975 boosters — open source, verified from GitHub, ready to install
Train object detection, image classification, and SAM/SAM2 segmentation models on managed cloud GPUs. No local GPU setup required—results are automatically saved to the Hugging Face Hub. Use this skill when users want to: Helper scripts use PEP 723 inline dependencies. Run them with :
Provides the Hugging Face Hub CLI (`hf`) tool for downloading, uploading, and managing models, datasets, and Spaces directly from Claude Code. Essential for developers integrating Hugging Face resources into AI workflows.
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:
A skill that generates reusable command-line scripts for automating Hugging Face API interactions, useful for developers who need to repeatedly fetch, process, or chain API calls.
A skill for querying and exploring Hugging Face datasets through the Dataset Viewer API, enabling developers to fetch metadata, paginate rows, search, filter, and download parquet files. Useful for data scientists and engineers working with public datasets in their AI/ML workflows.
A skill that enables researchers and AI engineers to publish, manage, and link research papers on Hugging Face Hub with arXiv integration and professional markdown generation. Useful for academics and ML practitioners looking to streamline paper publication workflows.
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.
This skill enables AI assistants to create, configure, and manage datasets on Hugging Face Hub with SQL-based querying and transformation capabilities. It's valuable for developers building data workflows and ML projects that require programmatic dataset management.
This skill automates the process of adding, extracting, and managing evaluation results in Hugging Face model cards, supporting multiple data sources including Artificial Analysis API and custom evaluations with vLLM/lighteval. It's valuable for ML practitioners and model maintainers who need to track and display model performance metrics.
Trackio is an ML experiment tracking library that integrates with Hugging Face to log metrics, visualize training progress, and trigger alerts during model development. It's useful for ML engineers and researchers who need real-time monitoring and experiment management.
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.
A skill booster for building interactive web UIs and ML demos using Gradio in Python. Developers creating data applications, ML interfaces, and chatbots benefit from guided assistance with Gradio components and patterns.
Enables developers to interact with Hugging Face Hub directly from Claude Code using the `hf` CLI—downloading models/datasets, uploading files, creating repositories, and managing cache without leaving the coding environment.
Generates per-category catalog JSON files from the npm package's public API. Each catalog lists all UseCase and EventHandler abstractions in a category with their resolved source file paths. LLMs read source files on demand for exact, up-to-date types — no enrichment phase needed. 1. Discovers all
A CLI tool for managing and installing reusable skills across multiple AI coding agents (Claude Code, Cursor, etc.), enabling developers to extend agent capabilities with pre-built tools and workflows from repositories like GitHub and GitLab.
Automates GitHub pull request analysis by gathering diffs, comments, related issues, and local code context to provide comprehensive reviews. Developers and code reviewers benefit from faster, more thorough PR evaluations.
Do NOT check or review pull requests. Do NOT call commands. Run CodeRabbit locally against the working repository only. From the output, extract for each finding:
当用户对已有前任 Skill 说以下内容时，进入进化模式： 本 Skill 运行在 Claude Code 环境，使用以下工具： 本 Skill 在生成和运行过程中严格遵守以下规则：
If a concern is not "X calls a lock/alloc/ObjC from a signal-reachable path" or "a comment misstates signal safety", it does not belong in the output. When in doubt, cut it. The authoritative rules live in . Read that file before every review — do not summarize or duplicate the rules here; they may
Use to stream-read from archive:
A physics tutoring booster that adapts explanations from intuitive reasoning to formal derivations based on learner level. Ideal for students, educators, and researchers seeking physics help across all difficulty tiers.
Keep your agent running by automatically managing wallet balances, topping up via MoonPay, and paying for x402-enabled APIs. 1. MoonPay CLI installed and authenticated: 2. A funded wallet (can be created via )
Filter by smart money category with :
A CLI tool for interacting with the Mutinynet Bitcoin testnet faucet. Download a prebuilt binary from GitHub Releases, or install from source: Authenticate with GitHub via device flow. Required before using commands that need a token.
