awslabs/agent-plugins is a collection of plugins, skills, instructions, an MCP server, a hook, and a setting that guide AI coding agents in architecting, deploying, and operating software on Amazon Web Services. It is for developers using coding agents such as Claude Code, Codex, and Cursor with AWS projects. The catalogue entries are the repository's own agent workflows and supporting components.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add awslabs/agent-plugins --skill model-deploymentgit clone --depth 1 https://github.com/awslabs/agent-pluginsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/awslabs/agent-plugins/model-deployment)<a href="https://agentmods.dev/skills/awslabs/agent-plugins/model-deployment"><img src="https://agentmods.dev/badge/skills/awslabs/agent-plugins/model-deployment/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/awslabs/agent-plugins/model-deployment"><img src="https://agentmods.dev/badge/skills/awslabs/agent-plugins/model-deployment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00076 | $0.01331 |
| Opus 5.5 | $0.00030 | $0.00532 |
| Sonnet 5.5 | $0.00015 | $0.00266 |
| Haiku 4.5 | $0.00008 | $0.00133 |
Grade A, and why
model-deployment scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Model Deployment
Identifies the correct deployment pathway based on model characteristics and generates deployment code.
Scope
This skill supports deploying Nova and OSS models that were fine-tuned through SageMaker Serverless Model Customization only.
Not supported:
- Base models (not fine-tuned)
- Models fine-tuned through other processes
- Full Fine-Tuning (FFT) — only LoRA fine-tuned models are supported
Prerequisites
- The SDK environment has been verified (SDK version, region, execution role). If not done, activate the
sdk-getting-startedskill first.
Principles
- One thing at a time. Each response advances exactly one decision.
- Confirm before proceeding. Wait for the user to agree before moving on. But don't re-ask questions already answered in the conversation — use what you know.
- Don't read files until you need them. Only read pathway references after the pathway is confirmed.
- Use what you know. If conversation history or artifacts already answer a question, confirm your understanding instead of asking again.
Workflow
Step 1: Identify the Training Job
You need the training job name or ARN. Check the conversation history first — the user may have already mentioned it, or it may be available from earlier steps in the workflow (e.g., fine-tuning). If not, ask the user.
Once you have the training job name or ARN, use the AWS MCP tool to look it up:
- Use the AWS MCP tool
describe-training-joband extract:- S3 output path (from
ModelArtifacts.S3ModelArtifactsorOutputDataConfig.S3OutputPath) - IAM role ARN (from
RoleArn) - Region
- S3 output path (from
- Use the AWS MCP tool
list-tagson the training job ARN and extract:- Model ID from the
sagemaker-studio:jumpstart-model-idtag
- Model ID from the
- Determine the model type from the model ID:
- Contains "nova" (nova-micro, nova-lite, nova-pro) → Nova
- Llama, Mistral, Qwen, GPT-OSS, DeepSeek, etc. → OSS
Unsupported models: This skill only supports OSS and Nova models that were LoRA fine-tuned through SageMaker Serverless Model Customization. If the model doesn't match, tell the user this skill can't help and suggest the finetuning skill.
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- code_templates/deploy-nova-bedrock.py 1.9 KB runs code
- code_templates/deploy-nova-sagemaker.py 1.7 KB runs code
- code_templates/deploy-oss-bedrock.py 3.9 KB runs code
- code_templates/deploy-oss-sagemaker.py 1.9 KB runs code
- references/code_output_guide.md 3.2 KB
- references/deploy-nova-bedrock.md 4.0 KB
- references/deploy-nova-sagemaker.md 5.0 KB
- references/deploy-oss-bedrock.md 4.7 KB
- references/deploy-oss-sagemaker.md 6.5 KB
- references/model-licenses.md 12 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 131 lines · 76 tokens per session scan A f2778296c666
model-deployment is a skill published in the GitHub repository awslabs/agent-plugins (924 stars, last pushed 6d ago), licensed Apache-2.0. It adds 76 tokens to every session and 1,331 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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