Skill UI

A comprehensive, step-by-step guide for security professionals to perform deep-dive malware analysis. It covers methodologies for static analysis (disassemblers, imports), dynamic execution (sandboxing, monitoring), identifying Indicators of Compromise (IOCs), and structuring professional threat reports. Essential for incident response and threat intelligence.

This skill specializes in implementing Service Level Objectives (SLOs) and Service Level Indicators (SLIs) to enforce reliability standards. It guides users in designing comprehensive SLO frameworks, calculating error budgets, and building robust monitoring systems. Use this when you need to establish data-driven reliability targets, balance feature velocity with technical health, and align service performance directly with core business objectives.

This tool parses and analyzes Cobalt Strike Malleable C2 profiles, extracting critical network indicators such as HTTP/DNS transforms, URIs, headers, jitter, and sleep cycles. It is essential for reverse-engineering captured profiles or building robust network detection signatures for threat hunting and incident response.

A comprehensive guide for deep packet inspection and network forensics using Wireshark and tshark. This skill is critical for incident response, where it allows users to capture, filter, and analyze network packet data to identify malicious command-and-control traffic, diagnose complex protocol issues, extract indicators of compromise (IOCs), and reconstruct data exfiltration paths. Use it for validating security rules and understanding network anomalies.

This comprehensive skill analyzes Zeek conn.log and NetFlow data to proactively detect advanced indicators of ransomware activity. It identifies critical threats such as Command and Control (C2) beaconing patterns, connections to TOR exit nodes, large-scale data exfiltration flows, and suspicious DNS anomalies. It is essential for threat hunting and complex security incident response.

A comprehensive system for systematically collecting, categorizing, and enriching Indicators of Compromise (IOCs) from various sources (network logs, memory dumps, email, etc.). It supports the entire incident response lifecycle, ensuring structured data representation using STIX/TAXII formats for robust threat intelligence sharing and defensive system automation.

A specialized Python detector designed to monitor Operational Technology (OT) historian servers (such as OSIsoft PI, Ignition, etc.). It identifies critical indicators of compromise (IOCs), including unauthorized network connections, data manipulation attempts, and lateral movement activities that bridge IT and OT zones. Use this skill when validating data integrity and investigating potential cyber breaches in industrial environments.

This skill detects compromised cloud credentials by analyzing anomalous API activity, impossible travel patterns, and unauthorized access indicators across AWS, Azure, and GCP. It integrates findings from GuardDuty, Defender for Identity, and Security Command Center to provide comprehensive threat visibility and assist in incident response and scope assessment.

A comprehensive guide and analysis framework for detecting compromised Office 365 or Google Workspace email accounts. It focuses on analyzing unified audit logs and Azure AD sign-in logs to identify indicators of account takeover (ATO) or Business Email Compromise (BEC). Detection methods include monitoring impossible travel events, identifying malicious inbox rules (e.g., external forwarding or deletion rules), and analyzing suspicious Microsoft Graph API access patterns.

A comprehensive guide and workflow for detecting Mimikatz and similar credential-dumping activities. It utilizes command-line pattern matching, LSASS memory access signatures, binary indicators, and in-memory analysis. Ideal for threat hunting, scoping compromises during incident response, or validating detection coverage in purple team exercises using EDR/SIEM data.

A comprehensive skill for detecting early-stage ransomware indicators before data encryption occurs. It utilizes advanced network detection tools such as Zeek and Suricata, combined with SIEM correlation rules and threat intelligence feeds. This method monitors the entire pre-encryption kill chain, identifying precursor behaviors like initial access broker activity, Cobalt Strike C2 beaconing, credential harvesting (e.g., Mimikatz signatures), and data staging attempts, enabling timely incident response and containment.

A comprehensive guide for security professionals to detect data exfiltration by analyzing network telemetry (Zeek, Suricata) and logs. Techniques include identifying DNS tunneling, unusual cloud uploads, and detecting large/abusive data flows across various channels during incident response and proactive threat hunting.