awslabs/sdk-getting-started
plugins/sagemaker-ai/skills/sdk-getting-started/SKILL.md
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.
$ install --global
skillsauthnpx skillsauth add awslabs/agent-plugins sdk-getting-startedInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
3
Scanners Passed
9
Scanners in report
Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
Skipped
VirusTotalMulti-engine malware detection
50%
Skipped
CrowdStrikeAdvanced threat intelligence
50%
Skipped
OSV-ScannerOpen Source Vulnerability database check
50%
Skipped
OWASP Dep-Check
50%
Last scanned: Jun 11, 2026, 4:06 AM159.8s3 files scanned
SKILL.md
- name:
- sdk-getting-started
- description:
- 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.
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 checksreferences/execution-role-setup.md— Execution role resolution and validation
Related Skills
awslabs/aws-step-functions
development
VerifiedTrustedCommunity
Build workflows with AWS Step Functions state machines using the JSONata query language. Covers Amazon States Language (ASL) structure, state types, variables, data transformation, error handling, AWS service integration, and migrating from the JSONPath to the JSONata query language.
785SKILL.mdUpdated Jun 13, 2026
awslabs/aws-lambda
tools
VerifiedTrustedCommunity
Design, build, deploy, test, and debug serverless applications with AWS Lambda. Triggers on phrases like: Lambda function, event source, serverless application, API Gateway, EventBridge, Step Functions, serverless API, event-driven architecture, Lambda trigger. For deploying non-serverless apps to AWS, use deploy-on-aws plugin instead.
785SKILL.mdUpdated Apr 3, 2026
awslabs/model-selection
data-ai
VerifiedTrustedCommunity
Selects a base model for the user's use case by querying SageMaker Hub. Use when the user asks which model to use, wants to select or change their base model, mentions a model name or family (e.g., "Llama", "Mistral", "Nova"), or wants to evaluate a base model — always activate even for known model names because the exact Hub model ID must be resolved. Queries available models, presents benchmarks and licenses, and confirms selection.
780SKILL.mdUpdated Jun 11, 2026
awslabs/finetuning-technique
testing
VerifiedTrustedCommunity
Selects a fine-tuning technique (SFT, DPO, RLVR, or RLAIF) for the user's use case and validates it against the selected model's available recipes. Use when the user has decided to finetune and needs to choose a technique, or when the technique needs to be validated against a model. Requires a base model to already be selected (via model-selection skill).
780SKILL.mdUpdated Jun 11, 2026