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 agentmods add skills/waybarrios/opencode-power-pack/hf-cloud-python-env-setupnpx skills add waybarrios/opencode-power-pack --skill hf-cloud-python-env-setupgit clone --depth 1 https://github.com/waybarrios/opencode-power-packWhat 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 | $0.00043 | $0.01505 |
| Opus 5 | $0.00022 | $0.00753 |
| Sonnet 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 yesterday.
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.
- yesterday 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 (489 stars, last pushed 8d ago), licensed MIT. It adds 43 tokens to every session and 1,505 once invoked, about $0.0002 per session on Opus 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.
Other skills, from other repositories
dingtalk_channel_connect
Use a headed browser to automatically complete DingTalk channel integration for QwenPaw. Applicable when the user mentions DingTalk, developer console, Client ID, Client Secret, bot, Stream mode, binding or configuring a channel. Supports pausing when a login page is detected and resuming after the user logs in.
make_plan
For external plan request scenarios, guides the Agent to request a clear, actionable, step-by-step plan from a stronger Agent via listagents and chatwithagent, emphasizing that the plan is executed by the requester, not by the consulted Agent.
当用户需要对PDF文件进行任何操作时，请使用此技能。包括从 PDF 中读取或提取文本/表格、合并多个 PDF、拆分 PDF、旋转页面、添加水印、创建新PDF、填写PDF表单、加密/解密 PDF、提取图片，以及对扫描版 PDF 进行 OCR 使其可搜索。如果用户提到 .pdf 文件或要求生成 PDF，请使用此技能。.
wiki-lint
Audit and maintain the health of the Obsidian wiki. Use this skill when the user wants to check their wiki for issues, find orphaned pages, detect contradictions, identify stale content, fix broken wikilinks, or perform general maintenance on their knowledge base. Also triggers on "clean up the wiki", "what needs…
wiki-status
Show the current state of the wiki — what's been ingested, what's pending, and the delta between sources and wiki content. Use this skill when the user asks "what's the status", "how much is ingested", "what's left to process", "show me the delta", "what changed since last ingest", "wiki dashboard", or wants an…
wiki-dashboard
Create dynamic, queryable dashboard views of the Obsidian vault using Obsidian Bases or Dataview. Use this skill when the user says "create a dashboard", "vault dashboard", "show all X as a table", "dynamic view", "query my vault", "build a content index", "show me all concepts/entities/projects", or wants a…