neuron-framework-autoport-agent

neuron-framework-autoport-agent is an agent for Claude Code from aws-neuron/neuron-agentic-development. It costs 318 tokens per session (1,052 once invoked), scanned D, original, Apache-2.0.

An autonomous agent for adapting Hugging Face machine-learning models to run with AWS Neuron on Trainium or Inferentia chips.

In plain words
What is it for?
Use it to analyze a model, implement the port, compile it, run inference tests, and validate the result.
Why use it?
It organizes the porting work and checks for required Neuron hardware before compiling and testing the adapted model.

Agent for Claude Code

Written for Claude Code: a Claude Code subagent (agents/*.md). Also seen: model in frontmatter; names the TodoWrite tool.

Good fit Use it to analyze a model, implement the port, compile it, run inference tests, and validate the result.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/aws-neuron/neuron-agentic-development/neuron-framework-autoport-agent
Install

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.

Clone the repo
git clone --depth 1 https://github.com/aws-neuron/neuron-agentic-development

Made for: Claude Code.

Wrote 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.

agentmods badge for neuron-framework-autoport-agent

README.md
[![agentmods](https://agentmods.dev/badge/agents/aws-neuron/neuron-agentic-development/neuron-framework-autoport-agent/github.svg)](https://agentmods.dev/agents/aws-neuron/neuron-agentic-development/neuron-framework-autoport-agent)
Your own site
<a href="https://agentmods.dev/agents/aws-neuron/neuron-agentic-development/neuron-framework-autoport-agent"><img src="https://agentmods.dev/badge/agents/aws-neuron/neuron-agentic-development/neuron-framework-autoport-agent/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.

agentmods 80×15 button for neuron-framework-autoport-agent

Your own site · 80×15
<a href="https://agentmods.dev/agents/aws-neuron/neuron-agentic-development/neuron-framework-autoport-agent"><img src="https://agentmods.dev/badge/agents/aws-neuron/neuron-agentic-development/neuron-framework-autoport-agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 318 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,052 The whole file, excluding the scripts and references it only reads on demand.
Security scan D 2 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00318 $0.01052
Opus 5.5 $0.00127 $0.00421
Sonnet 5.5 $0.00064 $0.00210
Haiku 4.5 $0.00032 $0.00105

Measured 1mo ago against content hash b03983ce2836, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade D, and why

neuron-framework-autoport-agent scanned grade D with 2 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.

Unrestricted tool accessmediumExcessive agency

A wildcard tool grant or "run any command" leaves no least-privilege boundary at all.

- Do not import, reference, or run any code from `transformers_neuronx`. It is an old API library.

Recursive force deletehighDestructive command

rm -rf with a variable or a broad path is one typo away from removing the wrong tree.

rm -rf /var/tmp/neuron-compile-cache
agents/neuron-framework-autoport-agent.md · 85 lines

How it starts

The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Neuron Autoport Agent

You are an autonomous model porting agent. You accept HuggingFace model parameters and execute the full porting workflow end-to-end. Select the appropriate workflow based on the request.

Workflow Routing

Request Type Skill
Port a HuggingFace model to NeuronX /neuron-framework-autoport

Prerequisites

Before starting any porting workflow, verify NeuronCores are available:

neuron-ls

If 0 cores are detected and the user did not specify dry-run mode, tell the user to allocate a compute node with Neuron hardware and STOP.

Also clear any stale compile cache:

rm -rf /var/tmp/neuron-compile-cache

Parsing Rules

  • Accept parameters as explicit key=value pairs or extract from natural language input.
  • For natural language input (e.g., "port ArceeForCausalLM from transformers/src/transformers/models/arcee"), infer the parameters from context.
  • Confirm all extracted parameters with the user before proceeding.
  • If any required parameter is missing, prompt the user for it before starting the workflow.

Project Guidelines

Prohibited Packages

  • Do not import, reference, or run any code from transformers_neuronx. It is an old API library.

PYTHONPATH Handling

  • If you run into issues with imports and PYTHONPATH, do not make changes to the script — change PYTHONPATH instead. When you test, do the same. At the end of the port, include a complete PYTHONPATH in your documentation.

Error Handling

  • Do not generate any try/except statements.
  • Let errors surface directly without catching them.
  • This allows for cleaner debugging and more transparent error reporting.

File Organization

  • agent_artifacts/tmp/ — All temporary files (compile scripts, test scripts, intermediate artifacts)
  • neuron_port/ — All ported model files (modeling and configuration files)
  • agent_artifacts/traces/ — Checkpoint prompts, completions, and tool use for every major step
  • agent_artifacts/data/ — All weights, checkpoints, and downloaded artifacts. Do not store weights anywhere else.

Read the full file on GitHub · 85 lines

Changes

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.

  1. 1mo ago First seen · 85 lines · 318 tokens per session scan D b03983ce2836

Subscribe to this mod's changes

neuron-framework-autoport-agent is an agent published in the GitHub repository aws-neuron/neuron-agentic-development (63 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 318 tokens to every session and 1,052 once invoked, about $0.0013 per session on Opus 5.5. A static security scan graded it D with 2 findings (unrestricted tool access, recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other agents, from other repositories

edge-ai-engineer

Edge AI deployment specialist for on-device inference using Google AI Edge Gallery, TFLite, ONNX Runtime, and MediaPipe with model quantization and hardware delegate optimization.

pjt222/agent-almanac · 38 tokens

intel-gpu-ai-skills-agent

Router agent exposing Intel GPU AI skill packs for running, benchmarking, and profiling Hugging Face models, and migrating workloads from CUDA on Intel GPUs.

intel/gpu-ai-skills · 37 tokens

iot-data-specialist

IoT data architecture, BLE communication, health metrics processing for pet devices.

atretyak1985/swarmery · 20 tokens

kpz

ML engineering specialist sub-agent. Designs and implements inference pipelines, provider selection, quantization strategies, and hardware abstraction for ONNX models. Benchmark-driven — measures before optimizing.

punt-labs/vox · 37 tokens

jensen-huang

Jensen Huang — Hardware & AI Infrastructure. Spawnable director agent for AI infrastructure planning, GPU and data center strategy, performance and cost optimization, and build-vs-buy decisions on compute. Use when compute, energy, or infrastructure is the bottleneck.

markusbegerow/board-of-directors · 56 tokens

ai-ml-engineer

Use when selecting, benchmarking, or tuning Ollama models on constrained edge hardware; analysing LLM inference performance and context window behaviour; designing or evaluating prompt injection detection approaches; or producing model research documentation. Produces research notes, configuration recommendations, and…

mcleo-d/claude-agents · 68 tokens