541 boosters for "mod" — open source, verified from GitHub, ready to install
A skill for bootstrapping, developing, and designing modern WinUI 3 desktop applications with C# using Microsoft's official guidance and patterns. Useful for Windows developers building new apps or troubleshooting WinUI-related setup and implementation challenges.
Build professional-grade design systems in Figma that match code. This skill orchestrates multi-phase workflows across 20–100+ calls, enforcing quality patterns from real-world design systems (Material 3, Polaris, Figma UI3, Simple DS). Every design system build follows this phase order. Skipping o
Use MCP to execute JavaScript in Figma files via the Plugin API. All detailed reference docs live in . Before anything, load plugin-api-standalone.index.md to understand what is possible. When you are asked to write plugin API code, use this context to grep plugin-api-standalone.d.ts for relevant t
Provides developers with authoritative, up-to-date OpenAI API documentation and guidance by prioritizing official MCP tools for questions about building with OpenAI products. Ideal for developers integrating OpenAI APIs who need reliable, cited references.
"version": "2.44.0", "description": "Integration between n8n workflow automation and Model Context Protocol (MCP)", "main": "dist/index.js",
Heuristic scoring (no AI key configured).
Context Mode is an MCP server that dramatically reduces Claude's context window consumption through sandboxed code execution and intelligent full-text search, enabling developers to work with larger codebases and knowledge bases without hitting token limits.
"name": "@pascal-app/mcp", "description": "Model Context Protocol server for Pascal 3D editor", "main": "./dist/index.js",
Enables AI assistants to programmatically create and edit PowerPoint presentations using PptxGenJS with layout helpers and validation utilities. Developers and content creators benefit from automating slide deck generation, modification, and troubleshooting.
Search the Hugging Face Hub for llama.cpp-compatible GGUF repos, choose the right quant, and launch the model with or . 1. Search the Hub with . 3. Prefer the exact HF local-app snippet and quant recommendation when it is visible.
You are an expert at using the TRL (Transformers Reinforcement Learning) library to train and fine-tune large language models. TRL provides CLI commands for post-training foundation models using state-of-the-art techniques: TRL is built on top of Hugging Face Transformers and Accelerate, providing s
This skill provides comprehensive tools for AI engineers and researchers to publish, manage, and link research papers on the Hugging Face Hub. It streamlines the workflow from paper creation to publication, including integration with arXiv, model/dataset linking, and authorship management. The inclu
Hugging Face Paper pages (hf.co/papers) is a platform built on top of arXiv (arxiv.org), specifically for research papers in the field of artificial intelligence (AI) and computer science. Hugging Face users can submit their paper at hf.co/papers/submit, which features it on the Daily Papers feed (h
Tiebreakers when the request is ambiguous: "embedding model" / "vector search" / "similarity" → [SentenceTransformer]. "rerank" / "ranker" / "two-stage" → [CrossEncoder]. "SPLADE" / "sparse" / "inverted index" → [SparseEncoder]. If still unclear, ask. Override only if the user specifies otherwise: T
Train object detection, image classification, and SAM/SAM2 segmentation models on managed cloud GPUs. No local GPU setup required—results are automatically saved to the Hugging Face Hub. Use this skill when users want to: Helper scripts use PEP 723 inline dependencies. Run them with :
Provides the Hugging Face Hub CLI (`hf`) tool for downloading, uploading, and managing models, datasets, and Spaces directly from Claude Code. Essential for developers integrating Hugging Face resources into AI workflows.
Train language models using TRL (Transformer Reinforcement Learning) on fully managed Hugging Face infrastructure. No local GPU setup required—models train on cloud GPUs and results are automatically saved to the Hugging Face Hub. Use this skill when users want to: Use Unsloth () instead of standard
Finds the best models for a task by querying official HF benchmark leaderboards, enriching results with model size data, filtering for what fits on the user's device, and returning a comparison table with benchmark scores.
This skill is for running evaluations against models on the Hugging Face Hub on local hardware. It does not cover: If the user wants to run the same eval remotely on Hugging Face Jobs, hand off to the skill and pass it one of the local scripts in this skill.
Hugging Face Spaces host machine-learning applications. There are 1M+ today; each Space is a git repo. This skill covers creating, building, debugging, and maintaining them. Before anything else: 1. Check the CLI is installed: . If not, .
estimates the required memory for inference, including model weights and an optional KV cache, for Safetensors and GGUF for models on the Hugging Face Hub using HTTP Range requests i.e., without downloading or loading any weights locally. Run with pointing to the Hugging Face Hub repository which w
Transformers.js enables running state-of-the-art machine learning models directly in JavaScript, both in browsers and Node.js environments, with no server required. Use this skill when you need to: The pipeline API is the easiest way to use models. It groups together preprocessing, model inference,
Build and publish a Gradio demo on Hugging Face Spaces that runs inference with a user-provided LoRA. Use whenever someone asks to create, generate, ship, or publish "a Space", "a demo", "a Gradio app", or "a playground" for a LoRA — whether the base model is Qwen-Image, Qwen-Image-Edit, LTX, or ano
"name": "huggingface-skills", "description": "Agent Skills for AI/ML tasks including dataset creation, model training, evaluation, and research paper publishing on Hugging Face Hub", "name": "Hugging Face"
