run-llm-d-benchmark

run-llm-d-benchmark is a skill for Claude Code, Codex from llm-d-incubation/llm-d-skills. It costs 120 tokens per session (3,960 once invoked), scanned C, original, Apache-2.0.

A procedure for benchmarking an already deployed llm-d language-model serving system with the llmdbenchmark command-line tool. It supports named deployment guides or a custom workload and endpoint.

In plain words
What is it for?
Installing or checking the benchmark tool, running inference workloads, testing guide-based or custom configurations, and collecting results for later comparison.
Why use it?
Performance tests are easy to run inconsistently or against the wrong endpoint. This guides setup, workload selection, execution, and saving the results locally.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: positional $N argument.

Good fit Installing or checking the benchmark tool, running inference workloads, testing guide-based or custom configurations, and collecting results for later comparison.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/llm-d-incubation/llm-d-skills/run-llm-d-benchmark
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 llm-d-incubation/llm-d-skills --skill run-llm-d-benchmark
Clone the repo
git clone --depth 1 https://github.com/llm-d-incubation/llm-d-skills

Made for: Claude Code, Codex.

Its marketplace also offers this one on its own, as the plugin run-llm-d-benchmark/plugin install run-llm-d-benchmark after adding the marketplace above.

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 run-llm-d-benchmark

README.md
[![agentmods](https://agentmods.dev/badge/skills/llm-d-incubation/llm-d-skills/run-llm-d-benchmark/github.svg)](https://agentmods.dev/skills/llm-d-incubation/llm-d-skills/run-llm-d-benchmark)
Your own site
<a href="https://agentmods.dev/skills/llm-d-incubation/llm-d-skills/run-llm-d-benchmark"><img src="https://agentmods.dev/badge/skills/llm-d-incubation/llm-d-skills/run-llm-d-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.

agentmods 80×15 button for run-llm-d-benchmark

Your own site · 80×15
<a href="https://agentmods.dev/skills/llm-d-incubation/llm-d-skills/run-llm-d-benchmark"><img src="https://agentmods.dev/badge/skills/llm-d-incubation/llm-d-skills/run-llm-d-benchmark.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 120 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,960 The whole file, excluding the scripts and references it only reads on demand.
Security scan C 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 $0.00120 $0.03960
Opus 5 $0.00060 $0.01980
Sonnet 5 $0.00024 $0.00792
Haiku 4.5 $0.00012 $0.00396

Measured 12d ago against content hash e5237aef7e64, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade C, and why

run-llm-d-benchmark scanned grade C 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 12d 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.

Downloads and executes remote codehighSupply chain

curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.

curl -sSL https://raw.githubusercontent.com/llm-d/llm-d-benchmark/main/install.sh | bash

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

curl -sSL https://raw.githubusercontent.com/llm-d/llm-d-benchmark/main/install.sh | bash
skills/run-llm-d-benchmark/SKILL.md · 409 lines

How it starts

The opening of the file, as written. The whole thing — 409 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Run llm-d Benchmark

Purpose

Run a benchmark workload against an already-deployed llm-d stack using the llmdbenchmark CLI. Useful for evaluating the performance of the llm-d stack. Supports two modes:

  • Guide mode: the stack was deployed from a named llm-d guide (e.g. optimized-baseline). Use --spec guides/<name> and pick from guide-specific or generic workload profiles.
  • Custom workload mode: the user provides their own workload profile (or a path to an existing one) and a direct endpoint URL. No guide name required.

Both modes follow helpers/benchmark.md. When the benchmark completes, results are available in the local workspace directory.


Workflow

Step 1: Verify or Install the llmdbenchmark CLI

Check whether the CLI is available:

command -v llmdbenchmark 2>/dev/null && llmdbenchmark --version

If not found, check for the repo locally and install if missing:

# Is the repo already cloned?
ls ./llm-d-benchmark 2>/dev/null || \
  curl -sSL https://raw.githubusercontent.com/llm-d/llm-d-benchmark/main/install.sh | bash

For troubleshooting install issues - Check Makefile for install instructions and dependencies.

Activate the virtualenv and enter the repo — both are required for every new shell session:

cd llm-d-benchmark
source .venv/bin/activate
llmdbenchmark --version

All subsequent llmdbenchmark commands must be run from inside llm-d-benchmark/ with the venv active.


Step 2: Locate the Namespace and (Optionally) the Guide Name

Locate the llm-d stack according to the following logic:

  1. If a NAMESPACE environment variable is specified, the llm-d stack is assumed to be deployed there
  2. If an oc project exists, the stack is assumed to be deployed in the current oc project.
  3. If none of the above holds, ask the user for the NAMESPACE where it is deployed.
  4. Make sure the NAMESPACE environment variable is set.

Read the full file on GitHub · 409 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. 12d ago First seen · 409 lines · 120 tokens per session scan C e5237aef7e64

Subscribe to this mod's changes

run-llm-d-benchmark is a skill published in the GitHub repository llm-d-incubation/llm-d-skills (6 stars, last pushed 29d ago), licensed Apache-2.0. It adds 120 tokens to every session and 3,960 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens

insight-error-page

Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…

vercel/next.js · 83 tokens