14 boosters for "uv" — open source, verified from GitHub, ready to install
Run any workload on fully managed Hugging Face infrastructure. No local setup required—jobs run on cloud CPUs, GPUs, or TPUs and can persist results to the Hugging Face Hub. Use this skill when users want to: When assisting with jobs:
Train language models using TRL (Transformer Reinforcement Learning) on fully managed Hugging Face infrastructure. No local GPU setup required—models train on cloud GPUs and results are automatically saved to the Hugging Face Hub. Use this skill when users want to: Use Unsloth () instead of standard
A skill for fine-tuning and training language models on Hugging Face's cloud GPU infrastructure using TRL, supporting SFT, DPO, GRPO methods and GGUF conversion for local deployment. Developers and ML engineers working with cloud-based model training benefit from this comprehensive guidance.
This skill enables users to run Python workloads, Docker jobs, and GPU-intensive tasks on Hugging Face's managed infrastructure without local setup. It's valuable for ML engineers, data scientists, and developers needing cloud compute for training, inference, and batch processing.
"name": "uuv-e2e-accessibility-test", "description": "Integrates the Unified UUV Testing Framework into Claude code to provide BDD (Behavior-Driven Development) assistance for your projects based on accessibility datas.", "name": "Louis Fredice NJAKO MOLOM (https://github.com/luifr10) & Stanley SERV
This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, and model persistence. Should be invoked for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
OpenDarts is a self-hosted dart application with computer vision-based auto-scoring, enabling players to track games and practice with automated score detection via their phone camera. It benefits dart enthusiasts and competitive players who want accurate scoring and game management without manual entry.
Heuristic scoring (no AI key configured).
Claude Flow is an enterprise-grade MCP server for orchestrating multi-agent AI workflows with swarm coordination and automation capabilities. It benefits developers building complex, distributed AI systems that require sophisticated agent coordination and workflow management.
UV Skill enables fast Python package management, project initialization, and tool installation within Claude Code. Developers working with Python projects benefit from streamlined dependency and version management.
An MCP server that integrates European Vulnerability Database (EUVD) and ENISA security data into Claude, enabling developers and security professionals to query vulnerability information and security intelligence directly within their workflow.
