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hugging-face-jobs

This skill should be used when users want to run any workload on Hugging Face Jobs infrastructure. Covers UV scripts, Docker-based jobs, hardware selection, cost estimation, authentication with tokens, secrets management, timeout configuration, and result persistence. Designed for general-purpose compute workloads including data processing, inference, experiments, batch jobs, and any Python-based tasks. Should be invoked for tasks involving cloud compute, GPU workloads, or when users mention running jobs on Hugging Face infrastructure without local setup.

80

1.31x
Quality

75%

Does it follow best practices?

Impact

88%

1.31x

Average score across 3 eval scenarios

SecuritybySnyk

Advisory

Suggest reviewing before use

Optimize this skill with Tessl

npx tessl skill review --optimize ./skills/hugging-face-jobs/SKILL.md
SKILL.md
Quality
Evals
Security

Evaluation results

76%

20%

Batch Sentiment Analysis on Customer Reviews

UV job submission with inline script and Hub persistence

Criteria
Without context
With context

Uses hf_jobs MCP call

0%

0%

Inline script content

100%

100%

PEP 723 dependency header

100%

100%

Token via secrets parameter

0%

100%

$HF_TOKEN placeholder used

0%

0%

Result persistence included

100%

100%

GPU hardware selected

50%

100%

Non-default timeout set

0%

0%

Does NOT save script to local file first

100%

100%

Token verification in script

0%

100%

Notes.md explains decisions

100%

100%

90%

35%

Automated Job Submission Integration for ML Platform

Python API token handling: get_token() vs $HF_TOKEN literal

Criteria
Without context
With context

Identifies $HF_TOKEN bug

50%

100%

Uses get_token() fix

0%

100%

Does NOT use $HF_TOKEN in Python API

100%

100%

Token via secrets not env

100%

100%

Explains MCP vs Python API distinction

0%

100%

Token not hardcoded

100%

100%

Script comment explaining approach

100%

100%

Script inner token verification

0%

0%

Explains 401 error root cause

75%

100%

99%

9%

Automated Weekly Model Evaluation Pipeline

Scheduled jobs with hardware selection and timeout configuration

Criteria
Without context
With context

Uses scheduled job API

100%

100%

Correct schedule format

100%

100%

Appropriate GPU hardware

100%

100%

Timeout with buffer above runtime

100%

100%

Inline script content

0%

100%

PEP 723 dependency header

100%

100%

Token via secrets

100%

90%

Result persistence in script

100%

100%

Timeout rationale documented

100%

100%

Repository
huggingface/skills
Evaluated
Agent
Claude Code
Model
Claude Sonnet 4.6

Table of Contents

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