benchmark-report

benchmark-report is a command for coding agents from jkitchin/discopt. It costs 44 tokens per session (1,123 once invoked), scanned A, original, EPL-2.0.

A reporting command for analysing JSON results from discopt benchmark runs, which measure solver performance on test problems.

In plain words
What is it for?
Use it to review one benchmark run or compare runs, including shifted geometric mean timing, layer profiling, performance profiles, and regression detection.
Why use it?
It turns raw benchmark data into a readable account of solved problems, timing, solution quality, performance differences, and regressions.

Command

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 commands/jkitchin/discopt/benchmark-report
Clone the repo
git clone --depth 1 https://github.com/jkitchin/discopt

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 benchmark-report

README.md
[![agentmods](https://agentmods.dev/badge/commands/jkitchin/discopt/benchmark-report.svg)](https://agentmods.dev/commands/jkitchin/discopt/benchmark-report)
Your own site
<a href="https://agentmods.dev/commands/jkitchin/discopt/benchmark-report"><img src="https://agentmods.dev/badge/commands/jkitchin/discopt/benchmark-report.svg" alt="Measured on agentmods" height="20"></a>
Per session 44 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,123 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin unknown 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 $0.00044 $0.01123
Opus 5 $0.00022 $0.00562
Sonnet 5 $0.00009 $0.00225
Haiku 4.5 $0.00004 $0.00112

Measured 4d ago against content hash 05cc252ee89a, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

benchmark-report 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.

python/discopt/skills/commands/benchmark-report.md · 125 lines

The source is not reproduced here

Licensed EPL-2.0

The repository is licensed EPL-2.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.

Read it on GitHub

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 · 125 lines · 44 tokens per session scan A 05cc252ee89a

Subscribe to this mod's changes

benchmark-report is a command published in the GitHub repository jkitchin/discopt (24 stars, last pushed 4d ago), licensed EPL-2.0. It adds 44 tokens to every session and 1,123 once invoked, about $0.0002 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.