Skill | Added | Review |
|---|---|---|
hugging-face-trackio Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, alerts with webhooks, HF Space syncing, and JSON output for automation. | 98 1.10x Agent success vs baseline Impact 94% 1.10xAverage score across 3 eval scenarios Securityby  Passed No known issues Reviewed: Version: 73246ad | |
hugging-face-tool-builder Use this skill when the user wants to build tool/scripts or achieve a task where using data from the Hugging Face API would help. This is especially useful when chaining or combining API calls or the task will be repeated/automated. This Skill creates a reusable script to fetch, enrich or process data. | 81 1.63x Agent success vs baseline Impact 100% 1.63xAverage score across 5 eval scenarios Securityby Advisory Suggest reviewing before use Reviewed: Version: 73246ad | |
hugging-face-paper-publisher Publish and manage research papers on Hugging Face Hub. Supports creating paper pages, linking papers to models/datasets, claiming authorship, and generating professional markdown-based research articles. | 68 1.37x Agent success vs baseline Impact 91% 1.37xAverage score across 3 eval scenarios Securityby Advisory Suggest reviewing before use Reviewed: Version: 73246ad | |
hugging-face-model-trainer This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, and model persistence. Should be invoked for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup. | 94 1.65x Agent success vs baseline Impact 99% 1.65xAverage score across 3 eval scenarios Securityby Advisory Suggest reviewing before use Reviewed: Version: 73246ad | |
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 Agent success vs baseline Impact 88% 1.31xAverage score across 3 eval scenarios Securityby Advisory Suggest reviewing before use Reviewed: Version: 73246ad | |
hugging-face-evaluation Add and manage evaluation results in Hugging Face model cards. Supports extracting eval tables from README content, importing scores from Artificial Analysis API, and running custom model evaluations with vLLM/lighteval. Works with the model-index metadata format. | 73 1.69x Agent success vs baseline Impact 95% 1.69xAverage score across 3 eval scenarios Securityby Advisory Suggest reviewing before use Reviewed: Version: 73246ad | |
hugging-face-datasets Create and manage datasets on Hugging Face Hub. Supports initializing repos, defining configs/system prompts, streaming row updates, and SQL-based dataset querying/transformation. Designed to work alongside HF MCP server for comprehensive dataset workflows. | 77 1.86x Agent success vs baseline Impact 82% 1.86xAverage score across 6 eval scenarios Securityby Advisory Suggest reviewing before use Reviewed: Version: 73246ad | |
hugging-face-cli Execute Hugging Face Hub operations using the `hf` CLI. Use when the user needs to download models/datasets/spaces, upload files to Hub repositories, create repos, manage local cache, or run compute jobs on HF infrastructure. Covers authentication, file transfers, repository creation, cache operations, and cloud compute. | 93 1.30x Agent success vs baseline Impact 93% 1.30xAverage score across 6 eval scenarios Securityby Advisory Suggest reviewing before use Reviewed: Version: 99a61b2 |