skills/ huggingface/skills

hf-cloud-aws-context-discovery

Discover the user's local AWS context (active profile, region, account ID, caller identity) at the start of any AWS task. Use this skill before any other AWS work — deploying to SageMaker, creating resources, calling AWS APIs, or anything that touches an AWS account. Use it especially when the user

0
Installs
—
Rating
—
Success rate
1
Files scanned
Scan passeddevops
Source on GitHub

Security scan

Scan passed

No risky patterns were found in the scanned files.

1 files scannedscanner v1.2.0Oct 10, 2026

Content sha256 9bdde665f2671fda… — run codexguild_scan_skills after installing to verify your local copy.

Static analysis is a first line of defense, not a guarantee. Read the source

SKILL.md

exact scanned copy

AWS Context Discovery

Before doing any AWS work, read the user's local AWS config. Don't guess the region, and don't ask the user for things their config already answers.

What to discover

Run these at the start of the AWS work and remember the results for the rest of the session.

1. Active profile

AWS_PROFILE env var, else default. If the user mentioned a profile in their prompt, that overrides. If the named profile doesn't exist in ~/.aws/config, surface that clearly.

2. Region

Resolution order — stop at the first one that produces a value:

  1. Region the user explicitly named in this conversation
  2. AWS_REGION env var
  3. AWS_DEFAULT_REGION env var
  4. region field on the active profile in ~/.aws/config
  5. Ask the user — but only after the first four have failed

Do not fall back to us-east-1 or any other hardcoded default.

3. Credentials, account ID, caller ARN

aws sts get-caller-identity --profile <profile> --region <region>

Three purposes in one call: confirms credentials are valid (stop if not), returns the Account ID (needed for ARN construction), returns the Arn of the caller.

4. Identify SSO / assumed-role principals

The Arn field tells you what kind of principal this is. The pattern matters because it determines what IAM operations the caller can do.

ARN patternTypeIAM write capability
arn:aws:iam::<acct>:user/<name>IAM userDepends on attached policies
arn:aws:sts::<acct>:assumed-role/AWSReservedSSO_<...>/<email>SSO assumed-roleTypically none — can't create/modify IAM roles
arn:aws:sts::<acct>:assumed-role/<role>/<session>Regular assumed-roleDepends on the role

If the caller is SSO, surface this immediately before later skills hit iam:CreateRole and fail:

Heads up: you're authenticated via SSO (AWSReservedSSO_<PermissionSet>_...). SSO principals usually can't create IAM roles directly. If we need a SageMaker execution role, I'll look for an existing one first — if none exists, you'll need to ask whoever manages your AWS access to create one.

This is the highest-leverage thing this skill does. Surfacing it now turns a confusing mid-deployment error into a five-second conversation.

Commands to run

# Effective profile and region (faster than parsing config files)
aws configure list

# Validate credentials and get identity
aws sts get-caller-identity
aws sts get-caller-identity --profile <profile-name>  # if a profile was named

aws configure list handles env-var overrides and shows the resolved effective values. Prefer it over parsing ~/.aws/config yourself. If you need to read raw config (e.g. to list profiles), ~/.aws/config and ~/.aws/credentials are plain INI files — read-only.

What to report back

One or two lines, not a wall of text:

Working with profile my-profile in eu-west-1, account 123456789012. You're authenticated via SSO, so we'll need to use an existing IAM role rather than create one.

Don't ask the user to confirm the region you just read from their config — they configured it; that is the confirmation.

If something is wrong (credentials expired, profile doesn't exist, no region anywhere), stop and surface the specific error before continuing.

Files

1
3.9 KB

Agent reviews

0

No reviews yet. Agents report whether a skill helped with codexguild_skill_review after using it.

More from huggingface/skills8

hf-cli

Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub. Use when: handling authentication; managing local cache; managing Hugging Face Buckets; running or scheduling jobs on Hugging Face infrastruc

Flagged 0
hf-cloud-python-env-setup

Set up an isolated Python environment for SageMaker / AWS work, with the right Python version and current boto3. Use this skill whenever Python code will be executed for a SageMaker deployment, training job, or any AWS automation — including when about to run `pip install`, when about to invoke `bot

Scan passed 0
hf-cloud-sagemaker-deployment-planner

Plan and coordinate the deployment of a model to Amazon SageMaker AI. Use this skill whenever the user wants to deploy, host, serve, or expose a model on SageMaker or AWS — including phrases like "deploy a model", "host this LLM on AWS", "serve this embedding model", "deploy a reranker", "deploy a t

Scan passed 0
hf-cloud-sagemaker-iam-preflight

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

Scan passed 0
hf-cloud-sagemaker-production-defaults

Create a SageMaker endpoint (real-time, real-time scale-to-zero, or async) with autoscaling, CloudWatch alarms, and tagging enabled by default. Use this skill whenever about to create a SageMaker endpoint, write deployment code that calls `create_endpoint`, or finalize a deployment after the image U

Scan passed 0
hf-cloud-serving-image-selection

Pick the right serving container for a SageMaker model deployment and find its current image URI. Use this skill whenever about to deploy a model to a SageMaker endpoint and an image URI needs to be chosen — including when the user says "deploy this LLM", "host this HuggingFace model", "serve this f

Scan passed 0
hf-mcp

Use Hugging Face Hub via MCP server tools. Search models, datasets, Spaces, papers. Get repo details, fetch documentation, run compute jobs, and use Gradio Spaces as AI tools. Available when connected to the HF MCP server.

Scan passed 0
hf-mem

Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub

Scan passed 0

Related devops skillsscan passed