Agent skill · software engineering · aiskillstore
hugging-face-jobs
Run workloads on Hugging Face Jobs with managed CPUs, GPUs, TPUs, secrets, and Hub persistence.
Why this skill is useful
Provides specific commands and workflows for running workloads on Hugging Face infrastructure that the AI wouldn't reliably generate on its own.
What it needs
Requires huggingface account access. About 11k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
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 tasks Use GPUs/TPUs for any workload Persist results to the Hugging Face Hub Key Directives When assisting with jobs: 1. ALWAYS use hfjobs() MCP tool - Submit jobs using hfjobs("uv", {...}) or hfjobs("run", {...}). The script parameter accepts Python code directly. Do NOT save to local files unless the user explicitly requests it. Pass the script content as a string to hfjobs(). 2. Always handle authentication - Jobs that interact with the Hub require HFTOKEN via secrets. See Token Usage section below. 3. Provide job details after submission - After submitting, provide job ID, monitoring URL, estimated time, and note that the user can request status checks later. 4. Set appropriate timeouts - Default 30min may be insufficient for long-running tasks. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/hugging-face-jobs