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Huggingface Hub

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Tommy-yw
huggingface-hub

Hugging Face Hub CLI (hf) — search, download, and upload models and datasets, manage repos, query datasets with SQL, deploy inference endpoints, manage Spaces and buckets.

Overview

PublisherTommy-yw
RepositoryRunbookHermes
Skill namehuggingface-hub
Stars
546
Forks
41
Bundled files
Instructions only
LicenseMIT
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Tommy-yw on GitHub. Read the source before you install it.

Installation

Install the Huggingface Hub AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Tommy-yw/RunbookHermes/tree/main/skills/mlops/huggingface-hub
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Tommy-yw/RunbookHermes.git /tmp/RunbookHermes
mkdir -p .claude/skills
cp -r /tmp/RunbookHermes/skills/mlops/huggingface-hub .claude/skills/huggingface-hub
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Huggingface Hub in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Huggingface Hub on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Huggingface Hub is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Hugging Face CLI (hf) Reference Guide

The hf command is the modern command-line interface for interacting with the Hugging Face Hub, providing tools to manage repositories, models, datasets, and Spaces.

IMPORTANT: The hf command replaces the now deprecated huggingface-cli command.

Quick Start

  • Installation: curl -LsSf https://hf.co/cli/install.sh | bash -s
  • Help: Use hf --help to view all available functions and real-world examples.
  • Authentication: Recommended via HF_TOKEN environment variable or the --token flag.

Core Commands

General Operations

  • hf download REPO_ID: Download files from the Hub.
  • hf upload REPO_ID: Upload files/folders (recommended for single-commit).
  • hf upload-large-folder REPO_ID LOCAL_PATH: Recommended for resumable uploads of large directories.
  • hf sync: Sync files between a local directory and a bucket.
  • hf env / hf version: View environment and version details.

Authentication (hf auth)

  • login / logout: Manage sessions using tokens from huggingface.co/settings/tokens.
  • list / switch: Manage and toggle between multiple stored access tokens.
  • whoami: Identify the currently logged-in account.

Repository Management (hf repos)

  • create / delete: Create or permanently remove repositories.
  • duplicate: Clone a model, dataset, or Space to a new ID.
  • move: Transfer a repository between namespaces.
  • branch / tag: Manage Git-like references.
  • delete-files: Remove specific files using patterns.

Specialized Hub Interactions

Datasets & Models

  • Datasets: hf datasets list, info, and parquet (list parquet URLs).
  • SQL Queries: hf datasets sql SQL — Execute raw SQL via DuckDB against dataset parquet URLs.
  • Models: hf models list and info.
  • Papers: hf papers list — View daily papers.

Discussions & Pull Requests (hf discussions)

  • Manage the lifecycle of Hub contributions: list, create, info, comment, close, reopen, and rename.
  • diff: View changes in a PR.
  • merge: Finalize pull requests.

Infrastructure & Compute

  • Endpoints: Deploy and manage Inference Endpoints (deploy, pause, resume, scale-to-zero, catalog).
  • Jobs: Run compute tasks on HF infrastructure. Includes hf jobs uv for running Python scripts with inline dependencies and stats for resource monitoring.
  • Spaces: Manage interactive apps. Includes dev-mode and hot-reload for Python files without full restarts.

Storage & Automation

  • Buckets: Full S3-like bucket management (create, cp, mv, rm, sync).
  • Cache: Manage local storage with list, prune (remove detached revisions), and verify (checksum checks).
  • Webhooks: Automate workflows by managing Hub webhooks (create, watch, enable/disable).
  • Collections: Organize Hub items into collections (add-item, update, list).

Advanced Usage & Tips

Global Flags

  • --format json: Produces machine-readable output for automation.
  • -q / --quiet: Limits output to IDs only.

Extensions & Skills

  • Extensions: Extend CLI functionality via GitHub repositories using hf extensions install REPO_ID.
  • Skills: Manage AI assistant skills with hf skills add.

Frequently asked questions

What does the Huggingface Hub AI skill do?

Hugging Face Hub CLI (hf) — search, download, and upload models and datasets, manage repos, query datasets with SQL, deploy inference endpoints, manage Spaces and buckets.

Why use Huggingface Hub on TypingMind?

Because you install it once and use it with any model. Huggingface Hub is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Huggingface Hub in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Tommy-yw/RunbookHermes/tree/main/skills/mlops/huggingface-hub. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Huggingface Hub?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Huggingface Hub?

As many as you like. As long as a model supports skills, you can use Huggingface Hub with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Huggingface Hub AI skill free?

Yes. It is published on GitHub by Tommy-yw under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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