Skill UI

This skill allows users to build comprehensive attack trees to systematically visualize complex threat paths. It is essential for mapping potential attack scenarios, identifying critical defense gaps, prioritizing security investments, and communicating detailed security risks to non-technical stakeholders.

This skill allows users to build comprehensive attack trees to systematically visualize complex threat paths. It is essential for mapping potential attack scenarios, identifying critical defense gaps, prioritizing security investments, and communicating detailed security risks to non-technical stakeholders.

This skill provides comprehensive guidance on leveraging d3.js (Data-Driven Documents) to create sophisticated, interactive, and publication-quality data visualizations. It covers core concepts like data binding, implementing various chart types (network graphs, geo-maps, etc.), handling complex transitions, and ensuring responsiveness across modern JavaScript frameworks (React, Vue, Vanilla JS). Learn the standard workflow for scale definition, axis creation, and drawing custom visual elements with fine-grained control.

Automates common CTE query builder tasks for data analytics, offering best-practice guidance, production-ready SQL snippets, and validation against standard requirements when you need help with BI, visualization, or statistical analysis.

This skill provides a comprehensive framework for monitoring Mistral AI API usage in production. It includes an instrumented client wrapper (TypeScript) to automatically capture critical metrics like latency, token consumption, cost, and error rates. Furthermore, it integrates with Prometheus for metric storage and Grafana for visualization, allowing users to set up sophisticated alerting rules for performance degradation, rate limiting, or cost spikes.

Automates guidance for statistical significance calculations in data analytics, offering triggered best practices, example implementations in SQL, visualization, and BI workflows, plus validation against common standards.

TensorBoard delivers a unified interface to track training metrics, debug network behavior, compare experiments, inspect model graphs, and profile performance for PyTorch or TensorFlow workflows.

Assists with tensorboard visualizer tasks for ML training by guiding setup, best practices, and experiment tracking whenever tensorboard visualization is mentioned, producing production-ready configs and validated outputs.

Defines a Markdown/Mermaid-first documentation skill for scientific and technical writing, providing diagram standards, 24 reference types, and templates so every report, README, or analysis stays text-first, versionable, and visualization-ready.

Guides using matplotlib to craft static, animated, and interactive plots through both pyplot and object-oriented APIs, detailing styling, subplots, and export options for publication-ready scientific workflows.

End-to-end Neuropixels recording analysis covering SpikeGLX/OpenEphys/NWB loading, preprocessing, motion correction, Kilosort4 sorting, quality metrics, Allen/IBL curation, and AI-assisted visualization for extracellular electrophysiology.

QuTiP is a Python toolbox for simulating open and closed quantum systems, offering solvers for master equations, trajectory methods, visualization, and analysis tailored to quantum research, education, and exploratory physics studies.