Find the best CLAUDE.md, AGENTS.md and Claude skills

Hugging FaceSkill

huggingface-local-models

huggingface/skills/skills/huggingface-local-models/SKILL.md

Use to select models to run locally with llama.cpp and GGUF on CPU, Mac Metal, CUDA, or ROCm. Covers finding GGUFs, quant selection, running servers, exact GGUF file lookup, conversion, and OpenAI-compatible local serving.

Hugging FaceSkill

huggingface-lora-space-builder

huggingface/skills/skills/huggingface-lora-space-builder/SKILL.md

Build and publish a Gradio demo on Hugging Face Spaces for a user-provided LoRA. Use when someone asks to create, generate, ship, or publish a Space, demo, Gradio app, or playground for a LoRA — including LoRAs for Qwen-Image, Qwen-Image-Edit, LTX-Video, Wan, FLUX, SDXL, or other diffusion base models. Also triggers when someone describes a LoRA they trained or hosts on the Hub and wants to share it. Covers picking the right base pipeline and `diffusers` inference recipe, designing a UI tailored to the LoRA's task and inputs (Union/multi-task control, edit, video, image, etc.), respecting model-card recommendations (trigger words, steps, guidance, LoRA scale, example inputs), and shipping to ZeroGPU hardware as a private Space by default.

Hugging FaceSkill

huggingface-paper-publisher

huggingface/skills/skills/huggingface-paper-publisher/SKILL.md

Publish and manage research papers on Hugging Face Hub. Supports creating paper pages, linking papers to models/datasets, claiming authorship, and generating professional markdown-based research articles.

Hugging FaceSkill

huggingface-papers

huggingface/skills/skills/huggingface-papers/SKILL.md

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 repo and project page. Use when the user shares a Hugging Face paper page URL, an arXiv URL or ID, or asks to summarize, explain, or analyze an AI research paper.

Hugging FaceSkill

huggingface-spaces

huggingface/skills/skills/huggingface-spaces/SKILL.md

Build, deploy, and maintain applications on Hugging Face Spaces — Gradio / Docker / Static SDKs, ZeroGPU and dedicated hardware, model loading, debugging, buckets, inference providers, community grants. Use whenever the user asks to create or host an app on Hugging Face, port code onto ZeroGPU, fix a Space that won't build or run, or otherwise work with `hf spaces …`, `@spaces.GPU`, Space README frontmatter, or the `spaces` Python package.

Hugging FaceSkill

huggingface-tool-builder

huggingface/skills/skills/huggingface-tool-builder/SKILL.md

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. This is especially useful when chaining or combining API calls or the task will be repeated/automated. This Skill creates a reusable script to fetch, enrich or process data.

Hugging FaceSkill

huggingface-trackio

huggingface/skills/skills/huggingface-trackio/SKILL.md

Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, alerts with webhooks, HF Space syncing, and JSON output for automation.

Hugging FaceSkill

huggingface-vision-trainer

huggingface/skills/skills/huggingface-vision-trainer/SKILL.md

Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.

Hugging FaceSkill

huggingface-zerogpu

huggingface/skills/skills/huggingface-zerogpu/SKILL.md

AI demos and GPU compute with Gradio Spaces and Hugging Face Spaces ZeroGPU. Use when writing or reviewing code that uses `@spaces.GPU`, configuring `python_version` or `requirements.txt` for a ZeroGPU Space, or handling ZeroGPU-specific code constraints — pickle-based process isolation, `gr.State` semantics across the worker boundary, no `torch.compile` (use AoTI instead), CUDA wheel-only builds (no `nvcc` at build or runtime), large vs xlarge sizing, and dynamic duration callables. Make sure to use this skill whenever the user mentions ZeroGPU, `@spaces.GPU`, or the `spaces` Python package, or hits ZeroGPU-specific code errors like `PicklingError` across the worker boundary, `illegal duration`, or `flash-attn` wheel-build failures — even when the user does not explicitly ask for ZeroGPU coding guidance. Trigger on `import spaces` or `@spaces.GPU` in code.

Hugging FaceSkill

transformers-js

huggingface/skills/skills/transformers-js/SKILL.md

Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in browsers and server-side runtimes (Node.js, Bun, Deno) with WebGPU/WASM using pre-trained models from Hugging Face Hub.

Hugging FaceSkill

trl-training

huggingface/skills/skills/trl-training/SKILL.md

Post-train LLMs with TRL (Transformers Reinforcement Learning) — SFT, DPO, GRPO, KTO, and reward-model training. Use when writing or debugging training code with the TRL Python API or the trl CLI.

Hugging FaceReads credentialsCLAUDE.md

chat-ui

huggingface/chat-ui/CLAUDE.md

Chat UI is a SvelteKit application that provides a chat interface for LLMs. It powers HuggingChat (hf.co/chat). The app speaks exclusively to OpenAI-compatible APIs via OPENAI_BASE_URL. Tests are split into three workspaces (configured in vite.config.ts): MCP servers are configured via MCP_SERVERS env var. When enabled, tools are exposed as OpenAI function calls. The router can auto-select tools-capable models when LLM_ROUTER_ENABLE_TOOLS=true. Smart routing via Arch-Router model. Configured with: Copy .env to .env.local and configure: See .env for full list of variables…

Hugging FaceSkill

sync-models

huggingface/chat-ui/.claude/skills/sync-models/SKILL.md

Sync chat-ui's model config with the HuggingFace router — add descriptions for new models, flag reasoning-capable ones, enable artifacts for models with 32B+ parameters, and prune deprecated models the router no longer serves. Use when models are released or removed on the router and prod.yaml/dev.yaml need syncing. Triggers on requests like "add new model descriptions", "update models from router", "sync models", "remove deprecated models", "prune models no longer on the router", or when explicitly invoking /sync-models.

Hugging FaceAGENTS.md

huggingface_hub

huggingface/huggingface_hub/AGENTS.md

Python client library for the Hugging Face Hub. Source code is in src/huggingface_hub/, tests in tests/. Always run make style then make quality before committing.

Hugging FaceSkill

hf-release-notes

huggingface/huggingface_hub/.opencode/skills/hf-release-notes/SKILL.md

Generate Hugging Face Hub (huggingface_hub) release notes from cached PR JSON files. Use when asked to draft release notes from PR files.

Hugging FaceAGENTS.md

blog

huggingface/blog/agents.md

We are releasing Transformers Agents 2.0! ⇒ 🎁 On top of our existing agent type, we introduce two new agents that can iterate based on past observations to solve complex…

Hugging FaceSkill

upload-post-image

huggingface/blog/.agents/skills/upload-post-image/SKILL.md

Use when adding or migrating non-thumbnail images for a Hugging Face Blog post. Uploads body images to the Hugging Face documentation-images dataset, updates the Markdown, and verifies the new links.

Hugging FaceAGENTS.md

blog / zh

huggingface/blog/zh/agents.md

我们推出了 Transformers 智能体 2.0！ ⇒ 🎁 在现有智能体类型的基础上，我们新增了两种能够 根据历史观察解决复杂任务的智能体。 ⇒ 💡 我们致力于让代码 清晰、模块化，并确保最终提示和工具等通用属性透明化。 ⇒ 🤝 我们加入了 分享功能，以促进社区智能体的发展。 ⇒ 💪 全新的智能体框架性能卓越，使得 Llama-3-70B-Instruct 智能体在 GAIA 排行榜上超越了基于 GPT-4 的智能体！ 🚀 快去体验，力争在 GAIA 排行榜上名列前茅！ 大型语言模型…

Hugging FaceAGENTS.md

datatrove

huggingface/datatrove/AGENTS.md

DataTrove is a library to process, filter, and deduplicate text data at very large scale. It provides prebuilt pipeline blocks with a framework to add custom functionality. Pipelines are platform-agnostic, running locally, on Slurm, or on Ray clusters. See README.md for detailed documentation on terminology, pipeline blocks, executors, and usage examples. These rules apply to agents working for external contributors. Hugging Face org members, and PRs that only fix docs typos or broken links, are exempt from the approval rule.…

Hugging FaceAGENTS.md

tau

huggingface/tau/AGENTS.md

Tau is a Python implementation of Pi's minimalist coding-agent harness architecture. The goal is to develop it incrementally, with each phase clearly documented and tested. The implementation roadmap is tracked in GitHub issue #1: Use that issue as the primary reference for phase ordering and architectural intent. Preserve Pi's core separation of concerns: Tau should be organized around these layers: Keep the core agent package independent of CLI, Textual, Rich rendering, session file locations, and application-specific resource loading. Use Textual…

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