HuggingFace hf CLI: search/download/upload models, datasets.
npx skills add https://github.com/dzackgarza/ai --skill huggingface-hub이 명령을 Claude Code에 복사하여 붙여넣어 스킬을 설치하세요
Turn a rough draft, vague ambition, or messy task brief into a powerful Codex /goal objective and, when needed, companion workflow docs for long-running autonomous work. Use when the user asks to write, improve, format, sharpen, stress-test, or activate a Codex goal, thread goal, durable goal, or /goal prompt.
Decomposition playbook + specialist-roster conventions + anti-temptation rules for an orchestrator profile routing work through Kanban. The "don't do the work yourself" rule and the basic lifecycle are auto-injected into every kanban worker's system prompt; this skill is the deeper playbook when you're specifically playing the orchestrator role.
Pitfalls, examples, and edge cases for Hermes Kanban workers. The lifecycle itself is auto-injected into every worker's system prompt as KANBAN_GUIDANCE (from agent/prompt_builder.py); this skill is what you load when you want deeper detail on specific scenarios.
Use when reading a PDF — converts to markdown via Mistral OCR with local caching.
Use when you need to query Zotero data, find references, export citations, search for papers, or fetch PDFs using the local Zotero Web API cache.
Manage Zotero reference libraries via the Web API. Search, list, add items by DOI/ISBN/PMID (with duplicate detection), delete/trash items, update metadata and tags, export in BibTeX/RIS/CSL-JSON, batch-add from files, check PDF attachments, cross-reference citations, find missing DOIs via CrossRef, and fetch open-access PDFs. Supports --json output for scripting. Use when the user asks about academic references, citation management, literature libraries, PDFs for papers, bibliography export, or Zotero specifically.
| name | huggingface-hub |
| description | HuggingFace hf CLI: search/download/upload models, datasets. |
| version | 1.0.0 |
| author | Hugging Face |
| license | MIT |
| tags | ["huggingface","hf","models","datasets","hub","mlops"] |
hf) Reference GuideThe 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
hfcommand replaces the now deprecatedhuggingface-clicommand.
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.
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.
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.
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.
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.
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.
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.
Treat Hugging Face Spaces MCP integrations as volatile:
Configure and verify one Space at a time; multi-space MCP configurations can connect while exposing no usable tools.
Create the configured work directory before relying on the server.
Test each Space with a real tool call before using it in a workflow; search visibility does not prove the Gradio endpoint works.
Check direct Gradio MCP endpoints before concluding the wrapper surface is limited:
/gradio_api/mcp/sse and /gradio_api/mcp/http can expose richer toolsets than a
generated adapter.
Validate the response shape of each tool. Spaces often expose heterogeneous strings, JSON-like dictionaries, binary artifacts, or disabled endpoints behind similar client calls.
Bound request size, timeout, retry, and cache behavior explicitly. Space latency, GPU quota, and cold starts are part of the integration contract.
Treat working-space lists as stale quickly. Re-run discovery and a live smoke call before reporting that a Space is available.
For MCP-specific tool-surface design, use the native-mcp skill and its
references/tool-interface-design.md note.
Treat Hugging Face Spaces MCP integrations as volatile:
/gradio_api/mcp/sse and /gradio_api/mcp/http can expose richer
toolsets than a generated adapter.For MCP-specific tool-surface design, use the native-mcp skill and its
references/tool-interface-design.md note.
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).
--format json: Produces machine-readable output for automation.
-q / --quiet: Limits output to IDs only.
Extensions: Extend CLI functionality via GitHub repositories using
hf extensions install REPO_ID.
Skills: Manage AI assistant skills with hf skills add.