Skill UI

This tool assesses whether coding agents adhere to defined skills, rules, or agent definitions. It automatically generates scenarios with varying prompt strictness, executes the agent, and generates a comprehensive report detailing the compliance rate of tool calls against predefined workflow specifications using advanced LLM classification.

Transformers.js allows developers to run state-of-the-art machine learning models directly within JavaScript/TypeScript environments (browser, Node.js). It supports a comprehensive range of tasks including NLP (text generation, summarization, translation), Computer Vision (image classification, object detection), and Audio processing. This capability enables building fully client-side, multimodal AI applications without requiring a dedicated Python backend or server infrastructure.

A sophisticated skill for detecting Command and Control (C2) traffic tunneled through DNS protocols. It utilizes advanced techniques like Shannon entropy analysis of subdomains, Machine Learning for DGA classification, and correlation with network logs from Zeek/Suricata. Ideal for SOC analysts and threat hunters investigating data exfiltration, DNS beaconing, or suspicious DNS activity.

This skill provides comprehensive guardrails for handling Protected Health Information (PHI) in US healthcare software. It guides development teams on data classification, security best practices, access control, and audit logging to ensure strict adherence to HIPAA regulations. Use it when dealing with patient data storage, transmission, or processing to prevent breaches and maintain compliance.

This comprehensive guide provides a structured set of prompts to manage the entire lifecycle of upgrading a legacy .NET solution. It covers everything from initial project classification and dependency compatibility review to code modernization (e.g., async conversion), updating CI/CD pipelines, and defining robust testing and deployment strategies. Ideal for large-scale migration of older .NET applications to modern frameworks like .NET 8.

This skill guides users on setting up and running LLM-as-judge evaluations using Arize. It covers defining evaluators (including prompts, classification choices, and models) and executing tasks against real data spans or experiments. It supports key metrics like hallucination, faithfulness, and correctness, and facilitates continuous monitoring for comprehensive LLM performance analysis.

Aeon is a comprehensive, scikit-learn compatible Python toolkit designed for advanced time series machine learning. It supports a wide range of temporal tasks, including classification, regression, clustering, forecasting, anomaly detection, and pattern recognition. Ideal for analyzing sequential data and extracting features from time-indexed observations.

This skill provides comprehensive guidance for classical machine learning using scikit-learn, the industry-standard Python library. It covers the full ML lifecycle, including supervised tasks (classification, regression), unsupervised tasks (clustering, dimensionality reduction), data preprocessing, robust model evaluation, and building scalable, production-ready ML pipelines.

PyG is the standard, powerful library for building Graph Neural Networks (GNNs) atop PyTorch. It provides specialized data structures for nodes, edges, and graphs, supporting various tasks like node classification, graph classification, and link prediction. Users can implement advanced models using built-in layers (GCN, GAT, GraphSAGE) and handle complex structures like heterogeneous graphs efficiently. It is essential for deep learning research and industrial applications involving graph data.

The Hugging Face Transformers library is a comprehensive toolkit providing access to thousands of pre-trained models across diverse domains including Natural Language Processing (NLP), Computer Vision, Audio, and Multimodal tasks. It enables developers to easily load models, perform high-level inference (e.g., text generation, classification, QA), and fine-tune models on custom datasets using streamlined APIs like Pipelines and the Trainer class. Ideal for building advanced, state-of-the-art AI applications.

This skill provides comprehensive methods for interacting with the Figma API. It covers retrieving and managing comments, accessing version history, and handling sensitive user data. Emphasis is placed on implementing robust privacy controls, PII redaction, data classification, and ensuring compliance with regulations like GDPR/CCPA.

A comprehensive guide to production-ready patterns for integrating with Adobe services (Firefly, PDF, etc.). This includes robust authentication with token caching, typed API wrappers with advanced error classification, exponential backoff retry mechanisms, and job polling for asynchronous tasks. Essential for building reliable and maintainable Adobe API integrations in TypeScript or Python.