huggingface-jobs Skill
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 incl Published by openai in plugins.
What is huggingface-jobs Skill?
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 incl Published by openai in plugins. This profile combines repository metadata with install, compatibility, and usage signals so developers can quickly decide whether it fits their agent workflow before opening the source repository.
Automated repository signals based on public metadata such as recency, license, installation evidence, and adoption. These are not a security audit or endorsement.
Key capabilities
- Includes SKILL.md support
- Reusable instructions support
- Testing
- Deployment
- Data analysis
- Testing use cases
- Deployment use cases
Technical details
- Install or run with Copy skill directory
When to use huggingface-jobs Skill
- Use it for testing.
- Use it for deployment.
- Use it for data analysis.
Built with
Editorial notes
Source
- Creator: openai
- Repository: openai/plugins
- Skill file: plugins/hugging-face/skills/jobs/SKILL.md
What it does
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 incl
Skill instructions
Running Workloads on Hugging Face Jobs Overview Run any workload on fully managed Hugging Face infrastructure. No local setup required—jobs run on cloud CPUs, GPUs, or TPUs and can persist results to the Hugging Face Hub. Common use cases: - Data Processing - Transform, filter, or analyze large datasets - Batch Inference - Run inference on thousands of samples - Experiments & Benchmarks - Reproducible ML experiments - Model Training - Fine-tune models (see model-trainer skill for TRL-specific training) - Synthetic Data Generation - Generate datasets using LLMs - Development & Testing - Test code without local GPU setup - Scheduled Jobs - Automate recurring tasks For model training specifically: See the model-trainer skill for TRL-based training workflows. When to Use This Skill Use this skill when users want to: - Run Python workloads on cloud infrastructure - Execute jobs without local GPU/TPU setup - Process data at scale - Run batch inference or experiments - Schedule recurring task
Explore related resources
Frequently asked questions
What is huggingface-jobs?
huggingface-jobs is a open-source AI agent skill with Copy skill directory. This skill should be used when users want to run any workload on Hugging Face Jobs infrastructure.
Who is huggingface-jobs best for?
huggingface-jobs is best for reusing agent instructions, scripts, and references, testing workflows, deployment workflows, data analysis workflows.
How do I install huggingface-jobs?
Install or run huggingface-jobs using Copy skill directory. Check huggingface-jobs for the latest setup command.
Is huggingface-jobs actively maintained?
huggingface-jobs may need a closer maintenance check before production use.
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