sample-sagemaker-agentic-model-deployment is a coding-agent workflow that takes an open-weight model from a public checkpoint to a deployed, benchmarked, and optimized endpoint on Amazon SageMaker AI. It is for automating model staging, serving-configuration selection, deployment, benchmarking, and optimization without manually writing the surrounding API and polling code. Its skills and instruction define the portable agent workflow.
Borrowing it
Nothing to install: this file belongs to aws-samples/sample-sagemaker-agentic-model-deployment. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/aws-samples/sample-sagemaker-agentic-model-deployment/main/.kiro/skills/sagemaker-benchmark/SKILL.mdgit clone --depth 1 https://github.com/aws-samples/sample-sagemaker-agentic-model-deploymentWrote 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/aws-samples/sample-sagemaker-agentic-model-deployment/sagemaker-benchmark)<a href="https://agentmods.dev/skills/aws-samples/sample-sagemaker-agentic-model-deployment/sagemaker-benchmark"><img src="https://agentmods.dev/badge/skills/aws-samples/sample-sagemaker-agentic-model-deployment/sagemaker-benchmark/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/aws-samples/sample-sagemaker-agentic-model-deployment/sagemaker-benchmark"><img src="https://agentmods.dev/badge/skills/aws-samples/sample-sagemaker-agentic-model-deployment/sagemaker-benchmark.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00079 | $0.02185 |
| Opus 5.5 | $0.00032 | $0.00874 |
| Sonnet 5.5 | $0.00016 | $0.00437 |
| Haiku 4.5 | $0.00008 | $0.00218 |
Grade A, and why
sagemaker-benchmark 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 2mo 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.
The source is not reproduced here
Licensed MIT-0
The repository is licensed MIT-0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
What ships with it
18 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.
- datasets/sharegpt-curated.jsonl 447 KB
- sample-output/benchmark_summary.txt 486 B
- sample-output/failure_reason.txt 100 B
- sample-output/logs/aiperf.log 32 KB
- sample-output/outputs.json 351 KB
- sample-output/plot_generation.log 225 B
- sample-output/plots/aiperf_plot.log 8.7 KB
- sample-output/plots/summary.txt 189 B
- sample-output/plots/ttft_over_time.png 119 KB
- sample-output/plots/ttft_timeline.png 114 KB
- sample-output/profile_export_aiperf.csv 4.0 KB
- sample-output/profile_export_aiperf.json 15 KB
- sample-output/profile_export.jsonl 1251 KB
- sample-output/README.md 1.5 KB
- scripts/benchmark_results.py 9.9 KB runs code
- scripts/benchmark.py 13 KB runs code
- scripts/cloudwatch_metrics.py 4.1 KB runs code
- scripts/config.py 6.1 KB runs code
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.
- 2mo ago First seen · 186 lines · 79 tokens per session scan A 8839084280ef
sagemaker-benchmark is a skill published in the GitHub repository aws-samples/sample-sagemaker-agentic-model-deployment (5 stars, last pushed 3mo ago), licensed MIT-0. It adds 79 tokens to every session and 2,185 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 2026-08-31.
sagemaker-benchmark compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| sagemaker-benchmarkaws-samples/sample-sagemaker-agentic-model-deployment | Skill | 5 | 3mo ago | MIT-0 | A | 79 | Kiro |
| agent-platform-model-registrygoogle/skills | Skill | 22k | yesterday | Apache-2.0 | A | 60 | Claude Code, Codex |
| foundry-config-setupmicrosoft/agent-framework | Skill | 15k | yesterday | MIT | A | 65 | Claude Code, Codex |
| google-cloud-solution-agentic-analytics-spark-knowledge-cataloggoogle/skills | Skill | 22k | yesterday | Apache-2.0 | A | 132 | Claude Code, Codex |
| training-checkwanshuiyin/Auto-claude-code-research-in-sleep | Skill | 19k | 3d ago | MIT | A | 35 | Claude Code |
| nemo-automodel-launcher-configNVIDIA/skills | Skill | 3.9k | yesterday | Apache-2.0 | A | 30 | Claude Code |
| finetuningmicrosoft/skills | Skill | 3.2k | yesterday | MIT | A | 132 | Claude Code |