Claude Cursor Skill

huggingface

Inspect and manage robotics datasets, models, Jobs, and Spaces on Hugging Face.

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Download robium-ai-robium-skills_huggingface-498ea4e.zip · 3 KB
Part of robium-ai/robium — 44 skills

Install

skills CLI npx skills add https://github.com/robium-ai/robium/tree/main/skills/huggingface
Claude Code claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install robium-ai-robium@llmmart
Git git clone https://github.com/robium-ai/robium.git

The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole robium-ai/robium collection as a plugin from our marketplace. Git is the plain clone.

Skill manifest

Hugging Face

The Hub is an artifact boundary. Know exactly what is crossing it, under which identity and revision, before transferring data or changing remote state.

Inspect first

  • Check the installed hf CLI and the relevant command group's live help. Commands and hosted services evolve faster than this skill.
  • Inspect the repository card, file tree, revision, license, access state, and transfer size before downloading or planning around an artifact.
  • For robot data, also verify embodiment, task, observation/action schema, cameras, timing, episode structure, and format version.
  • Pin an immutable revision when the artifact feeds a reproducible build, fixture, evaluation, or training run.

Treat remote changes as real changes

  • Use an existing authenticated identity or a secure browser/device login. Do not place tokens in prompts, source, shell history, or logs.
  • Before creating or uploading, establish the destination owner, repository type, visibility, local source, changed paths, and whether deletion is involved.
  • Starting Jobs is paid remote compute. Establish the current hardware, command, timeout, outputs, secrets, and cost exposure before submission.
  • Inspect a Space's metadata, build logs, and runtime logs before restarting or changing hardware.
  • A Space is an optional hosted presentation layer, not a requirement for using Hub datasets or models. Most Docker Spaces can also be run locally; keep the application portable when self-hosting or another service is plausible.
  • After a mutation, inspect the remote result rather than trusting the local command's intent.

Go deeper only when needed

  • For current CLI shapes, Dataset Viewer endpoints, and mutation preflights, read references/hub-operations.md.
  • LeRobot owns dataset structure, processors, training, and evaluation; this skill owns the Hub boundary around those artifacts.
  • Data owns source selection. Use integration for service packaging, network, health, and self-hosting boundaries; use the relevant deployment skill for provider mechanics when a Space is not the chosen runtime.
  • Use the current Hugging Face documentation and live CLI help as the source of truth for volatile flags and services.

Done

  • The intended artifact, revision, identity, destination, and remote result are all verified, with no credential exposed and no unapproved paid or destructive action.
Files (robium)
  • references
    • hub-operations.md 3.3 KB
      # Hub operations checklist
      
      Use this compact reference after confirming the installed `hf` version. Run the
      specific command group's `--help` before using a flag not shown here.
      
      ## Read-only CLI path
      
      ```bash
      hf version
      hf auth whoami
      hf models info ORG/MODEL
      hf datasets info ORG/DATASET
      hf download ORG/REPO [FILES...] --repo-type model|dataset|space
      hf spaces info ORG/SPACE
      hf spaces logs ORG/SPACE --build
      hf jobs hardware
      hf jobs list
      hf jobs inspect JOB_ID
      hf jobs logs JOB_ID
      ```
      
      Authentication can be established with `hf auth login`. Never print or place a
      token directly in a logged command. `HF_TOKEN` is suitable only when a scoped
      secret has already been supplied through the environment or CI secret store.
      
      ## Dataset Viewer API
      
      Base URL: `https://datasets-server.huggingface.co`
      
      | Endpoint | Purpose | Key query parameters |
      |---|---|---|
      | `/is-valid` | Viewer processing status | `dataset` |
      | `/splits` | Subsets/configs and splits | `dataset` |
      | `/first-rows` | Preview first rows | `dataset`, `config`, `split` |
      | `/rows` | Fetch a slice, maximum 100 rows | previous plus `offset`, `length` |
      | `/parquet` | Discover Parquet exports | `dataset` |
      | `/size` | Rows and byte sizes | `dataset` |
      | `/statistics` | Precomputed column statistics | `dataset`, `config`, `split` |
      
      For gated/private data, pass `Authorization: Bearer <token>` through a secure
      HTTP client configuration. Do not paste a token into source, shell history, or
      captured logs. Start with `/splits`; use the returned config and split values in
      row/statistics calls.
      
      ## Mutation preflight
      
      Before creating a repo or uploading, state and confirm:
      
      1. account or organization namespace;
      2. model/dataset/space type;
      3. public/private visibility;
      4. exact local source and destination path;
      5. whether files are only added/replaced or any deletion is requested;
      6. license/card/robotics metadata and, for reproducible publication, revision.
      
      Before starting a Job, state and confirm hardware, namespace, image or UV
      command, timeout, secrets, output destination, and cost exposure. Use
      `hf jobs hardware` and live `hf jobs run --help` or `hf jobs uv run --help`;
      do not encode a stale flavor list here.
      
      ## Spaces are optional hosting
      
      - Spaces host Gradio, Docker, or static applications, but Hub artifacts do not
        require a Space. Choose one only when its managed build, visibility, and demo
        workflow are useful.
      - Most Space Docker images can be run locally. Verify the current Space's **Run
        with Docker** instructions and registry access instead of assuming the hosted
        runtime is the only deployment path.
      - This file owns Space repository metadata and logs. Route the application's
        container, health, ports, authentication, and self-hosted service boundary to
        `integration`, then use the selected provider skill for deployment.
      - Re-check the current [Spaces overview](https://huggingface.co/docs/hub/spaces-overview)
        and [Run with Docker](https://huggingface.co/docs/hub/spaces-run-with-docker)
        guidance before promising hardware, pricing, visibility, or portability.
      
      ## Source verification
      
      Checked directly on 2026-08-27 against the official Hugging Face CLI guide and
      reference, Dataset Viewer quickstart, Jobs overview/manage guides, and local
      `hf` 1.24.0 help. Re-check the live help for the target version.
      
  • evals.yaml 593 B
    triggers:
      positive:
        - phrase: if we use gradio do we need to host it on hugging face? can we just host it on our server and demo window would show the application?
          source: learnings/2026-07-13.md
        - phrase: inspect this robotics dataset on the Hugging Face Hub before downloading it
        - phrase: show me the available Hugging Face Jobs hardware, but do not start anything
      negative:
        - phrase: record a new LeRobotDataset episode from my robot arm
          expect: lerobot
        - phrase: should we collect demonstrations or generate synthetic data
          expect: data
    tasks: []
    
  • SKILL.md 2.6 KB
    ---
    name: huggingface
    description: Inspect and manage robotics datasets, models, Jobs, and Spaces on Hugging Face.
    ---
    
    # Hugging Face
    
    The Hub is an artifact boundary. Know exactly what is crossing it, under which
    identity and revision, before transferring data or changing remote state.
    
    ## Inspect first
    
    - Check the installed `hf` CLI and the relevant command group's live help.
      Commands and hosted services evolve faster than this skill.
    - Inspect the repository card, file tree, revision, license, access state, and
      transfer size before downloading or planning around an artifact.
    - For robot data, also verify embodiment, task, observation/action schema,
      cameras, timing, episode structure, and format version.
    - Pin an immutable revision when the artifact feeds a reproducible build,
      fixture, evaluation, or training run.
    
    ## Treat remote changes as real changes
    
    - Use an existing authenticated identity or a secure browser/device login. Do
      not place tokens in prompts, source, shell history, or logs.
    - Before creating or uploading, establish the destination owner, repository
      type, visibility, local source, changed paths, and whether deletion is
      involved.
    - Starting Jobs is paid remote compute. Establish the current hardware,
      command, timeout, outputs, secrets, and cost exposure before submission.
    - Inspect a Space's metadata, build logs, and runtime logs before restarting or
      changing hardware.
    - A Space is an optional hosted presentation layer, not a requirement for using
      Hub datasets or models. Most Docker Spaces can also be run locally; keep the
      application portable when self-hosting or another service is plausible.
    - After a mutation, inspect the remote result rather than trusting the local
      command's intent.
    
    ## Go deeper only when needed
    
    - For current CLI shapes, Dataset Viewer endpoints, and mutation preflights,
      read [references/hub-operations.md](references/hub-operations.md).
    - LeRobot owns dataset structure, processors, training, and evaluation; this
      skill owns the Hub boundary around those artifacts.
    - Data owns source selection. Use integration for service packaging, network,
      health, and self-hosting boundaries; use the relevant deployment skill for
      provider mechanics when a Space is not the chosen runtime.
    - Use the current [Hugging Face documentation](https://huggingface.co/docs)
      and live CLI help as the source of truth for volatile flags and services.
    
    ## Done
    
    - The intended artifact, revision, identity, destination, and remote result are
      all verified, with no credential exposed and no unapproved paid or
      destructive action.
    

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