Skill UI

Guides GTM measurement for AI products by outlining revenue, efficiency, pipeline, retention, and funnel metrics, aligning dashboards with attribution models and weekly reviews so founders and revenue leaders monitor ARR, CAC, TTFV, NRR, and cost signals without implementation detail.

Builds AI-powered paid media creative across Meta, Google, TikTok, YouTube, and LinkedIn by combining native platform tools and generative models to automate asset creation, fatigue management, and performance testing while driving ROAS.

A comprehensive strategy for regression testing in AI-assisted development. It focuses on identifying systematic blind spots, such as schema inconsistencies between sandbox and production paths, and integrating automated bug checks into the development workflow. This guide teaches how to build robust, database-free API tests to ensure code quality when AI models contribute to the codebase.

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.

This skill provides a comprehensive guide to using the Malpedia API for advanced malware intelligence analysis. Users can track malware family relationships, analyze variant evolution, extract associated YARA rules for detection, and map connections to specific threat actors. It is essential for security researchers and SOC analysts building robust threat models.

This tool assists in defining a structured Security Operations Center (SOC) escalation matrix. It maps incident response procedures based on severity (P1-P4), asset criticality, and business impact. Users can model tiered response paths, set Service Level Agreements (SLAs), and implement context-driven automatic escalation triggers to ensure rapid and consistent handling of cybersecurity incidents.

A comprehensive guide covering methodologies for conducting authorized penetration testing across major cloud platforms, including AWS, Azure, and GCP. It details the shared responsibility model, techniques for exploiting IAM misconfigurations, testing for SSRF vulnerabilities to cloud metadata services, and utilizing industry tools like Pacu and ScoutSuite. Findings are mapped to the MITRE ATT&CK Cloud matrix for robust reporting.

This guide details the implementation of Microsoft's Enhanced Security Admin Environment (ESAE) tiered model for Active Directory. It covers best practices for establishing Tier 0/1/2 separation, deploying Privileged Access Workstations (PAWs), and designing robust access control policies. Ideal for security architects and compliance officers seeking to meet stringent standards like NIST 800-53.

Utilizes User and Entity Behavior Analytics (UEBA), statistical baselines, and machine learning models to monitor authentication logs. This skill identifies sophisticated security threats such as impossible travel, credential stuffing, brute force attacks, and compromised accounts in real-time. Essential for security operations and identity access management.

This skill provides comprehensive anomaly detection for Modbus/TCP and Modbus RTU traffic in Operational Technology (OT) and Industrial Control Systems (ICS). It monitors function codes, validates register access, analyzes timing, and detects unauthorized clients. By utilizing advanced tools like Zeek, Suricata, and custom Python models, it helps establish behavioral baselines and identify potential cyber threats.

Structures completed intrusion investigations with the Diamond Model by documenting adversaries, capabilities, infrastructure, and victims plus their relationships, metadata, and pivots to cluster incidents and support campaign attribution, confidence scoring, and analytic write-ups.