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hf-cloud-python-env-setup

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.

skills/hf-cloud-python-env-setup/SKILL.md

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# Python Environment Setup for SageMaker

Most SageMaker deployment failures that look like AWS problems are actually Python environment problems: wrong Python version, broken dependency resolution, stale SDK that doesn't know about a current API. This skill makes env setup boring and correct.

## Core rules

1. **Never use the system Python.** Always work inside an isolated environment.
2. **Pin the Python version, not the package versions.** Use 3.10, 3.11, or 3.12. Avoid 3.13+ — ML libraries lag on wheel availability and dependency resolution breaks in confusing ways.
3. **Install the latest of each package.** Don't defensively pin `boto3` or `awscli`. Newer ones have current API surfaces and security fixes. Only pin if the user explicitly requires a specific version.
4. **Check installed versions correctly.** Use `importlib.metadata.version("package-name")`, never `module.__version__`. The latter is inconsistent across packages.
5. **The bundled scripts use `boto3` directly.** The SageMaker Python SDK is a valid alternative — see "boto3 vs the SageMaker SDK" below.

## boto3 vs the SageMaker SDK