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- huggingface-spaces
Build, deploy, and maintain applications on Hugging Face Spaces — Gradio / Docker / Static SDKs, ZeroGPU and dedicated hardware, model loading, debugging, bucke
- huggingface-trackio
Track and visualize ML training experiments with Trackio.
- huggingface-gradio
Build Gradio web UIs and demos in Python.
- huggingface-llm-trainer
Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure.
- 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,
- train-sentence-transformers
Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder; dense or static embedding model; for retrieval, similarity, clustering
- 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.
- 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,
- 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.
- hf-cloud-sagemaker-deployment-planner
Plan and coordinate the deployment of a model to Amazon SageMaker AI.
- huggingface-local-models
Use to select models to run locally with llama.cpp and GGUF on CPU, Mac Metal, CUDA, or ROCm.
- 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
- hf-cloud-python-env-setup
Set up an isolated Python environment for SageMaker / AWS work, with the right Python version and current boto3.
- huggingface-paper-publisher
Publish and manage research papers on Hugging Face Hub.
- 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.
- 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.
- hf-cloud-sagemaker-iam-preflight
Ensure a usable SageMaker execution role exists before deploying or training.
- hf-cloud-serving-image-selection
Pick the right serving container for a SageMaker model deployment and find its current image URI.
- huggingface-zerogpu
AI demos and GPU compute with Gradio Spaces and Hugging Face Spaces ZeroGPU.
- transformers-js
Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript.
- trl-training
Train and fine-tune transformer language models using TRL (Transformers Reinforcement Learning).
- huggingface-community-evals
Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware.
- huggingface-lora-space-builder
Build and publish a Gradio demo on Hugging Face Spaces for a user-provided LoRA.
- hf-mem
Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub
- 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 rep
- @cocaxcode/database-mcp
A database MCP server providing 33 tools across PostgreSQL, MySQL, and SQLite, with connection groups, rollback, dump/restore, and schema auto-discovery via nat
- mnemon-mcp
A local-first MCP server providing persistent, layered memory (episodic, semantic, procedural, resource) for AI agents, with search, versioning, and session man
- mcp-near-wallet-manager
Enables NEAR wallet operations including wallet creation, balance checking, and transaction signing for agent workflows.
- scan-mcp
Enables scanner capture via SANE on Linux, providing tools for device discovery, scan jobs with ADF/duplex support, document batching, and multipage assembly th
- VinvAI
Runs, tests, and finds issues in your Python services with zero code changes, then helps your AI agent fix what breaks and proves it with acceptance tests.