Hugging FaceSkilladd-or-fix-type-checking
huggingface/transformers/.ai/skills/add-or-fix-type-checking/SKILL.md
Fixes broken typing checks detected by ty, make typing, or make check-repo. Use when typing errors appear in local runs, CI, or PR logs.
Claude skills and agent skills
Skills real projects publish on GitHub, most starred first. Each one says what it will make an agent do before you copy it.
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Hugging FaceSkillHugging FaceSkillcustom-blocks
huggingface/diffusers/.ai/skills/custom-blocks/SKILL.md
Use when the user has written (or wants to write) a `ModularPipelineBlocks` subclass in a local Python file and needs to package it into a Hub-uploadable directory. Covers the workflow from a single `block.py` file to a published custom-block repo that consumers can load via `ModularPipeline.from_pretrained(<repo>, trust_remote_code=True)`.
Hugging FaceSkilldiffusers-cli
huggingface/diffusers/.ai/skills/diffusers-cli/SKILL.md
Use when the user wants to run a diffusers pipeline from a terminal (one-off generation, batch jobs, smoke-testing a new model), run on HF Sandbox hardware via `--remote`, introspect a pipeline's input schema before calling it, or attach a LoRA at inference time. Prefer this over writing ad-hoc Python scripts for generation tasks.
Hugging FaceSkillmodel-integration
huggingface/diffusers/.ai/skills/model-integration/SKILL.md
Use when adding a new model or pipeline to diffusers, setting up file structure for a new model, converting a pipeline to modular format, or converting weights for a new version of an already-supported model.
Hugging FaceSkillself-review
huggingface/diffusers/.ai/skills/self-review/SKILL.md
Use before opening a PR, or whenever asked to self-review a diffusers contribution. Applies the same rubric as the `@claude` CI (checks the diff against references/review-rules.md, traces call paths for dead code). Reports findings grouped by severity, flagging what to fix before submitting (blocking issues + dead code) vs what to leave for the actual review. Report-only — does not edit files.
Hugging FaceSkillpeft-method-changes
huggingface/peft/.ai/skills/peft-method-changes/SKILL.md
Use when modifying an existing PEFT method (its forward method, parameters/buffers, state_dict handling, or config defaults) to ensure that existing checkpoints keep working.
Hugging FaceSkillupdate-paper-index
huggingface/trl/.agents/skills/update-paper-index/SKILL.md
Add or review a paper entry in TRL's paper index. Use when a PR implements a method, algorithm, or training approach from a research paper, or when reviewing such a PR.
Hugging FaceSkilltrl-training
huggingface/trl/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 FaceSkilltrain-sentence-transformers
huggingface/sentence-transformers/skills/train-sentence-transformers/SKILL.md
Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder, dense or static embedding model for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), `CrossEncoder` (reranker, pair scoring for two-stage retrieval / pair classification), `SparseEncoder` (SPLADE, sparse embedding model for learned-sparse retrieval), and `MultiVectorEncoder` (ColBERT / late-interaction, per-token embeddings scored with MaxSim). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing. Use for any sentence-transformers training task.
Hugging FaceSkilltransformers-js
huggingface/transformers.js/.ai/skills/transformers-js/SKILL.md
Run state-of-the-art machine learning models directly in JavaScript. `@huggingface/transformers` supports text, vision, audio, and multimodal tasks in browsers and Node.js / Bun / Deno, with WebGPU or WASM execution.
Hugging FaceSkillhf-mcp
huggingface/skills/hf-mcp/skills/hf-mcp/SKILL.md
Use Hugging Face Hub via MCP server tools. Search models, datasets, Spaces, papers. Get repo details, fetch documentation, run compute jobs, and use Gradio Spaces as AI tools. Available when connected to the HF MCP server.
Hugging FaceSkillhf-cli
huggingface/skills/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`.
Hugging FaceSkillhf-cloud-python-env-setup
huggingface/skills/skills/hf-cloud-python-env-setup/SKILL.md
Set up an isolated Python environment for SageMaker / AWS work, with the right Python version and current boto3. Use this skill whenever Python code will be executed for a SageMaker deployment, training job, or any AWS automation — including when about to run `pip install`, when about to invoke `boto3`, when creating or activating a virtualenv, or when the user asks to "set up the environment". Never use system Python and never `pip install` into it. Always isolate. This skill prevents the most common failure modes: wrong Python version, dependency conflicts, and stale SDKs.
Hugging FaceSkillhf-cloud-sagemaker-deployment-planner
huggingface/skills/skills/hf-cloud-sagemaker-deployment-planner/SKILL.md
Plan and coordinate the deployment of a model to Amazon SageMaker AI. Use this skill whenever the user wants to deploy, host, serve, or expose a model on SageMaker or AWS — including phrases like "deploy a model", "host this LLM on AWS", "serve this embedding model", "deploy a reranker", "deploy a text-to-image / diffusion model", "host this for async inference", "create an endpoint", "serve my fine-tuned model", or any request that involves making a model available for inference on AWS. Use this even when the user is vague (e.g. "I just want to get this running on AWS, you figure it out"). Works for text-generation LLMs, embedding models, rerankers, classifiers, text-to-image / diffusion models — picks the right serving stack and chooses between real-time and async inference. This is the entry-point skill for SageMaker deployment work — it asks clarifying questions, picks a deployment pathway, and coordinates the other deployment skills.
Hugging FaceSkillhf-cloud-sagemaker-iam-preflight
huggingface/skills/skills/hf-cloud-sagemaker-iam-preflight/SKILL.md
Ensure a usable SageMaker execution role exists before deploying or training. Use this skill whenever about to create a SageMaker endpoint, model, training job, or any resource that requires an execution role. Use it especially when the user has not provided a role ARN explicitly, when scripts are about to call `iam:CreateRole`, or when an AccessDenied error mentions an IAM action. Never blindly call `iam:CreateRole` — always check for existing roles first. This skill prevents the most common SageMaker deployment failure: trying to create IAM resources from an SSO principal that has no IAM write permissions.
Hugging FaceSkillhf-cloud-sagemaker-production-defaults
huggingface/skills/skills/hf-cloud-sagemaker-production-defaults/SKILL.md
Create a SageMaker endpoint (real-time, real-time scale-to-zero, or async) with autoscaling, CloudWatch alarms, and tagging enabled by default. Use this skill whenever about to create a SageMaker endpoint, write deployment code that calls `create_endpoint`, or finalize a deployment after the image URI and IAM role are known. Provides deploy.py for real-time endpoints, deploy_ic.py for real-time endpoints that scale to zero instances via inference components, and deploy_async.py for async endpoints (also scale-to-zero). This is the last step in the SageMaker deployment workflow. Never generate a bare `create_endpoint` call without these defaults — endpoints without autoscaling or alarms are demos, not deployments.
Hugging FaceSkillhf-cloud-serving-image-selection
huggingface/skills/skills/hf-cloud-serving-image-selection/SKILL.md
Pick the right serving container for a SageMaker model deployment and find its current image URI. Use this skill whenever about to deploy a model to a SageMaker endpoint and an image URI needs to be chosen — including when the user says "deploy this LLM", "host this HuggingFace model", "serve this fine-tuned model", "deploy this embedding model", "host a reranker", "serve a sentence-transformers model", or when about to hardcode any container URI in deployment code. HuggingFace-curated Deep Learning Containers are ALWAYS preferred: HuggingFace vLLM (LLMs and generative rerankers), HuggingFace vLLM-Omni (multimodal), TEI (embeddings/cross-encoder rerankers), HF Inference Toolkit (other transformers). Generic images (AWS vLLM, DJL-LMI, SGLang) are used only when no HuggingFace image is compatible — never merely because they carry a newer version. Never hardcode a container URI from memory and never default to TGI. Prevents stale-image failures and wrong-region URIs.
Hugging FaceSkillhf-mem
huggingface/skills/skills/hf-mem/SKILL.md
Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub
Hugging FaceSkillhuggingface-best
huggingface/skills/skills/huggingface-best/SKILL.md
Use when the user asks about finding the best, top, or recommended model for a task, wants to know what AI model to use, or wants to compare models by benchmark scores. Triggers on: "best model for X", "what model should I use for", "top models for [task]", "which model runs on my laptop/machine/device", "recommend a model for", "what LLM should I use for", "compare models for", "what's state of the art for", or any question about choosing an AI model for a specific use case. Always use this skill when the user wants model recommendations or comparisons, even if they don't explicitly mention HuggingFace or benchmarks.
Hugging FaceSkillhuggingface-community-evals
huggingface/skills/skills/huggingface-community-evals/SKILL.md
Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. Use for backend selection, local GPU evals, and choosing between vLLM / Transformers / accelerate. Not for HF Jobs orchestration, model-card PRs, .eval_results publication, or community-evals automation.
About skills
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A folder with a SKILL.md file: a name, a description of when to use it, and instructions. Claude loads a skill only when the task matches its description.
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