Skill UI

A comprehensive, all-in-one skill for conducting deep SEO audits across any industry (E-commerce, SaaS, Local). It orchestrates multiple sub-agents to analyze technical SEO, content quality (E-E-A-T), schema markup, Core Web Vitals, local presence, and AI readiness (AI Overviews). It culminates in a synthesized, actionable strategy report.

This skill initializes an autonomous, scheduled loop for running complex research or engineering experiments. Users specify the target experiment and select a recurring interval (e.g., 10 minutes, daily, weekly). It utilizes cron scheduling to ensure continuous research, guiding iterative changes, evaluating metrics, and committing results automatically until manually stopped.

This tool analyzes ML experiment results from various paths (JSON/CSV). It computes comprehensive statistics, builds detailed comparison tables based on model types and hyperparameters, and generates actionable insights. Use it when you need to interpret raw experimental data, compare different model runs, or determine the next steps for research.

This skill provides a systematic framework for analyzing cyber campaign attribution. It involves collecting, weighting, and correlating diverse indicators—such as infrastructure overlaps, TTP consistency, malware code similarities, and operational patterns—to determine the responsible threat actor. Utilizing structured methodologies like Analysis of Competing Hypotheses (ACH) and the Diamond Model, users can build confidence-weighted assessments to enhance threat intelligence accuracy.

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.

A specialized tool for digital forensics investigators that parses the Windows Amcache.hve registry hive. It extracts crucial evidence regarding program execution history, application installation metadata, and loaded driver binaries. By correlating SHA-1 hashes and reconstructing timelines, users can determine which executables ran on a system, aiding in incident response and threat detection.

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.