Skill UI

PufferLib is a high-performance framework optimized for speed and scale in Reinforcement Learning. It achieves millions of steps per second through advanced vectorization, native multi-agent support, and optimized PPO implementation. Use this library for training complex RL agents, developing custom environments (PufferEnv), or scaling experiments across massive parallel simulated environments.

PyTorch Lightning is an advanced deep learning framework built on PyTorch. It simplifies the entire ML workflow, allowing users to organize models into LightningModules, automate training loops using the Trainer, and manage complex data pipelines with DataModules. It supports advanced features like multi-GPU/TPU scaling (DDP, FSDP), integrated logging (W&B, MLflow), and custom callbacks, making large-scale neural network development efficient and reproducible.

Stable Baselines3 is a PyTorch-based library providing production-ready implementations of core reinforcement learning algorithms, including PPO, SAC, DQN, and TD3. It offers a scikit-learn-like API for easily training RL agents, developing custom Gymnasium environments, and managing complex training workflows. Ideal for standard RL research, rapid prototyping, and advanced model optimization.

This skill acts as a comprehensive Music Learning Coach, guiding users through the full spectrum of music education. It covers fundamental music theory (scales, harmony), structured instrument practice routines, advanced ear training techniques, performance skills, and deep dives into music history. Ideal for beginners and advanced learners looking to build a solid, well-rounded understanding and practical ability in music.

This guide details how to set up and execute large-scale, distributed GPU training jobs on the CoreWeave platform. It covers both single-node multi-GPU setups and multi-node training utilizing PyTorch DDP and Kubernetes resources (A100/H100). This workflow is ideal for fine-tuning large language models (LLMs) or running complex deep learning models that require high-performance, shared-storage compute clusters.

This guide provides a deep dive into migrating machine learning workloads, including inference services and training pipelines, from major hyperscalers (AWS, GCP, Azure) to the CoreWeave GPU cloud. It covers cost comparisons, containerization, Kubernetes YAML adaptation, and a phased deployment strategy, helping users ensure a smooth, cost-effective, and optimized transition.

Use the `idc-index` library to query and download vast public cancer imaging datasets from the National Cancer Institute Imaging Data Commons (IDC). This skill allows users to filter metadata, visualize images, and download DICOM files for large-scale AI model training and deep medical research. No authentication is required for data access.

This comprehensive workflow manages the end-to-end life cycle of sales readiness training on the MindTickle platform. Users can create complex courses containing mixed media (videos, documents, quizzes). It allows assigning courses to teams with due dates, tracking learner progress and quiz scores via analytics API, and updating content modules as business needs evolve.

This guide provides a minimal working example demonstrating core MindTickle API functionality. It shows how to programmatically create structured training modules (including videos and quizzes), assign them to specific teams or users with deadlines, and track user progress and overall completion rates using analytics endpoints. It is ideal for integrating Learning Management System (LMS) functionality into internal corporate tools and automating onboarding processes.

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.

qzcli is a powerful command-line interface (CLI) tool designed for managing complex GPU compute jobs on the Qizhi platform. Modeled after industry standards like kubectl, it allows users to log in, discover available resources (nodes, workspaces), submit interactive or highly complex batch jobs (including matrix sweeps), and monitor job status efficiently. It is essential for handling high-performance computing (HPC) and distributed machine learning training workflows.