mcpbeat

SDK Getting Started

awslabs/sdk-getting-started

Validates the user's environment for SageMaker AI operations — checks SDK version, AWS region, and execution role. Use when the user says "set up", "getting started", "check my environment", "configure SDK", or as the first step in any plan involving SageMaker/Bedrock training, evaluation, or deployment.

1k tokens
context cost
the whole folder, loaded on every use
3
files
instructions only
0
copies elsewhere
how many repositories repackaged it
850
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository 
npx skills add https://github.com/awslabs/agent-plugins --skill sdk-getting-started

The instruction itself

4 sections, as written by the author

SDK Getting Started

Preflight checks to verify the user's environment can run SageMaker AI operations. The agent runs these checks directly (no code generation) and stores results in conversation context for downstream skills.

Principles

  • Don't ask for what you can look up. Resolve region and role programmatically before asking the user.

Workflow

Read and follow references/sagemaker-python-sdk-setup.md.

References

  • references/sagemaker-python-sdk-setup.md - SageMaker Python SDK version, region, and execution role checks
  • references/execution-role-setup.md — Execution role resolution and validation

How to use it

Copy the folder

Take awslabs/sdk-getting-started from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

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.