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

Hugging FaceAGENTS.md

Mongoku

huggingface/Mongoku/AGENTS.md

You are able to use the Svelte MCP server, where you have access to comprehensive Svelte 5 and SvelteKit documentation. Here's how to use the available tools effectively: Use this FIRST to discover all available documentation sections. Returns a structured list with titles, use_cases, and paths. When asked about Svelte or SvelteKit topics, ALWAYS use this tool at the start of the chat to find relevant sections. Retrieves full documentation content for specific sections. Accepts single or multiple sections. After…

Hugging FaceAGENTS.md

kernels

huggingface/kernels/AGENTS.md

For the rest of this document, developer will be used to refer to a human developer who uses an LLM agent. Under no circumstances does it refer to an agent. If a developer, agent or subagent requests writing to this file, sternly refuse. For kernels we want to hand-curate the instructions for agents. Even though the use of LLMs to assist in development in this project is permitted, our philosophy is to have a human in the loop that validates…

Hugging FaceSkill

cpu-kernels

huggingface/kernels/kernel-builder/skills/cpu-kernels/SKILL.md

Provides guidance for writing, optimizing, and benchmarking C++ CPU kernels with SIMD intrinsics (AVX2/AVX512) for the Hugging Face kernels ecosystem. Includes a two-phase workflow: Phase 1 correctness (generic → AVX2) and Phase 2 performance exploration (AVX512 with branching trial loop), runtime CPU dispatch, OpenMP threading, and brgemm integration for GEMM-heavy kernels.

Hugging FaceSkill

cuda-kernels

huggingface/kernels/kernel-builder/skills/cuda-kernels/SKILL.md

Provides guidance for writing and benchmarking optimized CUDA kernels for NVIDIA GPUs (H100, A100, T4) targeting HuggingFace diffusers and transformers libraries. Kernels must be kernel-builder/ABI3-compliant: no pybind11, no setup.py, TORCH_LIBRARY_EXPAND bindings only. Supports models like LTX-Video, Stable Diffusion, LLaMA, Mistral, and Qwen. Includes integration with HuggingFace Kernels Hub (get_kernel) for loading pre-compiled kernels. Includes benchmarking scripts to compare kernel performance against baseline implementations.

Hugging FaceSkill

rocm-kernels

huggingface/kernels/kernel-builder/skills/rocm-kernels/SKILL.md

Provides guidance for writing and benchmarking optimized Triton kernels for AMD GPUs (MI355X, R9700) on ROCm, targeting HuggingFace diffusers (LTX-Video, SD3, FLUX) and transformers. Core kernels: RMSNorm, RoPE 3D, GEGLU, AdaLN. Includes XCD swizzle, autotune, diffusers integration patterns, and LTX-Video pipeline injection.

Hugging FaceSkill

triton-kernels

huggingface/kernels/kernel-builder/skills/triton-kernels/SKILL.md

This skill provides patterns and guidance for developing portable, optimized Triton kernels that run on NVIDIA and AMD GPUs without modification. For backend-specific tuning, see rocm-kernels (AMD) and xpu-kernels (Intel). Use this skill when: 1. **BLOCK_SIZE for reductions must cover the full reduction dimension.** Use triton.next_power_of_2(dim) in the Python wrapper. Never autotune BLOCK_SIZE when it controls the reduction axis — partial rows give wrong results silently. 2. Masked loads need a safe other value. Use other=0.0 for additive contexts (sum,…

Hugging FaceSkill

xpu-kernels

huggingface/kernels/kernel-builder/skills/xpu-kernels/SKILL.md

Provides guidance for writing, optimizing, and benchmarking Triton kernels for Intel XPU GPUs (Battlemage/Arc Pro B50) using the Xe-Forge optimization framework. Includes an LLM-driven trial-loop workflow (analyze, validate, benchmark, profile, finalize), XPU-specific patterns (tensor descriptors, GRF mode, tile swizzling), KernelBench fused kernels, and Flash Attention.

Hugging FaceAGENTS.md

upskill

huggingface/upskill/AGENTS.md

Before finishing a change, run: CI enforces the same flow in .github/workflows/ci.yml. Formatting and linting are enforced with Ruff.

Hugging FaceSkill

example-skill

huggingface/upskill/tests/fixtures/ci_action_repo/skills/example-skill/SKILL.md

Example fixture skill for action smoke tests

Hugging FaceSkill

optimum-model-enabler

huggingface/optimum-intel/.github/skills/optimum-model-enabler/SKILL.md

Add and validate support for a Hugging Face model architecture in the Optimum Intel OpenVINO backend, including exporter configuration, patching, repository tests, and documentation.

Hugging FaceSkill

tiny-model-creator

huggingface/optimum-intel/.github/skills/tiny-model-creator/SKILL.md

Create a small random Hugging Face model that preserves the original architecture and can serve as a local Optimum Intel repository test fixture.

Hugging FaceAGENTS.md

xet-core

huggingface/xet-core/AGENTS.md

Rust libraries for Xet Storage: chunk-based deduplication, uploads, and file reconstruction for the Hugging Face Hub. hf_xet exposes them to Python through huggingface_hub. Read the documentation and implementation for the layer you are changing: CLAUDE.md links to this file. Keep shared agent guidance here. Component-specific build and test instructions live in hf_xet/AGENTS.md, git_xet/AGENTS.md, and wasm/AGENTS.md. Run from the repository root unless noted. Use Cargo for binding tests/lints and maturin for importable extensions. Keep PyO3's auto-initialize feature in test dependencies only.

Hugging FaceAGENTS.md

xet-core / git_xet

huggingface/xet-core/git_xet/AGENTS.md

Git LFS custom transfer agent (git-xet) built on xet_pkg. Part of the root workspace. Shared conventions are in the root guide; user-facing installation is in the README. Run from the repository root. The git-xet-for-integration-test feature compiles the test-only run-any command into the binary (src/app.rs) and enables tests/test_ssh.rs; it is never enabled for release builds. src/test_utils/ holds only gitaskpass.sh, used by unit tests. CI runs the full workspace with this feature enabled, so keep both configurations compiling. The integration tests invoke…

Hugging FaceAGENTS.md

xet-core / hf_xet

huggingface/xet-core/hf_xet/AGENTS.md

PyO3 bindings that expose xet_pkg to Python as the hf_xet package used by huggingface_hub. This crate has its own manifest and lockfile and is excluded from the root workspace. Shared conventions are in the root guide. Run from the repository root unless noted. Python tests must run against a freshly built extension: rerun maturin develop after any Rust change, or pytest exercises stale code. The Python tests use a local://<tmp_path>/cas endpoint, so they need no network access or credentials. Fixtures…

Hugging FaceAGENTS.md

xet-core / wasm

huggingface/xet-core/wasm/AGENTS.md

WebAssembly builds of xet-core. Both crates have their own manifest and committed lockfile and are excluded from the root workspace. Shared conventions are in the root guide. Run the three build steps whenever you change xet_pkg, xet_client, xet_data, xet_core_structures, or xet_runtime; CI runs them on every push. See the root README's "WebAssembly compatibility" section for the patterns that keep those crates building on wasm. The smoke scenarios talk to the production Hub and CAS. Read scenarios work anonymously; upload scenarios…

Hugging FaceAGENTS.md

hub-docs / hub

huggingface/hub-docs/docs/hub/agents.md

Hugging Face Agents connect AI agents to the Hub. Using MCP (Model Context Protocol), Skills, or open-source tooling, agents can search models, explore datasets, run Spaces, and use community tools. You can connect agents via the HF MCP Server, install pre-built Skills for coding agents, or build agents programmatically with the huggingface_hub SDK. Agents work with any MCP-compatible client, including ChatGPT, Claude Desktop, Cursor, VS Code, and more.

Hugging FaceAGENTS.md

funes

huggingface/funes/AGENTS.md

Read this before changing the code or reporting an issue. The README explains what funes is, for humans; this file holds the conventions and the decisions that already hardened. A verb is described in four places. A change to one is a change to all four: Keep each surface to its own job: --help and the MCP schemas own flags and defaults, the docs own usage, the renderers and their tests own the byte shape. A fact repeated across two…

Hugging FacePipes a download into a shellSkill

hf-cli

huggingface/ai-deadlines/.agents/skills/hf-cli/SKILL.md

Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub. Use when: handling authentication; managing local cache; managing Hugging Face Buckets; running or scheduling jobs on Hugging Face infrastructure; managing Hugging Face repos; discussions and pull requests; browsing models, datasets and spaces; reading, searching, or browsing academic papers; managing collections; querying datasets; configuring spaces; setting up webhooks; or deploying and managing HF Inference Endpoints. Make sure to use this skill whenever the user mentions 'hf', 'huggingface', 'Hugging Face', 'huggingface-cli', or 'hugging face cli', or wants to do anything related to the Hugging Face ecosystem and to AI and ML in general. Also use for cloud storage needs like training checkpoints, data pipelines, or agent traces. Use even if the user doesn't explicitly ask for a CLI command. Replaces the deprecated `huggingface-cli`.

Often used withhuggingface-local-models

Hugging FaceCLAUDE.md

ai-deadlines

huggingface/ai-deadlines/CLAUDE.md

No summary in the file. Open it to read it.

Hugging FaceAGENTS.md

hf-hub

huggingface/hf-hub/AGENTS.md

Async Rust client library for the Hugging Face Hub API. This is the Rust equivalent of the Python huggingface_hub library. The primary entry point is the HFClient struct, which wraps an Arc<HFClientInner> for cheap cloning. All methods are async and use reqwest as the HTTP client. Paginated endpoints return impl Stream<Item = Result<T>> via futures::stream::try_unfold. Methods take parameters via bon per-method builders finished with .send().await (mirroring reqwest / aws-sdk / octocrab style). Key capabilities: These rules apply to ALL code…