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-iam-preflightgit 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-iam-preflight)<a href="https://agentmods.dev/skills/practicalswan/agent-skills/hf-cloud-sagemaker-iam-preflight"><img src="https://agentmods.dev/badge/skills/practicalswan/agent-skills/hf-cloud-sagemaker-iam-preflight/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-iam-preflight"><img src="https://agentmods.dev/badge/skills/practicalswan/agent-skills/hf-cloud-sagemaker-iam-preflight.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 | $0.00131 | $0.02121 |
| Opus 5 | $0.00066 | $0.01060 |
| Sonnet 5 | $0.00026 | $0.00424 |
| Haiku 4.5 | $0.00013 | $0.00212 |
Grade A, and why
hf-cloud-sagemaker-iam-preflight 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 2d 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
86% identical to hf-cloud-sagemaker-iam-preflight — 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 — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SageMaker IAM Preflight
Every SageMaker resource needs an execution role — the IAM role SageMaker assumes to read model artifacts from S3, pull serving containers from ECR, and write logs. Most deployments fail here because the script tried to create a new role without checking if a usable one already existed, then blew up because the caller is an SSO principal.
This skill encodes the right order: discover, validate, only create if necessary.
Running the helpers (cross-platform)
The helpers are Python so they run identically on Windows, macOS, and Linux:
python3 scripts/check_role.py # macOS / Linux
python scripts/check_role.py # Windows (PowerShell / cmd)
Run them from the shell where the AWS CLI already works — i.e. wherever aws sts get-caller-identity succeeds. The script shells out to that same aws binary and inherits the shell's profile, region, SSO session, proxy, and credential chain.
Windows / WSL / Git Bash caveat. Do not invoke these through a Bash shim (WSL, Git Bash, MSYS) on Windows. Those Bash environments frequently do not share the Windows AWS config, credentials, SSO sessions, environment variables, or proxy settings — so
aws sts get-caller-identityfails inside Bash even when it works natively in PowerShell. (This is exactly why the old.shhelpers failed on Windows and were replaced with Python.) If you're in PowerShell, runpython ...\check_role.pydirectly in PowerShell. If the helper still can't see your identity, run the same discovery natively (see "Native AWS CLI equivalent" below) in the shell whereaws sts get-caller-identityreturns your ARN.
Order of operations
Step 1 — Did the user provide a role?
Validate that one specifically:
python3 scripts/check_role.py "<role-name-or-arn>"
On success it prints the ARN to stdout (exit 0). On failure it logs why on stderr. Don't try to silently fix a broken role — surface the problem.
Step 2 — Discover existing roles
What ships with it
6 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.
- 2d ago Changed 87c46d8ae919
- 3d ago Changed 336b614cb996
- 6d ago First seen · 154 lines · 131 tokens per session scan A 74124c245ff0
hf-cloud-sagemaker-iam-preflight is a skill published in the GitHub repository PracticalSwan/agent-skills (14 stars, last pushed 2d ago), licensed MIT. It adds 131 tokens to every session and 2,121 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to hf-cloud-sagemaker-iam-preflight, differing in 55 lines, and is treated as a copy.
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