Skills hugging-face-jobs
๐Ÿ“ฆ

hugging-face-jobs

Content revision r2 High Risk ๐ŸŒ Network access๐Ÿ“ Filesystem access๐Ÿ”‘ Env variablesโš™๏ธ External commandsโšก Contains scripts

Run Managed Hugging Face Jobs

Large machine learning workloads need costly hardware and reliable persistence. This skill prepares Hugging Face Jobs with suitable compute, secrets, schedules, and Hub storage.

Supports: Claude Codex Code(CC)
โš ๏ธ 38 Poor

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request
Review the Skillstore skill "hugging-face-jobs" from https://skillstore.io/skills/sickn33-hugging-face-jobs.md and its manifest at https://skillstore.io/api/skills/sickn33-hugging-face-jobs/manifest. Verify the artifact. Stop and obtain explicit user consent before installing or changing files.

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-jobs". Prepare a small sentiment-analysis test on managed CPU.

Expected outcome:

  • Hardware: cpu-basic
  • Timeout: 20 minutes
  • Persistence: Save the result to the named Hub dataset
  • Status: Awaiting approval before submission

Using "hugging-face-jobs". Run batch inference on a private dataset with an eight-billion-parameter model.

Expected outcome:

  • Hardware: Start with one A10G GPU and validate a small sample
  • Authentication: Use a read token for input and write access only for the output repository
  • Safety: Pin the model, script, dependencies, and container image

Using "hugging-face-jobs". Create a daily dataset statistics job.

Expected outcome:

  • Schedule: Daily at the approved UTC time
  • Output: Versioned statistics in the selected Hub repository
  • Control: Confirm estimated monthly cost before schedule creation

Security Audit

High Risk
v5 โ€ข 7/24/2026 Open versioned report

Most static findings are detector noise from Markdown, documentation URLs, safe environment access, and ordinary API examples. Confirmed risks involve exposed tokens and mutable remote code; semantic review also found unpinned images and missing approval gates for paid jobs.

9
Files scanned
4,871
Lines analyzed
13
Review items
0
False positives ignored

Confirmed security concerns (3)

High
Unpinned Container Images Receive Credentials
Examples run mutable latest or implicit-latest container images. One command passes HF_TOKEN into that image, allowing a compromised future image to capture the credential.
The image references are visibly unpinned, and the script example places HF_TOKEN in the same container invocation.
Medium
Paid Job Submission Has No Confirmation Gate
The skill directs the agent to submit paid jobs before reporting cost details and also supports recurring schedules. It does not require final user approval before creating either workload.
The directive explicitly submits first and reports details afterward, while the documented examples show nonzero costs and recurring jobs.
Medium
Secrets Accepted Through Command-Line Arguments
All bundled scripts accept Hugging Face tokens as command-line values. Tokens supplied this way can remain in shell history or appear in process listings.
Each parser explicitly defines an hf-token option, a well-known credential exposure channel when users supply the value on a command line.
Capability review items (13)

These are real local capabilities that may be expected for this skill, so they require review but are not counted as confirmed malicious behavior.

High
Hardcoded URL
hf_jobs("uv", {"script": "https://huggingface.co/user/repo/resolve/main/script.py"})
The example executes a remote script from a mutable main branch without a commit pin or integrity check. Upstream changes could run arbitrary code inside a credentialed job.
High
Shell command substitution
-e HF_TOKEN=$(python3 -c "from huggingface_hub import get_token; print(get_token())") \\
The example expands the local HF token into a command-line environment argument. The token can be exposed through process listings, shell tracing, or copied command output.
High
Shell command substitution
-e HF_TOKEN=$(python3 -c "from huggingface_hub import get_token; print(get_token())") \\
The example expands the local HF token into a command-line environment argument. The token can be exposed through process listings, shell tracing, or copied command output.
High
Hardcoded URL
https://huggingface.co/datasets/uv-scripts/synthetic-data/raw/main/cot-self-instruct.py \\
The example executes a remote script from a mutable main branch without a commit pin or integrity check. Upstream changes could run arbitrary code inside a credentialed job.
High
Hardcoded URL
uv run https://huggingface.co/datasets/uv-scripts/synthetic-data/raw/main/cot-self-instruct.py \\
The example executes a remote script from a mutable main branch without a commit pin or integrity check. Upstream changes could run arbitrary code inside a credentialed job.
High
Hardcoded URL
https://huggingface.co/datasets/uv-scripts/synthetic-data/raw/main/cot-self-instruct.py \\
The example executes a remote script from a mutable main branch without a commit pin or integrity check. Upstream changes could run arbitrary code inside a credentialed job.
High
Hardcoded URL
https://huggingface.co/datasets/uv-scripts/dataset-stats/raw/main/finepdfs-stats.py \\
The example executes a remote script from a mutable main branch without a commit pin or integrity check. Upstream changes could run arbitrary code inside a credentialed job.
High
Hardcoded URL
uv run https://huggingface.co/datasets/uv-scripts/dataset-stats/raw/main/finepdfs-stats.py \\
The example executes a remote script from a mutable main branch without a commit pin or integrity check. Upstream changes could run arbitrary code inside a credentialed job.
High
Hardcoded URL
" https://huggingface.co/datasets/uv-scripts/dataset-stats/raw/main/finepdfs-stats.py \\"
The example executes a remote script from a mutable main branch without a commit pin or integrity check. Upstream changes could run arbitrary code inside a credentialed job.
High
Hardcoded URL
uv run https://huggingface.co/datasets/uv-scripts/vllm/raw/main/generate-responses.py \\
The example executes a remote script from a mutable main branch without a commit pin or integrity check. Upstream changes could run arbitrary code inside a credentialed job.
High
Hardcoded URL
https://huggingface.co/datasets/uv-scripts/vllm/raw/main/generate-responses.py \\
The example executes a remote script from a mutable main branch without a commit pin or integrity check. Upstream changes could run arbitrary code inside a credentialed job.
High
Hardcoded URL
hf_jobs("uv", {"script": "https://huggingface.co/datasets/uv-scripts/.../raw/main/foo.py"})
The example executes a remote script from a mutable main branch without a commit pin or integrity check. Upstream changes could run arbitrary code inside a credentialed job.
High
Hardcoded URL
hf_jobs("uv", {"script": "https://raw.githubusercontent.com/huggingface/trl/main/trl/scripts/sft.py"
The example executes a remote script from a mutable main branch without a commit pin or integrity check. Upstream changes could run arbitrary code inside a credentialed job.

Risk Factors

๐ŸŒ Network access (45)
๐Ÿ“ Filesystem access (2)
๐Ÿ”‘ Env variables (47)
โš™๏ธ External commands (50)
โšก Contains scripts (1)
Audited by: codex View Audit History โ†’
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Security Assessment

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Academic citations (APA ยท BibTeX ยท CFF)

APA citation

sickn33. (2026). hugging-face-jobs security audit report (audit version 5) [Author version unspecified]. Skillstore. https://skillstore.io/skills/sickn33-hugging-face-jobs/audits/5

BibTeX citation

@techreport{sickn33-sickn33-hugging-face-jobs-2026, author = {sickn33}, title = {hugging-face-jobs security audit report (audit version 5)}, institution = {Skillstore}, year = {2026}, number = {5}, url = {https://skillstore.io/skills/sickn33-hugging-face-jobs/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-jobs 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-jobs/audits/5" identifiers: - type: other value: "skillstore:sickn33-hugging-face-jobs:audit:5" description: "Skillstore immutable audit report identifier"

Skillstore Score

Why this score Evidence Confidence: Medium
68
Architecture
85
Maintainability
87
Content
69
Community
70
Spec Compliance

What You Can Build

Process Large Datasets

Prepare a managed CPU job that transforms data and persists results to a selected Hub repository.

Run Batch Model Inference

Configure a GPU job that generates responses for a Hub dataset and publishes the completed dataset.

Automate Recurring Workloads

Create reviewed schedules for periodic processing, monitoring, and Hub updates.

Try These Prompts

Prepare a CPU Test
Prepare a Hugging Face Job for [task] on cpu-basic. Estimate runtime and cost, then wait for my approval before submission.
Configure Batch Inference
Prepare batch inference for dataset [dataset] with model [model]. Recommend hardware, timeout, output repository, and a secure token configuration.
Design a Scheduled Pipeline
Design a scheduled job for [task] using schedule [schedule]. Show expected cost, persistence, failure handling, and approval details before creation.
Plan a Reproducible Multi-GPU Run
Plan a multi-GPU job for [workload]. Pin scripts and images, set least-privilege secrets, add checkpoints, and define monitoring and cancellation criteria.

Best Practices

  • Pin remote scripts, dependencies, models, and container images to immutable versions.
  • Confirm hardware, timeout, schedule, destination, and estimated cost before submission.
  • Use least-privilege secrets and persist required outputs before the job ends.

Avoid

  • Do not place access tokens in scripts, environment examples, shell substitutions, or command arguments.
  • Do not execute scripts or images from mutable branches and tags.
  • Do not submit paid or recurring jobs before the user approves the final configuration.

Frequently Asked Questions

Do I need a paid Hugging Face account?
Yes. Hugging Face Jobs requires an eligible Pro, Team, or Enterprise account.
Can this skill use GPUs and TPUs?
Yes. It documents available CPU, GPU, and TPU flavors and helps select a suitable option.
How are results preserved?
Jobs are temporary. Persist required outputs to the Hugging Face Hub or another approved external store.
How should authentication be provided?
Use the platform secret mechanism with a least-privilege token. Avoid command-line values and ordinary environment configuration.
Can the skill create scheduled jobs?
Yes. It supports predefined schedules and cron expressions, but recurring cost requires explicit approval.
Does the skill monitor jobs continuously?
No. It provides status, inspection, log, and cancellation operations without continuous polling.

Developer Details

Author

sickn33

License

Complete terms in LICENSE.txt

Skillstore revision

r2

Version notice

The author did not declare a version.

Ref

88a8e9a07f4c54ab105c1c41b6267c287146b07b

Maintenance freshness

7/26/2026

Usage

7 downloads ยท 102 views

File structure

๐Ÿ“„ index.html

๐Ÿ“ references/

๐Ÿ“„ hardware_guide.md

๐Ÿ“„ hub_saving.md

๐Ÿ“„ token_usage.md

๐Ÿ“„ troubleshooting.md

๐Ÿ“ scripts/

๐Ÿ“„ cot-self-instruct.py

๐Ÿ“„ finepdfs-stats.py

๐Ÿ“„ generate-responses.py

๐Ÿ“„ SKILL.md

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