Hugging Face Skills is a collection of packaged instructions, scripts, and resources that teach AI agents how to perform tasks in the Hugging Face ecosystem, such as managing models and datasets, training models, and running evaluations. It is for coding agents that need to use Hugging Face Hub and machine-learning workflows. The catalogue entries are the project's own skills and integrations for agent clients.
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 huggingface/skills --skill hf-cloud-sagemaker-iam-preflightgit clone --depth 1 https://github.com/huggingface/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/huggingface/skills/hf-cloud-sagemaker-iam-preflight)<a href="https://agentmods.dev/skills/huggingface/skills/hf-cloud-sagemaker-iam-preflight"><img src="https://agentmods.dev/badge/skills/huggingface/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/huggingface/skills/hf-cloud-sagemaker-iam-preflight"><img src="https://agentmods.dev/badge/skills/huggingface/skills/hf-cloud-sagemaker-iam-preflight.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 8 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.01542 |
| Opus 5 | $0.00066 | $0.00771 |
| Sonnet 5 | $0.00026 | $0.00308 |
| Haiku 4.5 | $0.00013 | $0.00154 |
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 10d 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- hf-cloud-sagemaker-iam-preflight — 88% identical, 3 lines differ
- hf-cloud-sagemaker-iam-preflight — 86% identical, 55 lines differ
How it starts
The opening of the file, as written. The whole thing — 103 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
4 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.
- 10d ago First seen · 103 lines · 131 tokens per session scan A 192add8e50e8
hf-cloud-sagemaker-iam-preflight is a skill published in the GitHub repository huggingface/skills (11,028 stars, last pushed yesterday), licensed Apache-2.0. It adds 131 tokens to every session and 1,542 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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