Ensure a usable SageMaker execution role exists before deploying or training. Use this skill whenever about to create a SageMaker endpoint, model, training job, or any resource that requires an execution role. Use it especially when the user has not provided a role ARN explicitly, when scripts are a

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: 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 identity fails inside Bash even when it works natively in PowerShell. (This is exactly why the old .sh helpers failed on Windows and were replaced with Python.) If you're in PowerShell, run python ...\check role.py directly 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 where aws sts get caller identity returns your ARN. Order of operations Step 1 — Did the user provide a role? Validate that one specifically: 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 Lists 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. Step 3 — Create, only if discovery found nothing If the user can create (has IAM permissions): 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. What "validated" means 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. Minimum permissions references/minimum permissions.json is the standalone inline policy for endpoint execution: s3:GetObject + s3:ListBucket on the model artifact bucket ECR pull permissions CloudWatch logs and metrics create 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. Native AWS CLI equivalent (fallback) 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: Pick 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.

hf-cloud-sagemaker-iam-preflight