Skill UI

Implement robust, structured logging for API requests, capturing performance metrics, correlation IDs, and detailed security audit trails. This skill ensures compliance by handling PII redaction and providing comprehensive, JSON-formatted logs suitable for ELK Stack or Loki. Essential for debugging, performance analysis, and maintaining SOC 2 compliance in distributed microservices.

This guide details how to write advanced correlation searches using Splunk's Search Processing Language (SPL). It covers techniques like threshold-based detection, sequence analysis, and anomaly detection to identify sophisticated security threats (e.g., brute force, data exfiltration, lateral movement) in a Security Operations Center (SOC) environment. Mastery of SPL is essential for closing SIEM detection gaps and ensuring compliance.

This skill guides SOC analysts and detection engineers on how to correlate complex, multi-stage attacks within the IBM QRadar SIEM platform. It covers advanced usage of AQL (Ariel Query Language) for deep event investigation, building custom correlation rules (Building Blocks and Offenses), and cross-source data correlation (e.g., auth failures with network flows). It is essential for reducing false positives and improving threat detection accuracy.

Utilizes SIEM platforms (Splunk/Elastic) and Windows Event Log analysis (ID 4769) to detect indicators of Golden Ticket attacks. This skill analyzes Kerberos logs for specific anomalies such as RC4 encryption downgrades, abnormal ticket lifetimes, and correlation failures between TGT and TGS requests, crucial for advanced threat hunting in Active Directory environments.

A comprehensive guide and framework for detecting attacker lateral movement within enterprise networks. It details the use of network flow analysis (Zeek), authentication log review (Windows Event Logs, Kerberos), and dedicated SIEM correlation rules (Splunk, Elastic) to identify techniques like Pass-the-Hash and RDP hopping. Ideal for threat hunters and security engineers building detection pipelines.

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.

This guide focuses on developing multi-event correlation rules using Splunk SPL and Sigma format. It teaches how to detect Advanced Persistent Threats (APT) and lateral movement by chaining multiple Windows authentication, process execution, and network connection events across hosts within specified time windows. Use this to surface complex, multi-stage attack sequences that single-event detection rules typically miss.

This tool builds comprehensive forensic super-timelines by using Plaso to correlate events across diverse sources. It aggregates data from file system metadata, event logs, browser history, and registry artifacts into a unified chronological view. This capability is essential for complex digital forensic investigations requiring cross-source event correlation or detailed activity sequence reporting.

This skill utilizes AI/LLM reasoning capabilities combined with industry-standard OSINT tools (Sherlock, theHarvester, SpiderFoot). It correlates fragmented data—including usernames, emails, domain records, and breach data—from multiple sources into unified, confidence-scored intelligence profiles. It is ideal for deep target profiling, identity resolution, and mapping complex threat landscapes.

A sophisticated skill for detecting Command and Control (C2) traffic tunneled through DNS protocols. It utilizes advanced techniques like Shannon entropy analysis of subdomains, Machine Learning for DGA classification, and correlation with network logs from Zeek/Suricata. Ideal for SOC analysts and threat hunters investigating data exfiltration, DNS beaconing, or suspicious DNS activity.

Improves Go error handling by treating errors as structured data. This utility adds critical context—such as domain, user IDs, stack traces, and machine-readable error codes—to standard Go errors. This capability is vital for enhancing observability, enabling APM tools (like Sentry or Datadog) and on-call engineers to diagnose complex issues quickly without relying on manual log correlation.

This guide provides a systematic approach to deep debugging complex HubSpot API issues. It includes layer-by-layer network testing (DNS, TCP, TLS), correlation ID tracking, and timing analysis to isolate intermittent or hard-to-reproduce errors. Use this when standard troubleshooting fails or when compiling detailed evidence for technical support.