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.
npx skills add waybarrios/opencode-power-pack --skill hf-cloud-python-env-setupgit clone --depth 1 https://github.com/waybarrios/opencode-power-packWrote 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.
[](https://agentmods.dev/skills/waybarrios/opencode-power-pack/hf-cloud-python-env-setup)<a href="https://agentmods.dev/skills/waybarrios/opencode-power-pack/hf-cloud-python-env-setup"><img src="https://agentmods.dev/badge/skills/waybarrios/opencode-power-pack/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.
<a href="https://agentmods.dev/skills/waybarrios/opencode-power-pack/hf-cloud-python-env-setup"><img src="https://agentmods.dev/badge/skills/waybarrios/opencode-power-pack/hf-cloud-python-env-setup.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00043 | $0.01505 |
| Opus 5.5 | $0.00017 | $0.00602 |
| Sonnet 5.5 | $0.00009 | $0.00301 |
| Haiku 4.5 | $0.00004 | $0.00151 |
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 1mo ago.
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.
This is a copy
91% identical to hf-cloud-python-env-setup — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 92 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
boto3orawscli. 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"), nevermodule.__version__. The latter is inconsistent across packages. - The bundled scripts use
boto3directly. 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:
What ships with it
3 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.
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.
- 1mo ago First seen · 92 lines · 43 tokens per session scan A da34a42b94e0
hf-cloud-python-env-setup is a skill published in the GitHub repository waybarrios/opencode-power-pack (539 stars, last pushed 4d ago), licensed MIT. It adds 43 tokens to every session and 1,505 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 91% identical to hf-cloud-python-env-setup, differing in 3 lines, and is treated as a copy.
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