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huggingface-skills · Flowy

README

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Hugging Face Skills

Hugging Face Skills are definitions for AI/ML tasks like dataset creation, model training, and evaluation. The client plugin marketplaces expose the hf-cli skill as the bootstrap path for core Hub operations; additional workflow skills can be installed on demand with hf skills add <skill-name> or discovered by skill-aware clients over CLI/MCP integrations.

The skills in this repository follow the standardized Agent Skills format.

[!NOTE] Just want to give your agent access to the Hugging Face Hub? Start with hf-cli. It's the recommended first Skill to install: it teaches your agent every hf command (search models, manage datasets and buckets, launch Spaces, run jobs) and is generated from your locally installed CLI so it stays current.

How do Skills work?

In practice, skills are self-contained folders that package instructions, scripts, and resources together for an AI agent to use on a specific use case. Each folder includes a SKILL.md file with YAML frontmatter (name and description) followed by the guidance your coding agent follows while the skill is active.

[!TIP] If your agent doesn't support skills, you can use agentsmd/AGENTS.md directly as a fallback.

The hf-cli skill in this repository is also available through:

Cursor Marketplace (https://cursor.com/marketplace/huggingface)
Codex Plugins Directory (https://developers.openai.com/codex/plugins)

Installation

Hugging Face skills are compatible with Claude Code, Codex, Gemini CLI, and Cursor.

Claude Code

Register the repository as a plugin marketplace:

/plugin marketplace add huggingface/skills

Install the CLI skill:

/plugin install hf-cli@huggingface/skills

To install another Hugging Face skill, use the hf CLI:

hf skills add <skill-name>

Codex

Copy or symlink any skills you want to use from this repository's skills/ directory into one of Codex's standard .agents/skills locations (for example, $REPO_ROOT/.agents/skills or $HOME/.agents/skills) as described in the Codex Skills guide.
Once a skill is available in one of those locations, Codex will discover it using the Agent Skills standard and load the SKILL.md instructions when it decides to use that skill or when you explicitly invoke it.
If your Codex setup still relies on AGENTS.md, you can use the generated agentsmd/AGENTS.md file in this repo as a fallback bundle of instructions.

Gemini CLI

This repo includes gemini-extension.json to integrate with the Gemini CLI.
Install locally:

gemini extensions install . --consent

or use the GitHub URL:

gemini extensions install https://github.com/huggingface/skills.git --consent

See Gemini CLI extensions docs for more help.

Cursor

This repository includes Cursor plugin manifests:

.cursor-plugin/plugin.json
.mcp.json (configured with the Hugging Face MCP server URL)

Install from repository URL (or local checkout) via the Cursor plugin flow. The marketplace entry is intentionally limited to hf-cli; use hf skills add <skill-name> to install additional workflow skills.

For contributors, regenerate manifests with:

./scripts/publish.sh

Skills

This repository contains a few skills to get you started. You can also contribute your own skills to the repository.

Available skills

Name	Description	Documentation
`hf-cli`	Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub.	SKILL.md
`hf-cloud-aws-context-discovery`	Discover the user''s local AWS context (active profile, region, account ID, caller identity) at the start of any AWS task.	SKILL.md
`hf-cloud-python-env-setup`	Set up an isolated Python environment for SageMaker / AWS work, with the right Python version and current boto3.	SKILL.md
`hf-cloud-sagemaker-deployment-planner`	Plan and coordinate the deployment of a model to Amazon SageMaker AI.	SKILL.md
`hf-cloud-sagemaker-iam-preflight`	Ensure a usable SageMaker execution role exists before deploying or training.	SKILL.md
`hf-cloud-sagemaker-production-defaults`	Create a SageMaker endpoint (real-time or async) with autoscaling, CloudWatch alarms, and tagging enabled by default.	SKILL.md
`hf-cloud-serving-image-selection`	Pick the right serving container for a SageMaker model deployment and find its current image URI.	SKILL.md
`hf-mem`	Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub	SKILL.md

Using skills in your coding agent

Once a skill is installed, mention it directly while giving your coding agent instructions:

"Use the HF LLM trainer skill to estimate the GPU memory needed for a 70B model run."
"Use the HF model evaluation skill to launch run_eval_job.py on the latest checkpoint."
"Use the HF dataset creator skill to draft new few-shot classification templates."
"Use the HF paper publisher skill to index my arXiv paper and link it to my model."

Your coding agent automatically loads the corresponding SKILL.md instructions and helper scripts while it completes the task.

Contribute or customize a skill

Copy one of the existing skill folders (for example, hf-datasets/) and rename it.

Update the new folder's SKILL.md frontmatter:

---
name: my-skill-name
description: Describe what the skill does and when to use it
---

# Skill Title
Guidance + examples + guardrails

Add or edit supporting scripts, templates, and documents referenced by your instructions.
Do not add the skill to .claude-plugin/marketplace.json by default. Client marketplaces are intentionally limited to hf-cli; the full Hub CLI marketplace is generated at .claude-plugin/marketplace-internal.json.
Run:
```
./scripts/publish.sh
```
to regenerate and validate all generated metadata.
Reinstall or reload the skill bundle in your coding agent so the updated folder is available.

Marketplace

The .claude-plugin/marketplace.json and .cursor-plugin/marketplace.json files intentionally expose only hf-cli for client marketplace installation. This keeps install-time manifests focused on core Hub operations and points users to hf skills add <skill-name> for the rest of the repository.

The generated .claude-plugin/marketplace-internal.json file contains the full skill list. Publish automation uploads it to the Hub bucket as marketplace.json so hf skills list, hf skills add, and hf skills update continue to see every available skill.

Newer skill-aware integrations can also pull capabilities dynamically. Hugging Face's discovery flow lets clients search skills, MCP servers, and Spaces, while Skills-over-MCP work is standardizing how skills are discovered and consumed through MCP resources.

Additional references

Browse the latest instructions, scripts, and templates directly at huggingface/skills.
Review Hugging Face documentation for the specific libraries or workflows you reference inside each skill.

File tree

193

huggingface/skillsView on GitHub ↗

.claude-plugin/
  marketplace-internal.json
  marketplace.json
  plugin.json
.cursor-plugin/
  marketplace.json
  plugin.json
.github/
  dependabot.yml
  workflows/
    generate-agents.yml
    publish-clawhub-skills.yml
    publish-single-clawhub-skill.yml
    push-evals-leaderboard.yml
    push-hackers-leaderboard.yml
    push-quests.yml
    SECURITY.md
    sync-skills-to-bucket.yml
.gitignore
.mcp.json
agentsmd/
  AGENTS.md
apps/
  evals-leaderboard/
    app.py
    collect_evals.py
    requirements.txt
  hackers-leaderboard/
    app.py
    collect_points.py
    README.md
    requirements.txt
  quests/
    02_evaluate-hub-model.md
    03_publish-hub-dataset.md
    04_sft-finetune-hub.md
    README.md
assets/
  banner.png
fast-agent-log.jsonl
gemini-extension.json
hf-mcp/
  skills/
    hf-mcp/
      SKILL.md
LICENSE
README.md
scripts/
  AGENTS_TEMPLATE.md
  build_skill_distribution.py
  build_skills_index.py
  generate_agents.py
  generate_cursor_plugin.py
  plugin_versions.py
  publish.sh
  run_skills_help.py
  skills_index_queries.json
  UV_RULES.md
skills/
  hf-cli/
    SKILL.md
  hf-cloud-aws-context-discovery/
    SKILL.md
  hf-cloud-python-env-setup/
    requirements.txt
    scripts/
      check_versions.py
      setup_env.py
    SKILL.md
  hf-cloud-sagemaker-deployment-planner/
    SKILL.md
  hf-cloud-sagemaker-iam-preflight/
    references/
      minimum-permissions.json
      trust-policy.json
    scripts/
      check_role.py
      create_role.py
    SKILL.md
  hf-cloud-sagemaker-production-defaults/
    references/
      deployment-template.md
    scripts/
      _common.py
      deploy_async.py
      deploy.py
      invoke_endpoint.py
      teardown.py
    SKILL.md
  hf-cloud-serving-image-selection/
    references/
      model-to-image.md
    scripts/
      mirror_image.py
    SKILL.md
  hf-mem/
    SKILL.md
  huggingface-best/
    SKILL.md
  huggingface-community-evals/
    examples/
      .env.example
      USAGE_EXAMPLES.md
    scripts/
      inspect_eval_uv.py
      inspect_vllm_uv.py
      lighteval_vllm_uv.py
    SKILL.md
  huggingface-datasets/
    SKILL.md
  huggingface-gradio/
    examples.md
    SKILL.md
  huggingface-llm-trainer/
    references/
      gguf_conversion.md
      hardware_guide.md
      hub_saving.md
      local_training_macos.md
      reliability_principles.md
      trackio_guide.md
      training_methods.md
      training_patterns.md
      troubleshooting.md
      unsloth.md
    scripts/
      convert_to_gguf.py
      dataset_inspector.py
      estimate_cost.py
      hf_benchmarks.py
      train_dpo_example.py
      train_grpo_example.py
      train_sft_example.py
      unsloth_sft_example.py
    SKILL.md
  huggingface-local-models/
    references/
      hardware.md
      hub-discovery.md
      quantization.md
    SKILL.md
  huggingface-lora-space-builder/
    references/
      adapting-to-the-lora.md
      base-models/
        krea-2.md
        ltx.md
        qwen-image.md
      creative-mode.md
      tasks.md
      zerogpu-and-publishing.md
    SKILL.md
  huggingface-paper-publisher/
    examples/
      example_usage.md
    references/
      quick_reference.md
    scripts/
      paper_manager.py
    SKILL.md
    templates/
      arxiv.md
      ml-report.md
      modern.md
      standard.md
  huggingface-papers/
    SKILL.md
  huggingface-spaces/
    README.md
    references/
      buckets.md
      debugging.md
      gradio.md
      grants.md
      inference-providers.md
      known-errors.md
      requirements.md
      zerogpu.md
    SKILL.md
  huggingface-tool-builder/
    references/
      baseline_hf_api.py
      baseline_hf_api.sh
      baseline_hf_api.tsx
      find_models_by_paper.sh
      hf_enrich_models.sh
      hf_model_card_frontmatter.sh
      hf_model_papers_auth.sh
    SKILL.md
  huggingface-trackio/
    references/
      alerts.md
      logging_metrics.md
      retrieving_metrics.md
    SKILL.md
  huggingface-vision-trainer/
    references/
      finetune_sam2_trainer.md
      hub_saving.md
      image_classification_training_notebook.md
      object_detection_training_notebook.md
      reliability_principles.md
      timm_trainer.md
    scripts/
      dataset_inspector.py
      estimate_cost.py
      image_classification_training.py
      object_detection_training.py
      sam_segmentation_training.py
    SKILL.md
  huggingface-zerogpu/
    references/
      concurrency.md
      cuda-and-deps.md
      how-quota-works.md
      how-zerogpu-works.md
    SKILL.md
  train-sentence-transformers/
    references/
      base_model_selection.md
      dataset_formats.md
      evaluators_cross_encoder.md
      evaluators_sentence_transformer.md
      evaluators_sparse_encoder.md
      hardware_guide.md
      hf_jobs_execution.md
      losses_cross_encoder.md
      losses_sentence_transformer.md
      losses_sparse_encoder.md
      model_architectures.md
      prompts_and_instructions.md
      training_args.md
      troubleshooting.md
    scripts/
      mine_hard_negatives.py
      train_cross_encoder_distillation_example.py
      train_cross_encoder_example.py
      train_cross_encoder_listwise_example.py
      train_sentence_transformer_distillation_example.py
      train_sentence_transformer_example.py
      train_sentence_transformer_make_multilingual_example.py
      train_sentence_transformer_matryoshka_example.py
      train_sentence_transformer_multi_dataset_example.py
      train_sentence_transformer_static_embedding_example.py
      train_sentence_transformer_with_lora_example.py
      train_sparse_encoder_distillation_example.py
      train_sparse_encoder_example.py
    SKILL.md
  transformers-js/
    references/
      CACHE.md
      CONFIGURATION.md
      EXAMPLES.md
      MODEL_ARCHITECTURES.md
      MODEL_REGISTRY.md
      PIPELINE_OPTIONS.md
      TEXT_GENERATION.md
    SKILL.md
  trl-training/
    SKILL.md