Sagemaker AI Ops Review
aws/tools-for-devops-agent
Amazon SageMaker AI Operational Review. An agent skill from aws/tools-for-devops-agent.
Finds or validates a usable SageMaker execution role before deploying or training, so scripts do not try to create IAM roles they lack permission to create.
$ npx skills add huggingface/skills --skill hf-cloud-sagemaker-iam-preflight -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install huggingface/skills hf-cloud-sagemaker-iam-preflight --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/huggingface/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/hf-cloud-sagemaker-iam-preflight .claude/skills/hf-cloud-sagemaker-iam-preflight && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "hf-cloud-sagemaker-iam-preflight" agent skill from https://github.com/huggingface/skills/tree/main/skills/hf-cloud-sagemaker-iam-preflight into .claude/skills/hf-cloud-sagemaker-iam-preflight/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hf-cloud-sagemaker-iam-preflight", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/huggingface/skills/tree/main/skills/hf-cloud-sagemaker-iam-preflightType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add huggingface/skills --skill hf-cloud-sagemaker-iam-preflight -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install huggingface/skills hf-cloud-sagemaker-iam-preflight --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/huggingface/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/hf-cloud-sagemaker-iam-preflight .agents/skills/hf-cloud-sagemaker-iam-preflight && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "hf-cloud-sagemaker-iam-preflight" agent skill from https://github.com/huggingface/skills/tree/main/skills/hf-cloud-sagemaker-iam-preflight into .agents/skills/hf-cloud-sagemaker-iam-preflight/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hf-cloud-sagemaker-iam-preflight", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add huggingface/skills --skill hf-cloud-sagemaker-iam-preflight -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install huggingface/skills hf-cloud-sagemaker-iam-preflight --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/huggingface/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/hf-cloud-sagemaker-iam-preflight .cursor/skills/hf-cloud-sagemaker-iam-preflight && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "hf-cloud-sagemaker-iam-preflight" agent skill from https://github.com/huggingface/skills/tree/main/skills/hf-cloud-sagemaker-iam-preflight into .cursor/skills/hf-cloud-sagemaker-iam-preflight/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hf-cloud-sagemaker-iam-preflight", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/huggingface/skills.git --path skills/hf-cloud-sagemaker-iam-preflight--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add huggingface/skills --skill hf-cloud-sagemaker-iam-preflight -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install huggingface/skills hf-cloud-sagemaker-iam-preflight --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/huggingface/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/hf-cloud-sagemaker-iam-preflight .gemini/skills/hf-cloud-sagemaker-iam-preflight && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "hf-cloud-sagemaker-iam-preflight" agent skill from https://github.com/huggingface/skills/tree/main/skills/hf-cloud-sagemaker-iam-preflight into .gemini/skills/hf-cloud-sagemaker-iam-preflight/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hf-cloud-sagemaker-iam-preflight", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install huggingface/skills hf-cloud-sagemaker-iam-preflightInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add huggingface/skills --skill hf-cloud-sagemaker-iam-preflight -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/huggingface/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/hf-cloud-sagemaker-iam-preflight .github/skills/hf-cloud-sagemaker-iam-preflight && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "hf-cloud-sagemaker-iam-preflight" agent skill from https://github.com/huggingface/skills/tree/main/skills/hf-cloud-sagemaker-iam-preflight into .github/skills/hf-cloud-sagemaker-iam-preflight/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hf-cloud-sagemaker-iam-preflight", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add huggingface/skills --skill hf-cloud-sagemaker-iam-preflight -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install huggingface/skills hf-cloud-sagemaker-iam-preflight --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/huggingface/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/hf-cloud-sagemaker-iam-preflight .opencode/skills/hf-cloud-sagemaker-iam-preflight && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "hf-cloud-sagemaker-iam-preflight" agent skill from https://github.com/huggingface/skills/tree/main/skills/hf-cloud-sagemaker-iam-preflight into .opencode/skills/hf-cloud-sagemaker-iam-preflight/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hf-cloud-sagemaker-iam-preflight", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
hf-cloud-sagemaker-iam-preflightFinds or validates a usable SageMaker execution role before deploying or training, so scripts do not try to create IAM roles they lack permission to create.
Every SageMaker resource needs an execution role that SageMaker assumes to read model artifacts from S3, pull serving containers from ECR and write logs. The skill sets an order of operations of discover, validate, and create only if necessary, because many deployments fail when a script tries to make a new role without looking for an existing one and the caller is an SSO principal with no IAM write permissions.
Two Python helpers do the work. check_role.py validates a role you supply by name or ARN, printing the ARN on success and the reason on stderr on failure. With no argument it lists roles matching common SageMaker naming patterns, ranks them by last-used date, checks trust policies in that order and returns the first usable ARN. A create_role.py script and JSON files for the trust policy and minimum permissions are also included.
Run the helpers from a shell where aws sts get-caller-identity already works, because they call the same aws binary and inherit its profile, region and SSO session. On Windows, use PowerShell directly rather than WSL or Git Bash, which often do not share the AWS configuration.
3 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit c3ff942. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Ships 2 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
awspython3pythonFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use aws, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
SageMaker IAM Role Preflight loads about 1.8k tokens when it runs, and up to ~2.1k if it reads all its reference files. Until then it costs about 162 tokens; SKILL.md has 778 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
The full file from huggingface/skills at commit c3ff942, republished under its Apache-2.0 licence (© huggingface). 778 words, ~1,756 tokens.
.claude/skills/hf-cloud-sagemaker-iam-preflight/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.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.
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.
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.
python3 scripts/check_role.pyLists roles matching common SageMaker patterns (AmazonSageMaker-ExecutionRole-*, SageMakerExecutionRole*, etc.), ranks by last-used date (most recent first), validates trust policy in that order, returns the first usable ARN. Most accounts that have used SageMaker before already have one.
Why rank by last-used: in accounts with multiple roles (auto-generated 2021 role + manual project role + etc.), the alphabetically-first one is rarely the actively-maintained one. The most-recently-used role is more likely to have current policies — including cross-account ECR pull. The script prints the ranking so you can see which got picked.
IAM frequently reports no RoleLastUsed at all (tracking only covers recent activity). When every candidate ties at "never used", the script falls back to newest creation date — a newer role is more likely to have current policies than a 2021 leftover.
If the user can create (has IAM permissions):
python3 scripts/create_role.py "<role-name>" "<model-bucket>"Second arg scopes S3 access to a specific bucket. Omit if unknown; script warns and the user can update the policy later.
If the user cannot create (SSO principal — hf-cloud-aws-context-discovery will have flagged this):
Stop and surface this clearly. Don't retry alternative IAM operations hoping one works:
I can't find an existing SageMaker execution role, and you're authenticated via SSO so you can't create one directly. Please either:
- Ask your AWS admin for a SageMaker execution role ARN, or
- Have them grant your SSO permission set
iam:CreateRole,iam:PutRolePolicy
Specific instructions get unblocked fast; vague "permission denied" messages don't.
A role is usable when (1) it exists, (2) its trust policy allows sagemaker.amazonaws.com to sts:AssumeRole, and (3) its permissions grant only the actions and resources this deployment needs. See references/trust-policy.json for the canonical trust policy.
check_role.py verifies existence and trust because policy evaluation depends on the deployment's exact S3, ECR, logging, and optional output resources. Before deployment, inspect the selected role's policies and compare them with references/minimum-permissions.json; add only missing actions and scope them to the required resources. Do not attach AmazonSageMakerFullAccess or defer permission review until an AccessDenied failure.
references/minimum-permissions.json is the standalone inline policy for endpoint execution:
s3:GetObject + s3:ListBucket on the model artifact bucketcreate_role.py installs this inline policy without attaching a managed FullAccess policy. Replace REPLACE_WITH_MODEL_BUCKET in the template with the actual bucket name — create_role.py does this automatically when given a bucket as its second argument. Add narrowly scoped permissions separately for optional features such as async output or data capture.
If the Python helper can't run or can't see your identity (rare — usually a broken PATH or running under a Bash shim that lacks AWS context), do the same preflight by hand in the shell where aws sts get-caller-identity works. The logic is just AWS CLI calls; the helper exists only to bundle and rank them.
PowerShell:
# 1. List candidate SageMaker roles
aws iam list-roles --query "Roles[?contains(RoleName,'SageMaker') || contains(RoleName,'sagemaker')]" --output json
# 2. For each candidate, confirm the trust policy allows sagemaker.amazonaws.com
aws iam get-role --role-name <role-name> --query "Role.AssumeRolePolicyDocument" --output json
# 3. Prefer the most-recently-used role with SageMaker-execution naming
# (LastUsedDate is often None for every role — then prefer newest CreateDate)
aws iam get-role --role-name <role-name> --query "Role.[RoleLastUsed.LastUsedDate, CreateDate]" --output textPick the most-recently-used role whose trust policy contains sagemaker.amazonaws.com. Use the resulting ARN exactly as if check_role.py had returned it. Bash/macOS/Linux use the same commands.
© huggingface, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 4 other files (scripts, references) in skills/hf-cloud-sagemaker-iam-preflight of huggingface/skills.
Open the folder on GitHubat commit c3ff942
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in huggingface/skills, which our catalogue first saw on October 7, 2026.
SageMaker IAM Role Preflight next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| SageMaker IAM Role Preflight this skillhuggingface/skills | 11k | 1 repos | ~1.8k | Automated safety check: Pass | Apache-2.0 | |
| Sagemaker AI Ops Reviewaws/tools-for-devops-agent | 102 | — | ~3.9k | Automated safety check: Pass | Apache-2.0 | |
| SDK Getting Startedawslabs/agent-plugins | 915 | — | ~248 | Automated safety check: Pass | Apache-2.0 | |
| AWS AI MLaws/agent-toolkit-for-aws | 2.8k | — | ~1.7k | Automated safety check: Pass | Apache-2.0 | |
| Hyperpod Version Checkerawslabs/agent-plugins | 915 | — | ~910 | Automated safety check: Pass | Apache-2.0 | |
| Hf Cloud Sagemaker Iam Preflightwaybarrios/opencode-power-pack | 533 | — | ~1.6k | Automated safety check: Pass | Apache-2.0 |
aws/tools-for-devops-agent
Amazon SageMaker AI Operational Review. An agent skill from aws/tools-for-devops-agent.
awslabs/agent-plugins
Validates the user's environment for SageMaker AI operations — checks SDK version, AWS region, and execution role.
aws/agent-toolkit-for-aws
Selects, deploys, and customizes AI models on Amazon SageMaker.
awslabs/agent-plugins
Check and compare software component versions on SageMaker HyperPod cluster nodes - NVIDIA drivers, CUDA toolkit, cuDNN, NCCL, EFA, AWS OFI NCCL, GDRCopy, MPI, Neuron SDK (Trainium/Inferentia)…
waybarrios/opencode-power-pack
Verify or select a SageMaker execution role before creating models, endpoints, or training jobs.
zxkane/aws-skills
AWS Cloud Development Kit (CDK) expert for building cloud infrastructure with TypeScript/Python.
huggingface/skills
Chooses the right serving container and current image URI for deploying a Hugging Face model to a SageMaker endpoint, preferring Hugging Face images over generic ones.
huggingface/skills
Routes a sentence-transformers training task to the right model type and required reference docs and example scripts, covering bi-encoders, rerankers, sparse and multi-vector models.
huggingface/skills
Sets up an isolated Python environment with a supported interpreter and current boto3 before any SageMaker deployment, training or AWS automation code runs.
huggingface/skills
Runs evaluations of Hugging Face Hub models on local hardware with inspect-ai or lighteval, and helps choose between vLLM, Transformers and accelerate backends.
huggingface/skills
Deploys SageMaker endpoints with autoscaling, CloudWatch alarms and tags on by default, using scripts for real-time, scale-to-zero and async setups.
huggingface/skills
Indexes research papers on the Hugging Face Hub from arXiv, links them to models and datasets, claims authorship and generates markdown research articles from templates.
Categories
Finds or validates a usable SageMaker execution role before deploying or training, so scripts do not try to create IAM roles they lack permission to create. Every SageMaker resource needs an execution role that SageMaker assumes to read model artifacts from S3, pull serving containers from ECR and write logs. The skill sets an order of operations of discover, validate, and create only if necessary, because many deployments fail when a script tries to make a new role without looking for an existing one and the caller is an SSO principal with no IAM write permissions.
SageMaker IAM Role Preflight fits situations like: before creating a SageMaker endpoint, model or training job; when no role ARN was given and a script is about to call iam:CreateRole; diagnosing an AccessDenied error that mentions an IAM action.
Run `npx skills add huggingface/skills --skill hf-cloud-sagemaker-iam-preflight -a claude-code`. Or copy the skill folder (skills/hf-cloud-sagemaker-iam-preflight in huggingface/skills) into .claude/skills/hf-cloud-sagemaker-iam-preflight in your project. Claude Code loads it when a task matches its description.
Run `npx skills add huggingface/skills --skill hf-cloud-sagemaker-iam-preflight -a codex`. Or copy the skill folder (skills/hf-cloud-sagemaker-iam-preflight in huggingface/skills) into .agents/skills/hf-cloud-sagemaker-iam-preflight in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add huggingface/skills --skill hf-cloud-sagemaker-iam-preflight -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/hf-cloud-sagemaker-iam-preflight, .gemini/skills/hf-cloud-sagemaker-iam-preflight, .github/skills/hf-cloud-sagemaker-iam-preflight and .opencode/skills/hf-cloud-sagemaker-iam-preflight in your project.
Going by SKILL.md and its folder, SageMaker IAM Role Preflight needs Python for the scripts in its folder and the command-line tools its instructions call (aws, python3 and python). Our summary lists: Python 3; The AWS CLI with working credentials or an SSO session.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
SageMaker IAM Role Preflight is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 1.8k tokens (SKILL.md is roughly 7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 310 tokens, read only when the agent opens those files.
Skills that share tags, products or a category with SageMaker IAM Role Preflight: Sagemaker AI Ops Review (aws/tools-for-devops-agent, 102 stars), SDK Getting Started (awslabs/agent-plugins, 915 stars), AWS AI ML (aws/agent-toolkit-for-aws, 2.8k stars) and Hyperpod Version Checker (awslabs/agent-plugins, 915 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
huggingface (a GitHub organization, an official publisher) maintains it in huggingface/skills, which has 11,151 GitHub stars. The repository holds 25 skills in this directory. The repository was last updated on October 8, 2026.
Source: huggingface/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.