What does this skill do, and when should you use it?
This skill prepares isolated Python environments for SageMaker deployments, training jobs, and AWS automation. It recommends Python 3.10, 3.11, or 3.12 and installs current boto3 and awscli releases. It provides commands for environment creation, version verification, and troubleshooting common interpreter and dependency failures. It also explains when direct boto3 usage differs from using the SageMaker Python SDK.
Runs the bundled setup_env.py script, detects uv or falls back to the standard-library venv module, creates .venv, and installs requirements.txt. It supports custom environment directories and Python versions, then directs the agent to invoke the environment's interpreter explicitly. The check_versions.py helper reports boto3, botocore, awscli, or arbitrary package versions using importlib.metadata.version(). The guidance also addresses Python-version incompatibilities, mixed interpreters, PowerShell quoting, stale boto3 APIs, SDK credential issues, and generative reranker container selection.
- A developer preparing an isolated environment before deploying a model to SageMaker.
- An ML engineer starting a SageMaker training job that needs a compatible Python and boto3 setup.
- An AWS automation author about to run pip install or invoke boto3.
- A user diagnosing dependency resolution failures, missing modules, or boto3 unknown-parameter errors.
- A team using the SageMaker Python SDK that needs to understand how it relates to the bundled boto3 scripts.
How do you install this skill?
- requirements.txt is not included in the supplied source, so its actual dependencies, version ranges, and security posture cannot be verified statically.
- The scripts install the latest boto3/awscli, which can cause non-reproducible builds or compatibility drift; reviewed lockfiles are advisable for production.
- Dependency installation and AWS workflows depend on external networks and cloud services; no mainland-China reachability, mirror, or offline procedure is documented.
- The skill can create or recreate .venv without specifying user confirmation, backup, or rollback procedures.
- Shell / CLI
- Network access
- Local filesystem
Python 3.10, 3.11, or 3.12boto3awscliuv or Python venv
For Codex or another Agent Skills-compatible client, copy or symlink skills/hf-cloud-python-env-setup/ into the project's .agents/skills directory or another standard .agents/skills location. The README documents collection-level installation and does not provide a separate install command for this individual skill.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/huggingface/skills.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/skills/hf-cloud-python-env-setup" ~/.claude/skills/
rm -rf "$tmp"Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
Once installed, send your agent any of these to trigger it:
- Set up an isolated Python environment for my SageMaker deployment and verify boto3, botocore, and awscli.
Ask the coding agent something like: “Set up an isolated Python environment for my SageMaker deployment and verify boto3, botocore, and awscli.” Run python3 scripts/setup_env.py on macOS/Linux, or python scripts/setup_env.py on Windows. Invoke scripts explicitly with .venv/bin/python deploy.py, or .venv\Scripts\python.exe deploy.py on Windows. Verify versions with .venv/bin/python scripts/check_versions.py; additional package names may be supplied.
What are this skill's strengths and limitations?
- Strong guardrails against system-Python contamination and dependency conflicts.
- Supports uv and standard-library venv with macOS, Linux, and Windows command forms.
- Promotes current boto3 APIs and reliable package-version inspection.
- Covers concrete SageMaker SDK, generative-reranker, and SSO assumed-role caveats.
- Focused on SageMaker and AWS Python workflows rather than general Python project management.
- The default latest-package policy may not suit projects that require reproducible version pins.
- The source provides no test-suite information or independent platform test results.
- Environment creation and package installation require shell, filesystem, and network access.
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| SageMaker Python Environment Setup this page | 49 · Use with care | ★ 11k | 19d ago | Apache-2.0 |
| SageMaker IAM Preflight | 41 · Not recommended | ★ 11k | 19d ago | Apache-2.0 |
| SageMaker Production Defaults | 46 · Use with care | ★ 11k | 19d ago | Apache-2.0 |
| Creator Registry | 61 · Recommended | ★ 3k | 13d ago | Apache-2.0 |
| Dependency Refresh Playbook | 59 · Recommended | ★ 4.5k | 23d ago | MIT |
Compared with manual venv creation, setup_env.py detects uv, enforces supported Python versions, installs dependencies, and is idempotent. Manual uv venv or python3.12 -m venv remains documented. The SageMaker Python SDK v3 is the named alternative to direct boto3 usage, although the bundled scripts themselves require boto3.
How did FollowSkills review this skill?
The evidence shows isolated-environment requirements, a prohibition on modifying system Python, and use of stdlib venv or uv. No obvious malware or credential-theft logic is present. Points are deducted because dependencies are unpinned and installed from external package sources, user confirmation is not required, network and supply-chain risks are not disclosed, and data-flow, permission boundaries, and rollback are unspecified.
SKILL.md, setup_env.py, and check_versions.py are broadly consistent about version checks, cross-platform interpreter paths, environment reuse, and installation failure reporting. Points are deducted because execution was not performed, requirements.txt is absent from the supplied material, no key-path tests are shown, and diagnostics for uv, Python availability, or package-source failures are limited. Static calibration caps this at 10.
The audience, SageMaker/AWS scenarios, Python 3.10–3.12 range, and Windows/macOS/Linux usage are reasonably clear. Points are deducted because non-SageMaker boundaries, exclusion conditions, and input/output contracts are under-specified; documentation is English-only; and PyPI/uv and AWS ecosystem reachability in mainland China is not evidenced.
The skill has frontmatter, layered guidance, usage examples, verification commands, troubleshooting, and an Apache-2.0 license; the README documents repository-level installation and generation. Points are deducted because the skill lacks its own version, changelog, named maintenance responsibility, or explicit update path; requirements.txt is not supplied; and dependency and limitation disclosures are incomplete.
The skill supplies scripts to create an environment, install boto3/awscli, and inspect versions, which plausibly covers the core setup task and makes explicit interpreter invocation useful. Points are deducted because the missing dependency file prevents completeness verification, latest-unpinned installs can drift, no representative execution output is provided, and comparative benefit over manual setup is not demonstrated. Static calibration caps this at 7.
The supplied source includes auditable Python implementations, concrete commands, and failure messages, while the README and CI show repository-level generated-artifact validation. Points are deducted because no committed tests or real execution evidence cover this skill's key paths, the CI shown does not test setup_env.py/check_versions.py behavior, and this review did not execute anything. Static calibration caps this at 5.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →