Skill UI

This skill detects covert data exfiltration occurring over DNS tunnels. It analyzes DNS query names by computing Shannon entropy, examining query length distributions, and identifying high subdomain cardinality. It is essential for security operations, incident response, and proactive threat hunting when suspicious or encoded DNS activity is suspected.

This toolset guides users through advanced digital forensic techniques using tools like Foremost and Scalpel. It demonstrates how to perform file carving—recovering specific file types (images, documents, etc.) from raw disk images or unallocated space—by analyzing file headers and footers (magic bytes). This is critical for evidence extraction when traditional file system metadata is unavailable or corrupted.

This skill provides a systematic framework for managing Indicators of Compromise (IOCs) throughout their entire life cycle—from initial discovery and validation to enrichment, deployment, monitoring, and eventual retirement. It emphasizes implementing confidence decay functions, tracking hit rates, and managing false positives to maintain a high-quality, actionable threat intelligence database, thereby minimizing analyst fatigue and maximizing detection efficacy.

This skill automates deep packet capture (PCAP) analysis using tshark and pyshark. It is invaluable for security assessments and incident response, enabling the extraction of protocol statistics, identification of suspicious network flows (e.g., port scanning, beaconing), and comprehensive extraction of Indicators of Compromise (IOCs), including IPs, domains, and URLs. It also specializes in detecting complex anomalies like DNS tunneling.

This tool conducts comprehensive, sector-specific threat landscape assessments by analyzing designated threat actor patterns (e.g., APT groups, cybercriminals), common attack vectors, and industry-specific vulnerabilities. It is designed to provide actionable intelligence for risk management, security investment prioritization, and executive reporting across critical sectors like finance, healthcare, and energy.

This skill performs User and Entity Behavior Analytics (UEBA) to identify anomalous user activities such as impossible travel, unusual access patterns, and privilege abuse. It establishes behavioral baselines using SIEM logs and statistical analysis, helping SOC teams detect insider threats and compromised accounts by flagging deviations from normal user behavior. Use when monitoring security hygiene and suspicious activity is critical.

bdistill transforms AI interactions into a structured, queryable knowledge base. It extracts, structures, and quality-scores domain knowledge from both closed-source AI models (in-session) and local open-source models (via Ollama). Users can build comprehensive reference datasets, Q&A pairs, or generate structured training data for traditional machine learning, ensuring validated and exportable domain expertise.

Comprehensive Snowflake development assistant covering SQL best practices, dynamic tables, streams, tasks, Snowpipe, Cortex AI functions/agents, Snowpark Python, dbt integration, performance tuning, and security hardening for production data platforms.

This skill provides institutional-depth equity analysis by combining public SEC EDGAR filings and real-time market data. It can generate structured, verdict-style memos, score stocks across four key pillars (Momentum, Stability, Financial Health, Upside), and perform side-by-side comparisons of two tickers, enabling data-driven investment decision support without requiring proprietary terminal access.

A comprehensive tool for generating publication-quality figures essential for Machine Learning and AI academic papers. It supports two distinct workflows: generating complex system architecture and workflow diagrams using AI (Gemini), and creating precise, data-driven visualizations (line charts, heatmaps, etc.) using standard libraries like matplotlib and seaborn. Use it when preparing any figure for a conference submission.

A sophisticated, multi-stage workflow designed to generate publication-quality technical illustrations, such as architecture diagrams and complex method figures, for academic papers. It utilizes a strict, iterative loop supervised by an advanced AI (Claude) to refine layout, color compliance, and structural correctness, ensuring high fidelity and professional standards across multiple rendering passes.

This skill provides interactive, periodic monitoring of deep learning training runs. It continuously checks critical metrics—such as loss trends, evaluation scores, and gradient norms—against defined baselines to detect signs of divergence, NaN values, plateaus, or general instability. It delivers a structured report and recommends whether to continue, wait, or stop the training process, ensuring computational resources are not wasted on failing models.