What is hf-cloud-python-env-setup?

A setup guide for creating an isolated Python environment for AWS machine-learning work. It covers choosing a supported Python version and installing current AWS libraries such as boto3.

Use it before running Python code for SageMaker deployments, training jobs, or AWS automation.

It reduces deployment failures caused by using the system Python, an unsupported Python version, or outdated libraries.

hf-cloud-python-env-setup — Skill for Codex by PracticalSwan

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add PracticalSwan/agent-skills --skill hf-cloud-python-env-setup

Clone the repo

git clone --depth 1 https://github.com/PracticalSwan/agent-skills

Made for: Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/practicalswan/agent-skills/hf-cloud-python-env-setup/github.svg)](https://agentmods.dev/skills/practicalswan/agent-skills/hf-cloud-python-env-setup)

Your own site

<a href="https://agentmods.dev/skills/practicalswan/agent-skills/hf-cloud-python-env-setup"><img src="https://agentmods.dev/badge/skills/practicalswan/agent-skills/hf-cloud-python-env-setup/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/practicalswan/agent-skills/hf-cloud-python-env-setup"><img src="https://agentmods.dev/badge/skills/practicalswan/agent-skills/hf-cloud-python-env-setup.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 128 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,143 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector warn 7 Sept 2026

SkillSpector: 1 finding, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

high Prompt Injection · line 94
Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.

Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00128	$0.02143
Opus 5	$0.00064	$0.01071
Sonnet 5	$0.00026	$0.00429
Haiku 4.5	$0.00013	$0.00214

Measured 3d ago against content hash 4d62d9f2c0f3, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

hf-cloud-python-env-setup scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/check_versions.py, scripts/setup_env.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

hf-cloud-python-env-setup/SKILL.md · 142 lines

How it starts

The opening of the file, as written. The whole thing — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

Never use the system Python. Always work inside an isolated environment.
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.
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.
Check installed versions correctly. Use importlib.metadata.version("package-name"), never module.__version__. The latter is inconsistent across packages.
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

The bundled deploy scripts (deploy.py, deploy_async.py, teardown.py) use boto3 directly and read image URIs from AWS's published Deep Learning Containers catalog. That fits this workflow's explicit-stages design — each skill produces a concrete value (region, role ARN, image URI) that the next one consumes — and boto3 is the stable underlying API client.

The SageMaker Python SDK (v3) is fine to use when the user prefers it or their project already does. Since PR #5960 (June 2026), ModelBuilder auto-routes HuggingFace models to the current containers (text-generation → HuggingFace vLLM, multimodal → vLLM-Omni, embeddings → TEI). Don't avoid the SDK over stale-image or wrong-container concerns — that routing is fixed.

Two specific SDK cases that still need care:

Read the full file on GitHub · 142 lines

Files

What ships with it

5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

3d ago Changed 4d62d9f2c0f3
4d ago Changed d279cd32e9cf
7d ago First seen · 142 lines · 128 tokens per session scan A 158d96f4f55c

Subscribe to this mod's changes

hf-cloud-python-env-setup is a skill published in the GitHub repository PracticalSwan/agent-skills (14 stars, last pushed 3d ago), licensed MIT. It adds 128 tokens to every session and 2,143 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

Cloud DevOps and cloud Languages Python env hf hugging-face setup