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hf-cloud-aws-context-discovery

@huggingface⭐ 11.1k stars

Discover the user's local AWS context (active profile, region, account ID, caller identity) at the start of any AWS task. Use this skill before any other AWS work — deploying to SageMaker, creating resources, calling AWS APIs, or anything that touches an AWS account. Use it especially when the user has not specified a region or profile explicitly, when they say things like "use my AWS account", "deploy to AWS", "use my profile", or when about to make any AWS CLI or SDK call. Never guess the region or account ID — always use this skill to read it from the local configuration first.

Choose how to use this skill

You do not need every option. Choose the path your AI client supports. The stable page stays the same; versioned files are immutable.

Adopt or update this skill from https://proskills.md/skills/other/huggingface-skills-skills-hf-cloud-aws-context-discovery. First identify the installation path supported by this platform, detect whether it is already installed, compare the installed version with the latest stable release, show me what will change, and only then install or update it. If no native installer is available, use the complete package or source fallback described on the page.

1. Native installer

This listing has no registered native installer command. Use the complete package or source fallback below, depending on what your client supports.

Do not guess an installer command or replace an existing version without reviewing the diff.

2. Complete package recommended

Download the ZIP when available. It includes SKILL.md plus the references, security notes and version metadata.

No complete ProSkills package is published for this listing yet.

3. Prompt-only

Copy the prompt above when the agent can read the stable page or when you want to adopt the workflow without installing a skill.

Need only the instruction file?

Download SKILL.md only if your client requires a single file. The complete ZIP is safer for a full installation because it preserves the references and release context.

No path installs or executes anything by itself. Your agent still needs access to the project files. Before updating, compare the installed version and review the diff.

—/10

// RATINGS

⭐GitHub Stars

⭐⭐⭐⭐⭐ 11.1k on GitHubGitHub ↗

Very popular

🟢ProSkills Score

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📍

Not yet listed on ClawHub or SkillsMP

// README

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

/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, real-time scale-to-zero, 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
`huggingface-best`	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.	SKILL.md
`huggingface-community-evals`	Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware.	SKILL.md
`huggingface-datasets`	Use this skill for Hugging Face Dataset Viewer API workflows that fetch subset/split metadata, paginate rows, search text, apply filters, download parquet URLs, and read size or statistics.	SKILL.md
`huggingface-gradio`	Build Gradio web UIs and demos in Python.	SKILL.md
`huggingface-llm-trainer`	Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure.	SKILL.md
`huggingface-local-models`	Use to select models to run locally with llama.cpp and GGUF on CPU, Mac Metal, CUDA, or ROCm.	SKILL.md
`huggingface-lora-space-builder`	Build and publish a Gradio demo on Hugging Face Spaces for a user-provided LoRA.	SKILL.md
`huggingface-paper-publisher`	Publish and manage research papers on Hugging Face Hub.	SKILL.md
`huggingface-papers`	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.	SKILL.md
`huggingface-spaces`	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.	SKILL.md
`huggingface-tool-builder`	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.	SKILL.md
`huggingface-trackio`	Track and visualize ML training experiments with Trackio.	SKILL.md
`huggingface-vision-trainer`	Trains and fine-tunes

// HOW IT'S BUILT

KEY FILES

skills/hf-cloud-aws-context-discovery/SKILL.mdREADME.md

// REPO STATS

11.1k stars

// SHARE

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