benchmarkdotnet-setup

A guide for setting up BenchmarkDotNet, a .NET tool that measures how quickly code runs and how much memory it uses. It also explains how to compare implementations and interpret results.

In plain words
What is it for?
Use it to create benchmarks, compare .NET methods, measure allocations, establish baselines, and understand results such as nanoseconds per operation.
Why use it?
It replaces guesses about which .NET implementation is faster with repeatable measurements that include runtime and memory use.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/stefanthecode/dotnet-ai-toolkit/benchmarkdotnet-setup
Any agent
npx skills add StefanTheCode/dotnet-ai-toolkit --skill benchmarkdotnet-setup
Clone the repo
git clone --depth 1 https://github.com/StefanTheCode/dotnet-ai-toolkit

Made for: Claude Code, Codex.

Per session 100 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 693 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00100 $0.00693
Opus 5 $0.00050 $0.00347
Sonnet 5 $0.00020 $0.00139
Haiku 4.5 $0.00010 $0.00069

Measured 2d ago against content hash e4b255f2e07b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

benchmarkdotnet-setup 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 2d 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.

skills/benchmarkdotnet-setup/SKILL.md · 60 lines

How it starts

The opening of the file, as written. The whole thing — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.

BenchmarkDotNet Setup

Write correct BenchmarkDotNet benchmarks (no common pitfalls) and read the results properly — so "which is faster" is answered with data, not vibes.

Input — point it at your code

Works on a target: a file, folder, project, or GitHub URL. Identify the methods/implementations to compare; if the user pasted results, go straight to interpretation.

Project + benchmark class

dotnet add package BenchmarkDotNet
[MemoryDiagnoser]                     // allocations matter as much as time
[Orderer(SummaryOrderPolicy.FastestToSlowest)]
public class StringJoinBenchmarks
{
    private readonly string[] _items = Enumerable.Range(0, 1000).Select(i => i.ToString()).ToArray();

    [Benchmark(Baseline = true)]
    public string StringConcat() { var s = ""; foreach (var i in _items) s += i; return s; }

    [Benchmark]
    public string StringBuilder_() { var sb = new StringBuilder(); foreach (var i in _items) sb.Append(i); return sb.ToString(); }

    [Benchmark]
    public string StringJoin() => string.Join("", _items);
}
// Program.cs:  BenchmarkRunner.Run<StringJoinBenchmarks>();

Run in Release: dotnet run -c Release.

Pitfalls the skill prevents

  • Benchmarking in Debug, or without BenchmarkRunner (JIT/optimizations differ).
  • Dead-code elimination — return the result so the work isn't optimized away.
  • Setup work inside the [Benchmark] method — move it to [GlobalSetup].
  • Comparing without a [Baseline] — you need a reference point.
  • Tiny inputs that measure noise, not the algorithm.

Reading the summary

  • Mean (ns/us/ms) — central tendency; check StdDev/Error for noise.
  • Ratio — vs baseline (the headline comparison).
  • Gen0/1/2 + Allocated — allocations and GC pressure; often the real story, not raw time.
  • A faster method that allocates 10× more may lose under real load — weigh both.

Principles

  • Measure the realistic input size and shape, not a toy.
  • Allocations are a first-class result — keep [MemoryDiagnoser] on.
  • One variable at a time; keep benchmarks isolated and deterministic.
  • Don't micro-optimize a path that isn't hot — confirm it matters first (hotpath-profiler-assistant).

Read the full file on GitHub · 60 lines

Files

What ships with it

1 file 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. 2d ago First seen · 60 lines · 100 tokens per session scan A e4b255f2e07b

Subscribe to this mod's changes

benchmarkdotnet-setup is a skill published in the GitHub repository StefanTheCode/dotnet-ai-toolkit (19 stars, last pushed 23d ago), licensed MIT. It adds 100 tokens to every session and 693 once invoked, about $0.0005 per session on Opus 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 2026-08-30.

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