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 agentmods add skills/aws-neuron/neuron-agentic-development/neuron-framework-autoport-vllm-neuronnpx skills add aws-neuron/neuron-agentic-development --skill neuron-framework-autoport-vllm-neurongit clone --depth 1 https://github.com/aws-neuron/neuron-agentic-developmentWrote 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-neuron/neuron-agentic-development/neuron-framework-autoport-vllm-neuron)<a href="https://agentmods.dev/skills/aws-neuron/neuron-agentic-development/neuron-framework-autoport-vllm-neuron"><img src="https://agentmods.dev/badge/skills/aws-neuron/neuron-agentic-development/neuron-framework-autoport-vllm-neuron.svg" alt="Measured on agentmods" 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 | $0.00040 | $0.07847 |
| Opus 5 | $0.00020 | $0.03923 |
| Sonnet 5 | $0.00008 | $0.01569 |
| Haiku 4.5 | $0.00004 | $0.00785 |
Grade B, and why
neuron-framework-autoport-vllm-neuron scanned grade B with 1 finding 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 6d 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.
Unrestricted tool accessmediumExcessive agency
A wildcard tool grant or "run any command" leaves no least-privilege boundary at all.
- **Do not run any code** — no compilation, inference, or validation (no Trainium hardware available) How it starts
The opening of the file, as written. The whole thing — 507 lines — stays where its author put it; the contents beside it link to each section on GitHub.
vLLM-Neuron Model Porting Skill
Port the HuggingFace model $1 to the vLLM-Neuron Trainium2 backend as $0.
Conventions
MODEL_NAME=$0(snake_case, e.g.,yi,deepseek_v2)HF_MODEL_ID=$1(e.g.,01-ai/Yi-6B-Chat)--reviewflag (if present in$ARGUMENTS): pause at Step 2 for user confirmation- Derive
PascalNamefrom MODEL_NAME (e.g.,yi→Yi,gpt_neox→GPTNeoX) - Model code goes in
vllm_neuron/model - Example script goes in
examples/vllm_neuron/models/MODEL_NAME/
Dry-run
When the user specifies dry-run:
- Skip all agent-level prerequisites (package import checks, NeuronCore check via
neuron-ls) - Activate the provided venv and resolve source paths by filesystem lookup (do NOT use
import— dependencies are not installed):export PATH=<pathToVenv>/bin:$PATH VLLM_NEURON_SRC=~/tmp/vllm_autoport/vllm_neuron TRANSFORMERS_SRC=$(python3 -c "import site; print(site.getsitepackages()[0])")/transformers - Do not run any code — no compilation, inference, or validation (no Trainium hardware available)
Before You Start
Ensure the agent-level prerequisites have been completed (venv activation, package verification, NeuronCore check). These steps are owned by the agent prompt and should not be re-executed here.
Note: Do NOT clear
/var/tmp/neuron-compile-cacheas a pre-flight step — it is a shared system directory and other processes or users may depend on it. Only clear it reactively if you hit a[NLA001]JSON parse error orFileNotFoundErroron neff_output paths.
Step 0: Parse & Detect State
- Package check: Ensure that
private_vllm_neuronorvllm_neuronpackage exists in the user's workspace. If it does not, STOP and do not proceed without informing the user. - Parse
$ARGUMENTS— extract model-name, hf-model-id, and--reviewflag. - Check if
vllm_neuron/model/MODEL_NAME/already exists:- If model.py + registry entry both exist → skip to Phase C (validation)
- If model.py exists but not registered → resume at Step 5 (register)
- If directory doesn't exist → proceed to Phase A
What ships with it
6 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.
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.
- 6d ago First seen · 507 lines · 40 tokens per session scan B 06118c2aa752
neuron-framework-autoport-vllm-neuron is a skill published in the GitHub repository aws-neuron/neuron-agentic-development (57 stars, last pushed 17d ago), licensed Apache-2.0. It adds 40 tokens to every session and 7,847 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it B with 1 finding (unrestricted tool access). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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