hugging-face-cli
Manage Hugging Face Hub Resources with hf
Hugging Face Hub operations span many commands, resources, and safety considerations. This skill selects suitable `hf` workflows and explains their effects.
Install with my Agent
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Review the Skillstore skill "hugging-face-cli" from https://skillstore.io/skills/sickn33-hugging-face-cli.md and its manifest at https://skillstore.io/api/skills/sickn33-hugging-face-cli/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.Your Agent should still show its plan and request any confirmation required by the security policy.
Agent-readable resources
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
Test it
Using "hugging-face-cli". Download a fixed GPT-2 revision into a project directory without replacing existing files.
Expected outcome:
Recommended workflow: verify the revision, review disk space, preview the download, then fetch it into the requested directory without forcing replacement.
Using "hugging-face-cli". Synchronize a local dataset folder to a private bucket and show planned changes first.
Expected outcome:
- Start with a dry run that preserves remote-only files.
- Review uploads, replacements, and exclusions before applying the saved plan.
- Confirm the destination bucket and active account before execution.
Using "hugging-face-cli". Schedule a weekly GPU evaluation job with secrets and a controlled timeout.
Expected outcome:
The plan separates secrets from ordinary variables, checks hardware cost, defines the schedule and timeout, then verifies the created job.
Security Audit
SafeReview confirmed two medium network risks in the documented installers: each fetches a mutable remote script that is later executed after manual inspection. The remaining 187 findings are false positives caused by Markdown code spans, metadata links, controlled temporary-directory handling, and ordinary CLI documentation. No prompt injection, credential exfiltration, or malicious intent was found.
Capability review items (2)
These are real local capabilities that may be expected for this skill, so they require review but are not counted as confirmed malicious behavior.
Risk Factors
⚙️ External commands (50)
🌐 Network access (5)
📁 Filesystem access (5)
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<iframe src="https://skillstore.io/embed/skills/sickn33-hugging-face-cli.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>Academic citations (APA · BibTeX · CFF)
APA citation
sickn33. (2026). hugging-face-cli security audit report (audit version 5) [Author version unspecified]. Skillstore. https://skillstore.io/skills/sickn33-hugging-face-cli/audits/5BibTeX citation
@techreport{sickn33-sickn33-hugging-face-cli-2026,
author = {sickn33},
title = {hugging-face-cli security audit report (audit version 5)},
institution = {Skillstore},
year = {2026},
number = {5},
url = {https://skillstore.io/skills/sickn33-hugging-face-cli/audits/5},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "hugging-face-cli security audit report (audit version 5)"
version: "unspecified"
type: report
authors:
- name: "sickn33"
date-released: "2026-07-24"
url: "https://skillstore.io/skills/sickn33-hugging-face-cli/audits/5"
identifiers:
- type: other
value: "skillstore:sickn33-hugging-face-cli:audit:5"
description: "Skillstore immutable audit report identifier"
Skillstore Score
Why this score Evidence Confidence: HighWhat You Can Build
Transfer model artifacts
Choose safe download or upload options, revisions, filters, destinations, and output formats for model repositories.
Operate hosted workloads
Plan jobs, schedules, endpoints, Spaces, hardware settings, logs, and lifecycle actions with approval checkpoints.
Explore Hub resources
Search models, datasets, papers, leaderboards, collections, and repository metadata using focused CLI queries.
Try These Prompts
Confirm which Hugging Face account is active and explain the result. Do not print access tokens.
Prepare the safest `hf` command to download [REPO_ID] at [REVISION] into [LOCAL_DIR]. Explain authentication and disk requirements.
Create a dry-run-first plan to synchronize [LOCAL_DIR] with bucket [BUCKET_ID]. Preserve remote files unless I explicitly approve deletion.
Design an `hf` CLI workflow to deploy [MODEL_REPO] as endpoint [NAME] in [REGION]. Include cost checks, secret handling, verification, and rollback.
Best Practices
- Use dry runs, saved plans, and explicit confirmations before deletion, synchronization, deployment, or public visibility changes.
- Keep tokens in approved credential storage or environment variables, and never print or place them in command history.
- Pin repository revisions and verify current CLI help, pricing, quotas, and hardware availability before execution.
Avoid
- Do not run delete, merge, sync-delete, or infrastructure commands without reviewing the exact target and impact.
- Do not pass access tokens directly in commands, logs, prompts, or shared files.
- Do not install extensions or remote scripts without reviewing their source, pinning versions, and verifying integrity.