A powerful command-line interface to manage Hugging Face repositories, models, datasets, and cloud infrastructure directly from your terminal.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install hugging-face-cli
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install hugging-face-cli using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Hugging Face CLI?
The Hugging Face CLI skill provides a comprehensive bridge between your local environment and the Hugging Face Hub, the world's leading platform for open-source AI. By integrating these Openclaw Skills, developers can automate the management of AI models, datasets, and spaces without manual browser navigation. This tool enables professional AI workflows by providing direct access to the Hub's most powerful features through a simple terminal interface, ensuring that your model deployment and dataset management are handled with developer-centric precision.
Hugging Face CLI Use Cases
- Searching for trending AI models and datasets on the Hub.
- Automating the download of specific model revisions and weights in safetensors format.
- Uploading fine-tuned model weights to private or public repositories with commit messages.
- Managing Hugging Face Spaces, Inference Endpoints, and cloud compute jobs programmatically.
- Organizing AI research by creating and managing custom collections of repositories.
How Hugging Face CLI Works
- The user verifies the local environment has the hf CLI installed and configured for use with Openclaw Skills.
- Authentication is established using the HF_TOKEN environment variable to securely interact with private and public Hub assets.
- The skill executes targeted Hub commands for asset discovery, repository creation, or file synchronization.
- Large-scale data transfers are handled using specialized folder upload and download functions to ensure reliability.
- Project lifecycles are managed through branch tagging, pull request reviews, and discussion moderation directly from the CLI.
Hugging Face CLI Setup
To begin using these Openclaw Skills, ensure the hf CLI is installed on your system:
pip install -U \"huggingface_hub[cli]\"
# Or using Homebrew on macOS
brew install hf
Configure your environment with a Hugging Face User Access Token by setting the necessary environment variable:
export HF_TOKEN=\"your_hf_token_here\"
Verify your setup by running hf auth whoami to confirm your identity and access level.
Hugging Face CLI Data Schema & Taxonomy
The skill interacts with the Hugging Face Hub architecture and local storage through the following schema:
| Component | Description |
|---|---|
| Repository ID | The primary namespace (e.g., username/repo-name) used to identify Hub assets. |
| Local Cache | System-level storage managed via hf cache for efficient model and dataset reuse. |
| Metadata | Includes tags, revision hashes, and YAML-based frontmatter for repo documentation. |
| Compute Specs | Configuration data for Inference Endpoints and Cloud Jobs (Docker images, hardware types). |
Hugging Face CLI Advanced Features
- SQL Query Support: Execute SQL queries directly against remote datasets hosted on the Hub.
- Inference Deployment: Create, pause, and scale production-ready Inference Endpoints for AI models.
- Cloud Compute Integration: Run and monitor Docker-based training or evaluation jobs on Hugging Face infrastructure.
- Repository Governance: Advanced management of pull requests, discussions, and repository visibility settings using Openclaw Skills.
- Cache Optimization: Granular control over local disk usage with cache pruning and individual file removal.
SKILL.md
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