diagnose

diagnose is a command for coding agents from jkitchin/discopt. It costs 64 tokens per session (1,479 once invoked), scanned A, original, EPL-2.0.

A command for interpreting the result returned by a discopt optimization solve. It explains the solve status, solution gap, runtime, and performance of different model layers.

In plain words
What is it for?
Use it after a solve finishes with a result such as optimal, feasible, or time-limited, to decide what to inspect or improve next.
Why use it?
It turns solver output into concrete information about whether the result is usable and what may be slowing the solve down.

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/diagnose
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 diagnose

README.md
[![agentmods](https://agentmods.dev/badge/commands/jkitchin/discopt/diagnose.svg)](https://agentmods.dev/commands/jkitchin/discopt/diagnose)
Your own site
<a href="https://agentmods.dev/commands/jkitchin/discopt/diagnose"><img src="https://agentmods.dev/badge/commands/jkitchin/discopt/diagnose.svg" alt="Measured on agentmods" height="20"></a>
Per session 64 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,479 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 $0.00064 $0.01479
Opus 5 $0.00032 $0.00740
Sonnet 5 $0.00013 $0.00296
Haiku 4.5 $0.00006 $0.00148

Measured 4d ago against content hash 3bfb76f74214, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

diagnose 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/diagnose.md · 111 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 · 111 lines · 64 tokens per session scan A 3bfb76f74214

Subscribe to this mod's changes

diagnose is a command published in the GitHub repository jkitchin/discopt (25 stars, last pushed today), licensed EPL-2.0. It adds 64 tokens to every session and 1,479 once invoked, about $0.0003 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.