Benchmarking AI Agents Beyond Models

What this Claude Code skill does

Separates harness contribution from model contribution when evaluating AI agent performance, so that benchmark scores and practitioner comparisons reflect real-world system behavior rather than isolated model capability. Use when interpreting AI coding agent benchmark results, when a team's agent is underperforming relative to benchmark expectations, or when leadership is making procurement decisi

Benchmarking AI Agents Beyond Models is a packaged skill for Claude Code: a SKILL.md plus references and a runnable test that drops into .claude/skills/, so your agent can run this workflow reliably instead of you re-explaining it each time. Install it via MCP, run it as a CLI, or fork it into your own.

Inside the SKILL.md

<h1>Benchmarking AI Agents Beyond Models</h1>
<h2>The Benchmark Blind Spot</h2>
<p>Published AI benchmarks almost universally test models in isolation or within a single
reference harness. They compare "brains in jars." The body — where the brain actually
does its work — is not part of the comparison.</p>
<p>This produces a systematic blind spot: the same model can perform radically
differently in production depending on its harness.</p>
<blockquote>
<p>"The same Claude model, identical weights, identical training, scored 78% on the
SWE-bench benchmark inside Claude Code's harness, but scored 42% inside a different
harness built by another startup. Same brain, different body, nearly double the
performance."</p>
</blockquote>
<p>A benchmark score is not a prediction of what that model will do in your environment.
It is a measurement of what that model + reference harness did in the benchmark
environment. These are not the same thing.</p>
<hr />
<h2>The Performance Decomposition Model</h2>
<p>Agent performance in production = model capability × harness multiplier</p>
<p>Where:
- <strong>Model capability</strong>: the intelligence of the underlying weights (what benchmarks measure)
- <strong>Harness multiplier</strong>: context management quality × tool integration depth × memory
…