Skill UI

This skill provides comprehensive functionality for querying, comparing, and calculating nutritional data from a structured food database. Users can perform precise searches by food name or nutritional criteria (e.g., high protein, low GI). It supports calculating total nutrient intake based on specific portions, suggesting foods for various health conditions (like diabetes or high blood pressure), and performing detailed comparative analysis between multiple food items.

Literature Review Tool helps researchers gather and synthesize key insights from scholarly texts, highlighting patterns and critical arguments to accelerate literature surveying workflows even when structured content is initially unavailable.

Pandas is a Python data analysis library that simplifies loading, cleaning, transforming, and analyzing structured datasets, making it ideal for data wrangling in analytics or engineering workflows.

PyMC is a powerful Python library for probabilistic programming and advanced Bayesian inference. This comprehensive guide details the full workflow for building, fitting, and validating complex Bayesian models. Learn to implement hierarchical structures, perform MCMC sampling (NUTS), conduct posterior predictive checks (PPC), and effectively quantify uncertainty using industry best practices.

This tool is designed to analyze complex Machine Learning experiment results from various sources (JSON/CSV). It computes detailed statistics, builds comprehensive comparison tables across hyperparameter variations, and generates structured, actionable insights. It helps researchers interpret findings, identify trends, and propose 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 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.

This skill provides a systematic methodology for building comprehensive threat actor profiles using Open-Source Intelligence (OSINT). It involves gathering, correlating, and analyzing publicly available data from diverse sources (e.g., vendor reports, dark web forums, code repositories). Users can map adversary TTPs to MITRE ATT&CK, assess motivations, and build structured dossiers for proactive defensive strategies and attribution assessments.

This skill provides structured procedures for detecting Golden Ticket attacks, a severe threat involving forging Kerberos Tickets in Active Directory. It analyzes Windows Event ID 4769 for indicators such as RC4 encryption downgrades, abnormal ticket lifetimes, and inconsistencies in TGS/TGT requests. Designed for SOC analysts and threat hunters using SIEM platforms (Splunk/Elastic) to enhance security monitoring coverage against credential theft.

This skill provides structured procedures and Python examples for detecting internal data exfiltration. It analyzes endpoint, cloud, and email logs by establishing behavioral baselines and performing statistical anomaly detection. Use cases include investigating insider threats, monitoring DLP policy violations, and building robust User and Entity Behavior Analytics (UEBA) rules for SOC teams.

This skill guides the creation of high-quality, finished cyber threat intelligence reports. It tails content for specific audiences—from C-suite executives (strategic risk) to SOC analysts (tactical IOCs). Use it when synthesizing raw data into actionable intelligence products, generating threat briefings, or conducting post-incident assessments, ensuring adherence to industry standards like TLP and NIST.