1,753 boosters for "ai" — open source, verified from GitHub, ready to install
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.
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
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.
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
Trackio is an experiment tracking library for logging and visualizing ML training metrics. It syncs to Hugging Face Spaces for real-time monitoring dashboards. Use in your training scripts to log metrics: → See references/logging_metrics.md for setup, TRL integration, and configuration options.
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
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
Rules and patterns for ML demos on Hugging Face Spaces with ZeroGPU hardware. Covers , duration and quota tuning, process isolation, the CUDA availability model, concurrency safety, and CUDA build constraints. This skill is for Gradio SDK Spaces using ZeroGPU hardware. Docker and Static Spaces canno
"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"
A skill that enables researchers and AI engineers to publish, manage, and link research papers on Hugging Face Hub with arXiv integration and professional markdown generation. Useful for academics and ML practitioners looking to streamline paper publication workflows.
A skill that generates reusable command-line scripts for automating Hugging Face API interactions, useful for developers who need to repeatedly fetch, process, or chain API calls.
Trackio is an ML experiment tracking library that integrates with Hugging Face to log metrics, visualize training progress, and trigger alerts during model development. It's useful for ML engineers and researchers who need real-time monitoring and experiment management.
A skill for fine-tuning and training language models on Hugging Face's cloud GPU infrastructure using TRL, supporting SFT, DPO, GRPO methods and GGUF conversion for local deployment. Developers and ML engineers working with cloud-based model training benefit from this comprehensive guidance.
A multi-agent orchestration system for Claude Code that enables complex agentic workflows through a CLI and MCP server interface. Useful for developers building sophisticated AI-powered automation and coordinated agent systems within Claude's ecosystem.
1. Fetch homepage HTML (curl or WebFetch) 2. Detect business type (SaaS, Local, E-commerce, Publisher, Agency, Other) 3. Extract key pages from sitemap.xml or internal links (up to 50 pages)
Generates per-category catalog JSON files from the npm package's public API. Each catalog lists all UseCase and EventHandler abstractions in a category with their resolved source file paths. LLMs read source files on demand for exact, up-to-date types — no enrichment phase needed. 1. Discovers all
"description": "Developer experience essentials: GitHub Actions debugging, conversation cloning/half-cloning, context handoffs, and Reddit research via Gemini CLI", "version": "0.14.12", "email": "yoyoyosss@wearehackerone.com"
"description": "Agent Team & Skill Architect — Meta-skill that designs agent teams, defines specialized agents, and generates skills for your domain/project. 도메인/프로젝트에 맞는 하네스를 구성하고, 전문 에이전트를 정의하며, 에이전트가 사용할 스킬을 생성하는 메타 스킬.", "url": "https://github.com/revfactory" "homepage": "https://github.com/revf
Agents are the agentic logic layer of OpenJarvis. They determine how a query is processed -- whether it goes directly to a model, through a tool-calling loop, via ReAct reasoning, CodeAct code execution, recursive decomposition, or an external agent runtime. All agents implement the ABC and are reg
"name": "cursor-talk-to-figma-mcp", "description": "Cursor Talk to Figma MCP", "module": "dist/server.js",
IntentKit is an open-source, self-hosted cloud agent cluster that manages a collaborative team of AI agents for you.
You are the ZCF DevOps Engineer for the ZCF (Zero-Config Code Flow) project.
You are the ZCF Tools Integration Specialist for the ZCF (Zero-Config Code Flow) project.
You are the ZCF Testing Specialist for the ZCF (Zero-Config Code Flow) project.
