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Explore Hugging Face datasets through the Dataset Viewer API. Preview and retrieve rows, search text, filter records, inspect sizes and column statistics, and find Parquet download links for further analysis or data extraction.

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• Preview dataset contents and data splits
• Search text and filter dataset rows
• Inspect dataset sizes, statistics, and Parquet links

Register arXiv papers on Hugging Face Hub, connect them to models and datasets, and manage authorship claims and profile visibility. Create research articles from Markdown templates and convert them into HTML for sharing.

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• Registering an arXiv paper on Hugging Face Hub
• Linking papers to models and datasets
• Managing authorship claims and profile visibility

Build reusable command-line scripts that fetch and process Hugging Face API data. Combine multiple requests, enrich model information, and produce outputs for further filtering, with built-in help and usage examples for repeated tasks.

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• Automating repeated model and dataset lookups
• Combining trending models with metadata and paper links
• Extracting licenses and tags from model cards

Search Hugging Face Hub for models, datasets, papers, and AI apps. Inspect documentation, use hosted apps for image generation or transcription, and run or schedule code on cloud CPUs and GPUs through an MCP server connection.

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• Compare models for a specific task
• Find training datasets
• Generate images or transcribe audio with Spaces

Build and publish a browser demo for a supplied LoRA, a model adaptation for image or video tasks. Tailor the Gradio interface to its inputs and follow the model’s recommended settings on Hugging Face Spaces.

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• Share a trained LoRA through a browser demo
• Build an interface tailored to image editing or video generation
• Apply recommended trigger words and generation settings

Read and summarize AI research papers from Hugging Face or arXiv links and identifiers. Retrieve paper content alongside structured details about authors, related models, datasets, Spaces, and implementation links to support further investigation.

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• Summarizing a paper from its URL or arXiv ID
• Finding models, datasets, and Spaces linked to a paper
• Checking authors and implementation links

Build JavaScript or TypeScript applications that run text, image, and audio models in browsers or server runtimes. Guides compatible model selection, GPU or CPU execution, caching, and memory cleanup without requiring a Python server.

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• Add text classification or translation to a browser app
• Implement image classification and object detection
• Transcribe audio using JavaScript

Write and review Gradio demos for Hugging Face ZeroGPU. Configure GPU time limits and dependencies, and troubleshoot errors caused by separate worker processes, shared state, concurrent requests, or CUDA package installation.

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• Review GPU handlers in a Gradio demo
• Tune GPU duration and quota usage
• Fix PicklingError and stale state across worker processes

Manage Hugging Face Hub through the hf command line tool. Download and upload models or datasets, organize cloud storage, manage authentication and local caches, run scheduled jobs, and deploy or maintain model inference services.

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• Download or publish models and datasets
• Sync training checkpoints to cloud storage
• Inspect and clean the local cache

Evaluate Hugging Face Hub models using inspect-ai or lighteval, with local inference or a provider-backed option. Choose an inference engine, start with a small test, and switch engines when model compatibility requires it.

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• Run benchmark evaluations on a local GPU
• Choose an evaluation framework and inference engine
• Check a small sample before a full evaluation

Train or fine-tune language and vision models on Hugging Face Jobs without a local GPU. Covers dataset checks, hardware and cost planning, training monitoring, Hub storage, and GGUF conversion for local use.

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• Fine-tune a language model without a local GPU
• Validate preference data before DPO training
• Estimate training costs and choose GPU hardware

Prepare an isolated Python environment for SageMaker deployments and AWS automation. The skill selects a supported Python version, installs current AWS packages, and checks versions so installation and execution use the same environment.

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• Prepare a Python environment for SageMaker deployment
• Separate AWS automation dependencies from system Python
• Check installed AWS package versions

Keep chat-ui model settings aligned with the HuggingFace router. Add descriptions for newly available models, check reasoning support, enable artifact panels based on model size, and remove settings for models no longer served.

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• Add newly released models to chat-ui settings
• Remove settings for models no longer served
• Check reasoning support and artifact eligibility

Draft huggingface_hub release notes from saved pull request JSON files. The skill groups changes, explains major updates, adds examples from documentation changes, and includes author credits and verified documentation links in a Markdown document.

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• Drafting release notes from saved pull request data
• Explaining major features and breaking changes
• Adding command or API examples from documentation changes

Use when adding or migrating non-thumbnail images for a Hugging Face Blog post. Uploads body images to the Hugging Face documentation-images dataset, updates the Markdown, and verifies the new links.

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Release workflow for deploying OpenEnv environments to Hugging Face Spaces and keeping canonical references in sync.

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Start work on a GitHub issue. Extracts requirements, creates worktree, sets up TDD workflow.

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Work on a batch of GitHub issues in parallel using Agent Teams. Creates one worktree per issue with TDD enforcement, coordinates via a lead agent, then produces stacked PRs.

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Update documentation across the repo after API changes. Finds stale references in docs, examples, docstrings, and fixes them.

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Write failing tests from requirements. Invoke for each todo before /implement.

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Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing repositories, models, datasets, and Spaces on the Hugging Face Hub. Replaces now deprecated `huggingface-cli` command.

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OpenEnv CLI (`openenv`) for scaffolding, validating, building, and pushing OpenEnv environments.

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Validate changes before submitting a pull request. Run comprehensive checks including lint, tests, alignment review, and RFC analysis. Use before creating a PR, when asked if code is ready for review, or before pushing for PR.

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Review code changes for bugs and alignment with OpenEnv principles and RFCs. Use when reviewing PRs, checking code before commit, or when asked to review changes. Implements two-tier review model.

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