skills

In the author's own words, from each skill's SKILL.md.

• hf-cli

  Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub. Use when: handling authentication; managing local cache; managing Hugging Face Buckets; running or scheduling jobs on Hugging Face infrastructure; managing Hugging Face repos; discussions and pull requests; browsing models, datasets and spa…

  skills/hf-cli/SKILL.md on GitHub (opens in a new tab)

• hf-cloud-aws-context-discovery

  Discover the user's local AWS context (active profile, region, account ID, caller identity) at the start of any AWS task. Use this skill before any other AWS work — deploying to SageMaker, creating resources, calling AWS APIs, or anything that touches an AWS account. Use it especially when the user has not specified a region or profile explicitly, when they say things like "use my AWS account", "…

  skills/hf-cloud-aws-context-discovery/SKILL.md on GitHub (opens in a new tab)

• hf-cloud-python-env-setup

  Set up an isolated Python environment for SageMaker / AWS work, with the right Python version and current boto3. Use this skill whenever Python code will be executed for a SageMaker deployment, training job, or any AWS automation — including when about to run `pip install`, when about to invoke `boto3`, when creating or activating a virtualenv, or when the user asks to "set up the environment". N…

  skills/hf-cloud-python-env-setup/SKILL.md on GitHub (opens in a new tab)

• hf-cloud-sagemaker-deployment-planner

  Plan and coordinate the deployment of a model to Amazon SageMaker AI. Use this skill whenever the user wants to deploy, host, serve, or expose a model on SageMaker or AWS — including phrases like "deploy a model", "host this LLM on AWS", "serve this embedding model", "deploy a reranker", "deploy a text-to-image / diffusion model", "host this for async inference", "create an endpoint", "serve my f…

  skills/hf-cloud-sagemaker-deployment-planner/SKILL.md on GitHub (opens in a new tab)

• hf-cloud-sagemaker-iam-preflight

  Ensure a usable SageMaker execution role exists before deploying or training. Use this skill whenever about to create a SageMaker endpoint, model, training job, or any resource that requires an execution role. Use it especially when the user has not provided a role ARN explicitly, when scripts are about to call `iam:CreateRole`, or when an AccessDenied error mentions an IAM action. Never blindly…

  skills/hf-cloud-sagemaker-iam-preflight/SKILL.md on GitHub (opens in a new tab)

• hf-cloud-sagemaker-production-defaults

  Create a SageMaker endpoint (real-time, real-time scale-to-zero, or async) with autoscaling, CloudWatch alarms, and tagging enabled by default. Use this skill whenever about to create a SageMaker endpoint, write deployment code that calls `create_endpoint`, or finalize a deployment after the image URI and IAM role are known. Provides deploy.py for real-time endpoints, deploy_ic.py for real-time e…

  skills/hf-cloud-sagemaker-production-defaults/SKILL.md on GitHub (opens in a new tab)

• hf-cloud-serving-image-selection

  Pick the right serving container for a SageMaker model deployment and find its current image URI. Use this skill whenever about to deploy a model to a SageMaker endpoint and an image URI needs to be chosen — including when the user says "deploy this LLM", "host this HuggingFace model", "serve this fine-tuned model", "deploy this embedding model", "host a reranker", "serve a sentence-transformers…

  skills/hf-cloud-serving-image-selection/SKILL.md on GitHub (opens in a new tab)

• hf-mem

  Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub

  skills/hf-mem/SKILL.md on GitHub (opens in a new tab)

• huggingface-best

  Use when the user asks about finding the best, top, or recommended model for a task, wants to know what AI model to use, or wants to compare models by benchmark scores. Triggers on: "best model for X", "what model should I use for", "top models for [task]", "which model runs on my laptop/machine/device", "recommend a model for", "what LLM should I use for", "compare models for", "what's state of…

  skills/huggingface-best/SKILL.md on GitHub (opens in a new tab)

• huggingface-community-evals

  Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. Use for backend selection, local GPU evals, and choosing between vLLM / Transformers / accelerate. Not for HF Jobs orchestration, model-card PRs, .eval_results publication, or community-evals automation.

  skills/huggingface-community-evals/SKILL.md on GitHub (opens in a new tab)

• huggingface-datasets

  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.

  skills/huggingface-datasets/SKILL.md on GitHub (opens in a new tab)

• huggingface-gradio

  Build Gradio web UIs and demos in Python. Use when creating or editing Gradio apps, components, event listeners, layouts, or chatbots.

  skills/huggingface-gradio/SKILL.md on GitHub (opens in a new tab)

• huggingface-llm-trainer

  Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with 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, Track…

  skills/huggingface-llm-trainer/SKILL.md on GitHub (opens in a new tab)

• huggingface-local-models

  Use to select models to run locally with llama.cpp and GGUF on CPU, Mac Metal, CUDA, or ROCm. Covers finding GGUFs, quant selection, running servers, exact GGUF file lookup, conversion, and OpenAI-compatible local serving.

  skills/huggingface-local-models/SKILL.md on GitHub (opens in a new tab)

• huggingface-lora-space-builder

  Build and publish a Gradio demo on Hugging Face Spaces for a user-provided LoRA. Use when someone asks to create, generate, ship, or publish a Space, demo, Gradio app, or playground for a LoRA — including LoRAs for Qwen-Image, Qwen-Image-Edit, LTX-Video, Wan, FLUX, SDXL, or other diffusion base models. Also triggers when someone describes a LoRA they trained or hosts on the Hub and wants to share…

  skills/huggingface-lora-space-builder/SKILL.md on GitHub (opens in a new tab)

• huggingface-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.

  skills/huggingface-paper-publisher/SKILL.md on GitHub (opens in a new tab)

• huggingface-papers

  Look up and read Hugging Face paper pages in markdown, and use the papers API for structured metadata such as authors, linked models/datasets/spaces, Github repo and project page. Use when the user shares a Hugging Face paper page URL, an arXiv URL or ID, or asks to summarize, explain, or analyze an AI research paper.

  skills/huggingface-papers/SKILL.md on GitHub (opens in a new tab)

• huggingface-spaces

  Build, deploy, and maintain applications on Hugging Face Spaces — Gradio / Docker / Static SDKs, ZeroGPU and dedicated hardware, model loading, debugging, buckets, inference providers, community grants. Use whenever the user asks to create or host an app on Hugging Face, port code onto ZeroGPU, fix a Space that won't build or run, or otherwise work with `hf spaces …`, `@spaces.GPU`, Space README…

  skills/huggingface-spaces/SKILL.md on GitHub (opens in a new tab)

• huggingface-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.

  skills/huggingface-tool-builder/SKILL.md on GitHub (opens in a new tab)

• huggingface-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.

  skills/huggingface-trackio/SKILL.md on GitHub (opens in a new tab)

• huggingface-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 me…

  skills/huggingface-vision-trainer/SKILL.md on GitHub (opens in a new tab)

• huggingface-zerogpu

  AI demos and GPU compute with Gradio Spaces and Hugging Face Spaces ZeroGPU. Use when writing or reviewing code that uses `@spaces.GPU`, configuring `python_version` or `requirements.txt` for a ZeroGPU Space, or handling ZeroGPU-specific code constraints — pickle-based process isolation, `gr.State` semantics across the worker boundary, no `torch.compile` (use AoTI instead), CUDA wheel-only builds…

  skills/huggingface-zerogpu/SKILL.md on GitHub (opens in a new tab)

• train-sentence-transformers

  Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder, dense or static embedding model for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), `CrossEncoder` (reranker, pair scoring for two-stage retrieval / pair classification), `SparseEncoder` (SPLADE, sparse embedding model for learned-sparse retrieval), and `MultiVectorE…

  skills/train-sentence-transformers/SKILL.md on GitHub (opens in a new tab)

• 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 browsers and server-side runtimes (Node.js, Bun, Deno) with WebGPU/WASM using pre-trained mod…

  skills/transformers-js/SKILL.md on GitHub (opens in a new tab)

• trl-training

  Train and fine-tune transformer language models using TRL (Transformers Reinforcement Learning). Supports SFT, DPO, GRPO, KTO, RLOO and Reward Model training via CLI commands.

  skills/trl-training/SKILL.md on GitHub (opens in a new tab)

• hf-mcp

  Use Hugging Face Hub via MCP server tools. Search models, datasets, Spaces, papers. Get repo details, fetch documentation, run compute jobs, and use Gradio Spaces as AI tools. Available when connected to the HF MCP server.

  hf-mcp/skills/hf-mcp/SKILL.md on GitHub (opens in a new tab)

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