Skill UI

This tool provides comprehensive network traffic analysis capabilities by querying the Arkime API. Users can search sessions, download PCAPs for forensics, detect sophisticated threats like C2 beaconing (via jitter/interval analysis), spot DNS tunneling patterns, and flag connections to known-bad TLS certificate issuers. Ideal for security assessment and incident investigation.

This skill establishes normal network communication patterns by analyzing historical NetFlow/IPFIX data. Utilizing Python pandas, it computes hourly/daily volume distributions, per-host statistics, and top-talker profiles. Anomalies are detected using sophisticated statistical methods like z-score and IQR, allowing SOC analysts to quickly identify deviations such as data exfiltration spikes, persistent beaconing, or unusual port usage.

This skill guides security teams on systematically tuning SIEM detection rules in platforms like Splunk and Elastic. It focuses on reducing alert fatigue and false positives by analyzing historical alert volumes, establishing environmental baselines, creating context-aware whitelists, and measuring detection efficacy using metrics like precision and recall. This is crucial for maturing a Security Operations Center (SOC).

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.

Generate publication-quality figures and tables from experiment data. Supports line plots, bar charts, scatter plots, heatmaps, box/violin plots, and multi-panel figures for academic papers. Uses consistent matplotlib styling, high-resolution PDF/PNG output, and can optionally review figures via Codex MCP.

Implements a robust, multi-factor model to calculate the criticality score of IT assets. The scoring incorporates data sensitivity, business function dependency, regulatory scope, and network exposure, moving beyond simple CVSS scores. The resulting 1-5 criticality tier directly modifies vulnerability remediation Service Level Agreements (SLAs), ensuring security resources are focused on the organization's most valuable and risky systems.

This skill provides a comprehensive framework for vulnerability prioritization by combining three crucial data points: CISA's Known Exploited Vulnerabilities (KEV) catalog (confirming real-world exploitation evidence), EPSS scores (predicting exploitation probability), and CVSS metrics (assessing intrinsic severity). It builds a multi-factor prioritization engine to determine the most critical CVEs for immediate remediation, assisting security teams in managing large vulnerability backlogs efficiently.

This skill detects covert data exfiltration occurring over DNS tunnels. It analyzes DNS query names by computing Shannon entropy, examining query length distributions, and identifying high subdomain cardinality. It is essential for security operations, incident response, and proactive threat hunting when suspicious or encoded DNS activity is suspected.

This comprehensive guide outlines a structured workflow for investigating insider threats—incidents where authorized employees or contractors misuse their access to steal data or sabotage systems. It integrates DLP alerts, User Behavior Analytics (UBA), and digital forensics, emphasizing mandatory collaboration between Legal, HR, and Security teams to build an admissible, evidence-based case.

This comprehensive guide details techniques for performing forensic investigations on Linux systems. It covers analyzing critical logs—including auth.log, syslog, systemd journal, and auditd—to reconstruct user sessions, trace unauthorized access, detect privilege escalation, and establish detailed event timelines following a suspected security compromise.

A comprehensive guide for collecting, parsing, and correlating system, application, and security logs from diverse sources (Windows, Linux, Web). This technique is crucial for reconstructing detailed event timelines, identifying initial access vectors, tracking lateral movement, and establishing evidence of unauthorized activity during post-breach forensic investigations.

This skill performs deep forensic analysis on SQLite databases, going far beyond simple SQL queries. It is used in digital forensics and incident response to recover deleted records from freelist pages, analyze Write-Ahead Logs (WAL), and examine internal B-tree structures. It is essential for extracting critical evidence from mobile, browser, and application data sources.