannon entropy of subdomains, flags unusually long labels, and monitors unique subdomain counts per parent domain. This technique is crucial for identifying malicious tunneling tools like dnscat2 or iodine that bypass traditional security controls.

This skill provides structured procedures for detecting Golden Ticket attacks, a severe threat involving forging Kerberos Tickets in Active Directory. It analyzes Windows Event ID 4769 for indicators such as RC4 encryption downgrades, abnormal ticket lifetimes, and inconsistencies in TGS/TGT requests. Designed for SOC analysts and threat hunters using SIEM platforms (Splunk/Elastic) to enhance security monitoring coverage against credential theft.

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 guide details a structured threat hunting methodology designed to proactively detect sophisticated spearphishing campaigns. It instructs users on correlating indicators across email logs, endpoint telemetry (EDR), and network data (SIEM) to identify targeted threats, supporting incident response and security posture improvement.

A comprehensive guide on implementing AWS CloudTrail log analysis for robust security monitoring, threat detection, and forensic investigation. Learn how to configure CloudTrail, use Amazon Athena for SQL querying, and leverage CloudWatch Logs Insights and SIEM integration to identify unauthorized access, privilege escalation, and suspicious API activity across AWS services.

Systematically optimizes Security Information and Event Management (SIEM) detection rules in platforms like Splunk and Elastic. This process involves analyzing historical alert volumes, establishing environmental baselines, creating context-aware whitelists, and statistically adjusting detection thresholds to significantly reduce false positives, improving overall security efficacy.

Provides a detailed, multi-platform workflow to monitor long-running computational experiments. It checks the status of jobs across various environments (SSH, vast.ai, Modal), collects screen logs, fetches structured JSON results, and pulls detailed training metrics (loss curves, accuracy) from Weights & Biases (W&B). Essential for MLOps and automated result review.

This skill implements a multi-factor scoring model to assess the criticality of IT assets. By weighting factors such as business function impact, data sensitivity, regulatory scope, and network exposure, it assigns a risk tier. This tier is then used to dynamically adjust the Service Level Agreements (SLAs) for vulnerability remediation, ensuring that organizations prioritize patching efforts on their most critical 'crown jewel' systems first.

This skill provides a systematic framework for managing Indicators of Compromise (IOCs) throughout their entire life cycle—from initial discovery and validation to enrichment, deployment, monitoring, and eventual retirement. It emphasizes implementing confidence decay functions, tracking hit rates, and managing false positives to maintain a high-quality, actionable threat intelligence database, thereby minimizing analyst fatigue and maximizing detection efficacy.

This skill automates deep packet capture (PCAP) analysis using tshark and pyshark. It is invaluable for security assessments and incident response, enabling the extraction of protocol statistics, identification of suspicious network flows (e.g., port scanning, beaconing), and comprehensive extraction of Indicators of Compromise (IOCs), including IPs, domains, and URLs. It also specializes in detecting complex anomalies like DNS tunneling.

This tool conducts comprehensive, sector-specific threat landscape assessments by analyzing designated threat actor patterns (e.g., APT groups, cybercriminals), common attack vectors, and industry-specific vulnerabilities. It is designed to provide actionable intelligence for risk management, security investment prioritization, and executive reporting across critical sectors like finance, healthcare, and energy.

This guide demonstrates how to build comprehensive forensic super-timelines using Plaso (log2timeline) to correlate events across diverse sources. It covers the entire workflow, from processing disk images and various logs (MFT, registry, browser history) to generating standardized CSV, JSONL exports, and visualizing the results in tools like Timesketch for deep investigative analysis.