Skill UI

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 guide details a structured threat hunting methodology designed to proactively detect sophisticated spearphishing campaigns. It instructs users on correlating indicators across email logs, endpoint telemetry (EDR), and network data (SIEM) to identify targeted threats, supporting incident response and security posture improvement.

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.

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.

Plotly is a powerful Python graphing library designed to create highly interactive, publication-quality visualizations. It supports over 40 chart types, including scatter plots, heatmaps, candlestick charts, and geographical maps. Ideal for building sophisticated dashboards, conducting deep exploratory data analysis, and creating rich, web-embeddable presentations with features like zooming, panning, and hover tooltips.

This is an advanced, structured workflow designed to conduct deep, multi-source investigations. It synthesizes current public evidence with user-provided context to generate highly structured reports. The workflow explicitly differentiates between verifiable facts, user-supplied evidence, logical inferences, and actionable recommendations, making it ideal for complex comparative analysis or continuous monitoring tasks.

An expert agent designed to collect critical data assets—including metadata, data lineage, and query logs—from any data warehouse platform. It uses the push ingestion API to send this data directly to Monte Carlo. This method is essential for integrating data sources that cannot be reliably covered by standard pull collectors, such as custom lineage paths or historical query logs.

This skill automatically generates an interactive SQL Notebook designed to validate changes in dbt models or snapshots. It processes changes identified from a GitHub PR or local repository, generating comparison queries that compare 'before' (production) and 'after' (development) data states. It is essential for embedding data observability and quality checks into CI/CD pipelines.

A comprehensive expert prompt for evaluating Power BI data models. It guides the assessment of model architecture (Star Schema, relationships), storage optimization (Import, DirectQuery, Dual), data quality, performance scalability, and governance best practices. Use this when reviewing an existing BI model to ensure it is robust, maintainable, and highly performant.

Provides systematic Power BI performance troubleshooting guidance covering model optimization, DAX design, report layout, and capacity diagnostics, plus tool-driven measurement tips for faster page loads, interactions, and query execution.

Guides Power BI visualization and UX decisions—capturing business context, selecting optimal charts, specifying layouts, color, typography, and interaction patterns to create accessible, insight-driven dashboards across audiences.

A persistent, structured knowledge base designed to accumulate and connect all research artifacts—including papers, ideas, experiments, and claims—across the entire research lifecycle. Inspired by LLM Wiki patterns, it builds a comprehensive, evolving graph of knowledge by tracking typed relationships (e.g., supports, contradicts, extends), ensuring that findings are compounded and never re-derived. Ideal for complex, multi-stage scientific projects.