Amazon Web Services v1.3.0
Build, train, and deploy AI models with deep AWS AI/ML expertise brought directly into your coding assistants, covering the surface area of Amazon SageMaker AI.
From the developer
Apache-2.0
Claude Code
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Build, train, and deploy AI models with deep AWS AI/ML expertise brought directly into your coding assistants, covering the surface area of Amazon SageMaker AI.
Oct 27, 2026 · 18:00 UTC
Web → Id: “wf.69e50c2e946e4d89efb8e23b” → “directoryPlugin.ef5e98d8-2102-4074-a2e8-29d5a278b983”
Web → Anthropic verified: “false” → “true”
Web → Subheading: “Build, train, and deploy AI models with deep AWS AI/ML expertise brought directly into your coding assistants, coveri...” → “Build, train, and deploy AI models with deep AWS AI/ML expertise brought directly into your coding assistants, coveri...”
Oct 3, 2026 · 22:13 UTC
Catalogs updated; open the comparison for details.
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{
"name": "sagemaker-ai",
"source": {
"ref": "main",
"sha": "097fe8ad56d8a1d5e2c81d7880adf145553cf244",
"url": "https://github.com/awslabs/agent-plugins.git",
"path": "plugins/sagemaker-ai",
"source": "git-subdir"
},
"category": "development",
"homepage": "https://github.com/awslabs/agent-plugins",
"description": "Build, train, and deploy AI models with deep AWS AI/ML expertise brought directly into your coding assistants, covering the surface area of Amazon SageMaker AI."
}
{
"name": "sagemaker-ai",
"author": {
"name": "Amazon Web Services"
},
"license": "Apache-2.0",
"version": "1.3.0",
"homepage": "https://github.com/awslabs/agent-plugins",
"keywords": [
"sagemaker",
"machine-learning",
"generative-ai",
"fine-tuning",
"training",
"deployment",
"inference",
"mlops",
"aws"
],
"repository": "https://github.com/awslabs/agent-plugins",
"description": "Build, train, and deploy AI models with deep AWS AI/ML expertise brought directly into your coding assistants, covering the surface area of Amazon SageMaker AI."
}
plugin.json observed Nov 6, 2026 · 10:39 UTC. Manifest source ↗
Catalog declarations and available plugin.json metadata are saved. Referenced skills and executable source files are not downloaded or tested. Missing components in a manifest do not prove that the plugin has none.
Installation counts are source-reported, not independently verified active users. Observation dates are not release dates.
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