Skill UI

This guide provides production-grade architecture patterns for implementing robust LLM observability using Langfuse. It covers singleton SDK setup, context propagation across asynchronous boundaries, Express middleware for automatic tracing, and cross-service trace correlation using OpenTelemetry. Ideal for designing enterprise-level, scalable tracing infrastructure for LLM applications.

Analyze mental health data to track mood, anxiety, depression scores, therapy progress, and crisis risk. Supports correlation analysis with sleep, exercise, nutrition, and chronic conditions to generate personalized insights and recommendations.

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 framework provides a systematic methodology for cybersecurity professionals to transform raw threat intelligence, observed attack patterns, and environmental telemetry into verifiable, testable hunting hypotheses. It guides the end-to-end process, from hypothesis formulation and data identification across SIEM/EDR systems, to deep correlation and reporting, enabling proactive threat detection and scope assessment.

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.

This skill provides comprehensive detection methods for credential dumping techniques, a critical post-exploitation phase in cyber attacks. It targets the extraction of sensitive credentials from LSASS memory, SAM registry hives, and NTDS.dit using advanced security visibility. Detection relies on analyzing Sysmon Event ID 10 (ProcessAccess), Windows Security logs, and complex SIEM correlation rules, making it essential for SOC analysts and threat hunters investigating advanced persistent threats (APTs).

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.

A comprehensive guide and set of detection rules for identifying OS credential dumping techniques (MITRE T1003). It leverages EDR telemetry, Sysmon process access monitoring, and Windows security event correlation to detect attacks targeting LSASS memory, SAM databases, and NTDS.dit files. Essential for proactive threat hunting and incident response.

This guide provides a structured methodology for proactively hunting adversaries who establish persistence using Windows Scheduled Tasks (T1053). It outlines the necessary prerequisites (EDR, SIEM, Sysmon) and a comprehensive workflow—from hypothesis formulation to threat correlation—for detecting suspicious task creation, unusual scheduling patterns, and attacker TTPs during incident response or red teaming.

This guide details how to implement advanced SIEM correlation rules to detect sophisticated Advanced Persistent Threats (APTs). By chaining multiple event types—including Windows authentication events, process execution telemetry, and network connection logs—across hosts, users can surface complex attack sequences that are invisible to single-event detections. It utilizes Splunk SPL and Sigma rule formats for robust security posture enhancement.

This guide outlines systematic methodologies for reducing false positives in Security Information and Event Management (SIEM) systems. It covers advanced techniques including threshold tuning, multi-signal correlation enhancement, behavioral baseline integration, and threat intelligence filtering. Used by SOC analysts and detection engineers to combat alert fatigue and improve true positive detection rates.