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 has not specified a region or profile explicitly, when they say things like "use my AWS account", "deploy to AWS", "use my profile", or when about to make any AWS CLI or SDK call. Never guess the region or account ID — always use this skill to read it from the local configuration first.
npx skills add https://github.com/huggingface/skills --skill hf-cloud-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.
Run these at the start of the AWS work and remember the results for the rest of the session.
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.
Resolution order — stop at the first one that produces a value:
AWS_REGION env varAWS_DEFAULT_REGION env varregion field on the active profile in ~/.aws/configDo not fall back to us-east-1 or any other hardcoded default.
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.
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 pattern | Type | IAM write capability |
|---|---|---|
| arn:aws:iam::<acct>:user/<name> | IAM user | Depends on attached policies |
| arn:aws:sts::<acct>:assumed-role/AWSReservedSSO_<...>/<email> | SSO assumed-role | Typically none — can't create/modify IAM roles |
| arn:aws:sts::<acct>:assumed-role/<role>/<session> | Regular assumed-role | Depends 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.
# 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.
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.
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take huggingface/hf-cloud-aws-context-discovery from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.