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 PracticalSwan/agent-skills --skill hf-cloud-sagemaker-deployment-plannergit clone --depth 1 https://github.com/PracticalSwan/agent-skillsWrote 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/practicalswan/agent-skills/hf-cloud-sagemaker-deployment-planner)<a href="https://agentmods.dev/skills/practicalswan/agent-skills/hf-cloud-sagemaker-deployment-planner"><img src="https://agentmods.dev/badge/skills/practicalswan/agent-skills/hf-cloud-sagemaker-deployment-planner/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/practicalswan/agent-skills/hf-cloud-sagemaker-deployment-planner"><img src="https://agentmods.dev/badge/skills/practicalswan/agent-skills/hf-cloud-sagemaker-deployment-planner.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 | $0.00216 | $0.02519 |
| Opus 5 | $0.00108 | $0.01260 |
| Sonnet 5 | $0.00043 | $0.00504 |
| Haiku 4.5 | $0.00022 | $0.00252 |
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 3d 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
89% identical to hf-cloud-sagemaker-deployment-planner — 55 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 — 142 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 ships with it
2 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.
- 3d ago Changed 5eb4d2fc149f
- 4d ago Changed 071adb169d26
- 7d ago First seen · 142 lines · 216 tokens per session scan A a604469b5b81
hf-cloud-sagemaker-deployment-planner is a skill published in the GitHub repository PracticalSwan/agent-skills (14 stars, last pushed 3d ago), licensed MIT. It adds 216 tokens to every session and 2,519 once invoked, about $0.0011 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to hf-cloud-sagemaker-deployment-planner, differing in 55 lines, and is treated as a copy.
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