neuron-framework-profile-analysis-vllm-neuron

neuron-framework-profile-analysis-vllm-neuron is a skill for Claude Code, Codex from aws-neuron/neuron-agentic-development. It costs 110 tokens per session (10,812 once invoked), scanned A, original, Apache-2.0.

A tool and guide for turning vLLM Neuron execution traces into structured data and performance reports. It examines model phases, communication boundaries, engine limits, and data-transfer efficiency.

In plain words
What is it for?
Use it to ingest profiles, separate prefill and decode work, measure per-operation behavior, and investigate performance bottlenecks in Neuron-served language models.
Why use it?
It gives a repeatable way to inspect profile data instead of relying on isolated replay timings that may miss traffic from the live multi-device run. Some of its deeper performance conclusions are approximate.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions subagents.

Good fit Use it to ingest profiles, separate prefill and decode work, measure per-operation behavior, and investigate performance bottlenecks in Neuron-served language models.

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Install with agentmods
npx agentmods add skills/aws-neuron/neuron-agentic-development/neuron-framework-profile-analysis-vllm-neuron
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.

Any agent
npx skills add aws-neuron/neuron-agentic-development --skill neuron-framework-profile-analysis-vllm-neuron
Clone the repo
git clone --depth 1 https://github.com/aws-neuron/neuron-agentic-development

Made for: Claude Code, Codex.

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-profile-analysis-vllm-neuron

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

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Your own site · 80×15
<a href="https://agentmods.dev/skills/aws-neuron/neuron-agentic-development/neuron-framework-profile-analysis-vllm-neuron"><img src="https://agentmods.dev/badge/skills/aws-neuron/neuron-agentic-development/neuron-framework-profile-analysis-vllm-neuron.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 110 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 10,812 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 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 $0.00110 $0.10812
Opus 5.5 $0.00044 $0.04325
Sonnet 5.5 $0.00022 $0.02162
Haiku 4.5 $0.00011 $0.01081

Measured 4d ago against content hash 7d0bb2adef13, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

neuron-framework-profile-analysis-vllm-neuron 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 4d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (references/safe_serve.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/neuron-framework-profile-analysis-vllm-neuron/SKILL.md · 800 lines

How it starts

The opening of the file, as written. The whole thing — 800 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Analyzing vllm-neuron Profiles

Note: The analysis methodology (steps 2–5) is adapted from the NKI profiling skill and is not fully validated for framework-compiled NEFFs. Performance bounds, collective partitioning, and investigation results should be treated as approximate. Step 1 (ingestion) is stable.

Verification discipline (BLOCKING — read and satisfy before any analysis)

Profile analysis is where confident-sounding wrong answers are easiest to produce. These rules are a hard gate, not advice — distilled from repeated real failures on this workflow:

  1. Device time comes from the LIVE nc_exec_running system trace, never from an isolated neuron-profile / neuron-explorer capture NEFF replay. A standalone replay undercounts the real multi-rank collective traffic (measured: replay ~12.9 ms vs live ~18 ms for the same TP=16 graph; another run mis-quoted a 14.8 ms replay as "the device floor" and built a wrong "serving overhead" conclusion on it). Always cross-check against the live trace (references/system-trace-queries.md §1f).
  2. Query already-ingested profile data before writing any custom timing script. The parquet tables already hold per-op, per-engine, ns-resolution timestamps. If you catch yourself about to write time.perf_counter() around a model call, or a benchmark/timing harness for a device question — STOP and query the profile. Reach for a hand-rolled timer only when the question is genuinely host-side and never touches the device — and even then, check the system trace first.
  3. Never compare two numbers captured from different runs. Subtracting a device total_time from one capture and a wall-clock from another ("19.5 − 12.9 = 6.6ms overhead") is arithmetic dressed up as measurement. Numbers compared must come from the same profile / same execution.
  4. Validate concurrency claims with overlapping measurement windows, not overlapping process lifetimes. Two replicas whose processes run concurrently can still have timed windows that don't overlap (compile-time skew). Use an explicit barrier and confirm the windows overlap before reporting a scaling number.

Read the full file on GitHub · 800 lines

Files

What ships with it

7 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.

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. 4d ago First seen · 800 lines · 110 tokens per session scan A 7d0bb2adef13

Subscribe to this mod's changes

neuron-framework-profile-analysis-vllm-neuron is a skill published in the GitHub repository aws-neuron/neuron-agentic-development (65 stars, last pushed 4d ago), licensed Apache-2.0. It adds 110 tokens to every session and 10,812 once invoked, about $0.0004 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 2026-10-20.

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