Skill UI

Auto-activating data analytics skill that guides anomaly detection tasks, covering SQL, visualization, statistical analysis, and business intelligence best practices.

This comprehensive skill simulates an expert business analyst, specializing in transforming complex raw data into actionable, strategic insights. The scope covers modern Business Intelligence (BI) platforms (Tableau, Power BI), AI-powered predictive modeling, designing robust KPI/OKR frameworks, advanced financial modeling, and optimizing business processes. It is designed for users needing to drive data-driven decision-making and create executive-level reports.

This skill models the expertise of a senior data scientist, covering the entire data science lifecycle. It provides advanced capabilities in statistical modeling, machine learning (deep learning, XGBoost), predictive analytics, and business intelligence. From raw data exploration (EDA) to deploying production-ready models and creating actionable dashboards, it covers technical depth across Python, R, SQL, and cloud platforms, while applying domain knowledge in finance, marketing, and operations.

This advanced workflow provides comprehensive meeting intelligence by allowing users to search across stored transcripts using keywords and date filters. It integrates the AskFred AI assistant for natural language Q&A over single or cross-meeting data. Furthermore, it aggregates detailed meeting analytics, providing key metrics such as total duration, action items, and top keywords for robust reporting and knowledge retrieval.

This advanced workflow enables users to perform deep analyses on large-scale people intelligence datasets. Users can build custom queries with multi-dimensional filters, run comparisons across different candidate pools (e.g., Q1 vs Q4), and aggregate skill density by geographical region. It is essential for talent acquisition teams needing to identify market trends and optimize hiring strategies beyond standard search functions.

This module provides minimal working examples for the OpenEvidence API, designed for clinical intelligence applications. Users can perform complex clinical queries to get treatment recommendations, check potential drug interactions based on patient context, and search for authoritative medical guidelines from major health organizations (e.g., ACC/AHA). Ideal for developing evidence-based healthcare decision support systems.

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 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 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 skill provides a systematic methodology for mapping real-world threat actor Tactics, Techniques, and Procedures (TTPs) to the globally recognized MITRE ATT&CK framework. Users learn how to leverage tools like ATT&CK Navigator and Python libraries (e.g., attackcti, stix2) to analyze security incidents, visualize technical coverage heatmaps, identify critical detection gaps, and generate actionable intelligence reports linking observed Indicators of Compromise (IOCs) to specific adversary techniques across various platforms (Enterprise, Mobile, ICS).

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 skill automates the process of parsing Cyber Threat Intelligence (CTI) reports to extract adversary behaviors. It maps these behaviors to MITRE ATT&CK techniques and structures them as STIX 2.1 Attack Pattern objects. The resulting library supports detection engineering by generating rule templates (Sigma, YARA) for proactive defense and threat-informed security control implementation.