Skill UI

Generates Python code and workflows for collaborative, content-based, or hybrid recommendation models, handling data prep, training, evaluation, and scalability so Claude can deliver tailored suggestions across domains.

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.

A comprehensive workflow for executing advanced, semantic web searches using Exa. This tool moves beyond simple keyword matching, enabling deep context retrieval by understanding the intent behind the query. Key features include RAG context retrieval, content extraction (summaries, highlights, full text), and robust filtering by date range, specific domains, and content categories. Ideal for building knowledge base search features and complex research pipelines.

This skill functions as an expert-level web researcher, utilizing advanced search strategies (query optimization, domain filtering) to gather, synthesize, and validate information from multiple web sources. It is ideal for complex research tasks requiring fact verification, identifying trends, and producing structured, actionable insights, ensuring high credibility for the final output.

Conduct rigorous, multi-database literature reviews with systematic workflows, verified citations in styles like APA or Nature, and AI-enhanced schematics for publication-ready markdown and PDF outputs in biomedical and technical domains.

Query InterPro for protein family, domain, and GO annotations via a REST API with Python helpers, enabling function prediction, domain architecture mapping, and evolutionary classification insights for proteomic workflows.

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 skill provides a systematic method to map observed Indicators of Compromise (IOCs) and threat actor TTPs to the global MITRE ATT&CK framework. It guides users in building technique coverage heatmaps using the ATT&CK Navigator, identifying critical detection gaps, and generating actionable threat intelligence reports across Enterprise, Mobile, and ICS domains. Ideal for SOC analysts, threat hunters, and security researchers seeking structured threat analysis.

This skill provides methods for detecting covert data exfiltration channels using DNS tunneling. It analyzes DNS query patterns, including subdomain entropy, query volume anomalies, oversized TXT/CNAME records, and unusual query types, to identify potential data leakage attempts from compromised networks. Ideal for threat hunting and building advanced passive DNS monitoring capabilities.

This skill detects DNS-based data exfiltration by analyzing Zeek dns.log files. It identifies indicators such as high Shannon entropy subdomains, oversized TXT/NULL records, and abnormal query volumes. Use it when investigating suspected DNS tunneling or covert C2 channels to enhance threat detection capabilities.

This skill analyzes proxy access logs, DNS query logs, and network flow data to identify unauthorized SaaS and cloud services (Shadow IT). It uses Python and pandas to aggregate traffic, classify domains against known SaaS categories, and calculate risk scores based on data volume and user count. Ideal for conducting thorough security audits and generating actionable remediation reports.