Agent skills by huggingface

Finds or validates a usable SageMaker execution role before deploying or training, so scripts do not try to create IAM roles they lack permission to create.

huggingface/skills

Chooses the right serving container and current image URI for deploying a Hugging Face model to a SageMaker endpoint, preferring Hugging Face images over generic ones.

huggingface/skills

Trains or fine-tunes language and vision models with TRL or Unsloth on Hugging Face Jobs cloud GPUs, then converts the results to GGUF.

huggingface/skills

Routes a sentence-transformers training task to the right model type and required reference docs and example scripts, covering bi-encoders, rerankers, sparse and multi-vector models.

huggingface/skills

Sets up an isolated Python environment with a supported interpreter and current boto3 before any SageMaker deployment, training or AWS automation code runs.

huggingface/skills

A skill your agent uses when adding or migrating non-thumbnail images for a Hugging Face Blog post.

huggingface/blog

Runs evaluations of Hugging Face Hub models on local hardware with inspect-ai or lighteval, and helps choose between vLLM, Transformers and accelerate backends.

huggingface/skills

Indexes research papers on the Hugging Face Hub from arXiv, links them to models and datasets, claims authorship and generates markdown research articles from templates.

huggingface/skills

Builds reusable command line scripts that fetch, enrich or process data from the Hugging Face API, aimed at chained, repeated or automated tasks.

huggingface/skills

Deploys SageMaker endpoints with autoscaling, CloudWatch alarms and tags on by default, using scripts for real-time, scale-to-zero and async setups.

huggingface/skills

Diagnose broken Hugging Face Gradio Spaces from their actual logs and pinned source, then prepare a minimal verified source fix as candidate files.

huggingface/hf-mcp-server

Trains and fine-tunes object detection, image classification and SAM or SAM2 segmentation models on Hugging Face Jobs cloud GPUs and saves the results to the Hub.

huggingface/skills

Prioritize and run Modal conference-deadline agents on high-ROI conferences only.

huggingface/ai-deadlines

Finds llama.cpp-compatible GGUF models on the Hugging Face Hub, picks a quantization for your hardware and launches them with llama-cli or llama-server.

huggingface/skills

Logs and visualizes ML training metrics with Trackio, firing alerts for issues like loss spikes, and syncing a live dashboard to a Hugging Face Space.

huggingface/skills

Explores Hugging Face datasets through the read-only Dataset Viewer API: list splits, preview and page through rows, search, filter, and fetch parquet links and statistics.

huggingface/skills

Fetches Hugging Face paper pages as markdown and reads paper metadata through the papers API when you share a paper URL, an arXiv link or an arXiv ID.

huggingface/skills

Guides building Gradio web UIs and ML demos in Python, covering Interface, Blocks, ChatInterface, event listeners, layouts and chatbots.

huggingface/skills

Covers the rules for writing Gradio Spaces on ZeroGPU hardware: the @spaces.GPU decorator, duration and quota tuning, process isolation and CUDA build limits.

huggingface/skills

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

huggingface/skills

Finds top models for a task from official Hugging Face benchmark leaderboards, filters them to what fits your hardware, and returns a comparison table with scores.

huggingface/skills

Builds and publishes a Gradio demo on Hugging Face Spaces for a LoRA, with the pipeline, UI and settings chosen to match that LoRA's task and model card.

huggingface/skills

Build, deploy, and maintain applications on Hugging Face Spaces — Gradio / Docker / Static SDKs, ZeroGPU and dedicated hardware, model loading, debugging, buckets, inference providers, community…

huggingface/skills

Use Hugging Face Hub via MCP server tools. An agent skill from huggingface/skills.

huggingface/skills

Entry point for hosting a model on Amazon SageMaker: asks a few questions, picks a deployment pathway and hands off to the specialist skills.

huggingface/skills

Reference for post-training language models with TRL: which trainer and dataset format to use for SFT, DPO, GRPO, KTO and reward models, and how to add LoRA.

huggingface/skills

Runs pre-trained Hugging Face models in JavaScript or TypeScript with Transformers.js, in browsers or Node.js, Bun and Deno, for text, vision, audio and multimodal tasks.

huggingface/skills

Discover the user's local AWS context (active profile, region, account ID, caller identity) at the start of any AWS task.

huggingface/skills