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 aws-neuron/neuron-agentic-development --skill neuron-framework-profile-analysis-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-profile-analysis-vllm-neuron)<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.
<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>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 | $0.00110 | $0.10812 |
| Opus 5.5 | $0.00044 | $0.04325 |
| Sonnet 5.5 | $0.00022 | $0.02162 |
| Haiku 4.5 | $0.00011 | $0.01081 |
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.
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 — 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:
- Device time comes from the LIVE
nc_exec_runningsystem trace, never from an isolatedneuron-profile/neuron-explorer captureNEFF 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). - 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. - Never compare two numbers captured from different runs. Subtracting a device
total_timefrom 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. - 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.
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.
- references/collective-partitioning.md 12 KB
- references/fabricated-analysis-case-study.md 2.6 KB
- references/gemma4_report.md 6.3 KB
- references/gemma4_walkthrough.md 12 KB
- references/performance-bounds-nki-reference.md 17 KB
- references/safe_serve.py 210 B runs code
- references/system-trace-queries.md 13 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.
- 4d ago First seen · 800 lines · 110 tokens per session scan A 7d0bb2adef13
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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