Skill UI

This skill fetches the latest, live documentation and code examples for specific libraries and frameworks (e.g., Next.js, React, Prisma). Instead of relying on static or outdated training data, it utilizes the Context7 MCP to provide accurate, up-to-date answers for setup questions, API references, and code generation based on current best practices.

Interactive training for the GitHub Copilot CLI with guided lessons, quizzes, scenario challenges, and a reference covering slash commands, shortcuts, agents, MCP, and configuration; say “cliexpert” to begin.

PennyLane is a quantum computing library designed for building and training quantum circuits, particularly for quantum machine learning (QML) and quantum chemistry. It offers automatic differentiation capabilities for quantum circuits, making it ideal for developing hybrid quantum-classical models. It is hardware-agnostic, supporting backends from major providers like IBM, Google, and IonQ, and integrates seamlessly with classical ML frameworks such as PyTorch and JAX. Use cases include variational algorithms (VQE, QAOA), quantum neural networks, and molecular simulation.

Guide to building and troubleshooting TorchDrug 0.2.1 workflows for molecular graphs, property prediction, pretraining, generation, retrosynthesis, protein learning, and knowledge graph reasoning.

This guide provides expert strategies for optimizing GPU inference performance on CoreWeave infrastructure. It covers GPU selection based on workload (LLM, image generation, training), advanced techniques like continuous batching (using vLLM), autoscaling setup (HPA), and benchmarking data. Use this to maximize GPU utilization, minimize latency, and optimize large-scale AI model deployment.

A comprehensive checklist for ensuring GPU workloads, such as inference services or model training pipelines, are fully prepared for production deployment on CoreWeave. It covers critical MLOps and DevOps aspects including autoscaling, resource limits, security policies, persistent storage validation, monitoring setup, and rollback procedures.

This guide provides comprehensive, step-by-step instructions for installing and securely configuring the MindTickle SDK and API credentials. It covers prerequisites, environment variable setup, and includes verification snippets for both Node.js/TypeScript and Python environments. Use this when initiating a new MindTickle system integration or developing an application that requires API access to sales, training, or performance data.

This tool provides a robust local development workflow (dev loop) for integrating with the MindTickle sales enablement and readiness API. It simulates critical data points—such as training modules, user progress, and coaching data—using mock services. This allows developers to build and test complex sales readiness dashboards and features rapidly and reliably, by toggling between a mock mode for fast iteration and a sandbox mode for real-world validation against the MindTickle platform.

This comprehensive curriculum provides a detailed roadmap for mastering exploit development and vulnerability research. It covers fundamental techniques like fuzzy testing using AFL++, and progresses through advanced topics such as in-process fuzzing with Google FuzzTest. It is ideal for structuring professional security training programs or onboarding new research engineers.

A comprehensive workflow guide for fine-tuning open-source Large Language Models (LLMs) using the Together AI API. This process covers preparing structured training data (JSONL format), uploading the file, initiating the fine-tuning job, monitoring the training status, and finally deploying the custom model for inference. Ideal for developers needing to customize models for specific enterprise tasks, such as customer support or specialized content generation.

A comprehensive presentation template designed for educational workshops and training sessions. It features a warm paper aesthetic, persistent learning objectives on the left sidebar, core content slides, interactive Multiple Choice Question (MCQ) self-assessment pages, and module wrap-ups, ensuring a structured and engaging learning experience.

This skill fetches the latest, live documentation and code examples for specific libraries and frameworks (e.g., Next.js, React, Prisma). Instead of relying on static or outdated training data, it utilizes the Context7 MCP to provide accurate, up-to-date answers for setup questions, API references, and code generation based on current best practices.