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-sagemaker-deployment-plannergit 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-sagemaker-deployment-planner)<a href="https://agentmods.dev/skills/waybarrios/opencode-power-pack/hf-cloud-sagemaker-deployment-planner"><img src="https://agentmods.dev/badge/skills/waybarrios/opencode-power-pack/hf-cloud-sagemaker-deployment-planner.svg" alt="Measured on agentmods" 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 | $0.00052 | $0.01552 |
| Opus 5 | $0.00026 | $0.00776 |
| Sonnet 5 | $0.00010 | $0.00310 |
| Haiku 4.5 | $0.00005 | $0.00155 |
Grade A, and why
hf-cloud-sagemaker-deployment-planner 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 8d 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
88% identical to hf-cloud-sagemaker-deployment-planner — 10 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SageMaker Deployment Planner
You are helping a user deploy a model to Amazon SageMaker. Most users invoking this skill want the model deployed with reasonable defaults, in as few questions as possible. Ask only what you need, recommend a pathway honestly, and hand off to the specialized skills.
Workflow phases
- Discovery — what is being deployed and what are the constraints (this skill)
- Pathway selection — real-time / serverless / async / batch / Bedrock CMI (this skill)
- Context preflight —
hf-cloud-aws-context-discovery, thenhf-cloud-python-env-setup - IAM preflight —
hf-cloud-sagemaker-iam-preflight - Image selection —
hf-cloud-serving-image-selection - Deployment —
hf-cloud-sagemaker-production-defaults
Phases 1–2 are this skill's job. The others activate when their patterns match.
Discovery: ask only what you need
You will eventually need to know:
- What model: HuggingFace ID, S3 path to artifacts, or model name. If the user is vague ("the model I fine-tuned"), ask for the artifact location.
- Model type: text-generation LLM, embedding/reranker, or other (classifier, NER, etc.). This determines the serving stack — usually inferable from the model name (anything ending in
-embed-*, starting withBAAI/bge-,sentence-transformers/*etc. is embeddings; chat/instruct models are LLMs). Only ask if it's genuinely ambiguous. - Traffic shape: roughly how often will this be called?
- Latency tolerance: interactive, near-real-time, or async?
- Cost sensitivity: ask only if the user signals it or the traffic pattern is ambiguous.
Region comes from hf-cloud-aws-context-discovery — don't ask unless the user volunteers it.
Do not front-load all of these. A common minimal set is just: what model, and roughly how often will it be called? The model name usually settles the model-type question. That alone is often enough to narrow the pathway to two candidates. If the user already told you something, don't ask again.
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.
- 8d ago First seen · 87 lines · 52 tokens per session scan A 9f5a8782b1f0
hf-cloud-sagemaker-deployment-planner is a skill published in the GitHub repository waybarrios/opencode-power-pack (495 stars, last pushed yesterday), licensed MIT. It adds 52 tokens to every session and 1,552 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to hf-cloud-sagemaker-deployment-planner, differing in 10 lines, and is treated as a copy.
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