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AGENTS.md

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You have additional SKILLs documented in directories containing a "SKILL.md" file.

These skills are:

  • gradio -> "skills/huggingface-gradio/SKILL.md"
  • hf-cli -> "skills/hf-cli/SKILL.md"
  • hugging-face-dataset-viewer -> "skills/hugging-face-dataset-viewer/SKILL.md"
  • hugging-face-datasets -> "skills/hugging-face-datasets/SKILL.md"
  • hugging-face-evaluation -> "skills/hugging-face-evaluation/SKILL.md"
  • hugging-face-jobs -> "skills/hugging-face-jobs/SKILL.md"
  • hugging-face-model-trainer -> "skills/hugging-face-model-trainer/SKILL.md"
  • hugging-face-paper-publisher -> "skills/hugging-face-paper-publisher/SKILL.md"
  • hugging-face-tool-builder -> "skills/hugging-face-tool-builder/SKILL.md"
  • hugging-face-trackio -> "skills/hugging-face-trackio/SKILL.md"
  • hugging-face-vision-trainer -> "skills/hugging-face-vision-trainer/SKILL.md"
  • transformers-js -> "skills/transformers.js/SKILL.md"

IMPORTANT: You MUST read the SKILL.md file whenever the description of the skills matches the user intent, or may help accomplish their task.

<available_skills>

gradio: Build Gradio web UIs and demos in Python. Use when creating or editing Gradio apps, components, event listeners, layouts, or chatbots. hf-cli: "Hugging Face Hub CLI (hf) for downloading, uploading, and managing repositories, models, datasets, and Spaces on the Hugging Face Hub. Replaces now deprecated huggingface-cli command." hugging-face-dataset-viewer: Use this skill for Hugging Face Dataset Viewer API workflows that fetch subset/split metadata, paginate rows, search text, apply filters, download parquet URLs, and read size or statistics. 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. 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. 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. 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. 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. 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. 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. hugging-face-vision-trainer: Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs. transformers-js: Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in Node.js and browsers (with WebGPU/WASM) using pre-trained models from Hugging Face Hub. </available_skills>

Paths referenced within SKILL folders are relative to that SKILL. For example the hf-datasets scripts/example.py would be referenced as hf-datasets/scripts/example.py.