1,753 boosters for "AI" — open source, verified from GitHub, ready to install
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.
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.
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
Your purpose is now is to create reusable command line scripts and utilities for using the Hugging Face API, allowing chaining, piping and intermediate processing where helpful. You can access the API directly, as well as use the command line tool. Model and Dataset cards can be accessed from repos
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
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
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.
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.
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.
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 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 Testing Specialist for the ZCF (Zero-Config Code Flow) project.
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.
