Skill UI

Enables Claude to apply anomaly-detection-system workflows for spotting outliers, fraud, or unusual patterns across datasets, selecting tailored algorithms like Isolation Forest and summarizing deviations for any data monitoring or security use case.

Detects abnormal access patterns across major cloud storage platforms, including AWS S3, GCS, and Azure Blob Storage. By analyzing cloud audit logs (CloudTrail, etc.), this tool identifies critical security threats such as after-hours bulk data downloads, sudden spikes in API calls (GetObject spikes), access from new geographic IPs, and potential data exfiltration. It utilizes statistical baselines and time-series anomaly detection for proactive threat hunting and security incident investigation.

This skill analyzes DNS query logs to detect sophisticated threats such as data exfiltration via DNS tunneling, Domain Generation Algorithm (DGA) usage, and covert Command and Control (C2) channels. It utilizes advanced techniques like entropy analysis, query volume anomaly detection, and subdomain length analysis within SIEM platforms, helping SOC teams identify threats that bypass traditional network security controls.

This skill analyzes Indicators of Compromise (IOCs)—such as IP addresses, domains, and file hashes—by querying multiple threat intelligence platforms (VirusTotal, AbuseIPDB, MISP). It determines maliciousness confidence, assigns blocking priorities, and attributes threats to campaigns, crucial for incident response, threat triage, and making informed security decisions.

This tool provides comprehensive analysis of the cybersecurity threat landscape using the Malware Information Sharing Platform (MISP). It allows users to query event statistics, analyze attribute distributions (IPs, domains, hashes), identify top threat actors and malware families, and track temporal trends. It is essential for SOC analysts performing incident response, threat hunting, and building advanced detection rules.

This skill provides a method to parse Windows Prefetch files (.pf) using Python, allowing security analysts to reconstruct detailed application execution histories. It is crucial for incident response, enabling the detection of renamed binaries, identifying suspicious execution patterns, and building accurate chronological timelines of system activity for deep forensic investigation.

This system automates the tracking and mapping of threat actor Command-and-Control (C2) networks. By leveraging passive DNS records, WHOIS data, certificate transparency logs, and IP enrichment, it helps security analysts perform infrastructure pivoting. It identifies shared hosting patterns, monitors domain reuse, and continuously maps adversary relationships for proactive threat intelligence.

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.

Timesketch is a collaborative, open-source platform for security teams to analyze and reconstruct complex forensic timelines. It ingests and normalizes multi-source security data (logs, artifacts) using methods like Plaso, CSV, and JSONL. Users can visualize event chronology, track attack chains, and utilize advanced built-in analyzers (e.g., Sigma, Chain of Events) for comprehensive incident investigation and documentation.

This skill correlates disparate security incidents, Indicators of Compromise (IOCs), and adversary behaviors across time and organizations. It systematically identifies unified threat campaigns, attributes them to common threat actors, and extracts shared indicators for significantly improved detection capabilities. Ideal for cross-organizational threat intelligence sharing and building comprehensive campaign reports.

This skill provides a comprehensive framework for detecting data exfiltration attempts that utilize DNS tunneling. It analyzes DNS logs for anomalies such as high Shannon entropy in subdomains, excessive query volume, abnormal query lengths, and misuse of TXT/CNAME records. It is ideal for SOC analysts and security engineers building advanced threat detection rules and performing threat hunting.

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.