Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add oprogramadorreal/optimus-claude/plugin install optimusWrote 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.
[](https://agentmods.dev/skills/oprogramadorreal/optimus-claude/gauntlet)<a href="https://agentmods.dev/skills/oprogramadorreal/optimus-claude/gauntlet"><img src="https://agentmods.dev/badge/skills/oprogramadorreal/optimus-claude/gauntlet.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00105 | $0.02268 |
| Opus 5 | $0.00053 | $0.01134 |
| Sonnet 5 | $0.00021 | $0.00454 |
| Haiku 4.5 | $0.00011 | $0.00227 |
Grade A, and why
gauntlet 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.
How it starts
The opening of the file, as written. The whole thing — 181 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Gauntlet
Turn an ambitious goal into a builder/critic improvement loop judged against a concrete quality bar, and run that loop until the output wins or the user stops it.
The user's arguments are the goal, plus any references or quality bars they mentioned. If no goal was given, ask for one.
Gauntlet prompts are modeled on Matt Shumer's prompt for the Claude-of-Duty game:
I want you to build a first-person shooter at the level of the most recent Call of Duty games. It should be utterly perfect, visually beautiful, with every single thing done at AAA quality—from textures to physics to anything you could think of.
Fan out sub-agents and have sub-agents tackle each one individually so that the game is utterly perfect. You should /loop on each item and have a separate sub-agent check it visually to ensure it looks triple A. That separate sub-agent should be a really harsh critic, and if it doesn't look triple A, it should keep going.
Don't stop until each sub-agent is utterly wowed with the quality when compared with the actual Call of Duty game. It should literally compare them side by side blind and say which one looks better. Do this in ThreeJS. /loop until it's utterly perfect. Fan out sub-agents and ultracode.
Copy its register — short, concrete, goal-first, harsh about quality — not its
literal tokens. /loop and ultracode there are one user's shorthand typed
into a live session; as commands they mean something else entirely. Never
carry slash commands or effort keywords into the prompt you write.
1. Choose the bar
Choose the strongest concrete bar that an agent can actually inspect and compare its work against. If the user has not supplied one, propose a useful comp or measurement that plays the same role for this task that real Call of Duty screenshots played for the Claude-of-Duty game.
Then resolve it into something a fresh agent with no prior context can open: file paths, a URL, a command that renders it, or screenshots saved to disk. A bar that cannot be resolved that way cannot be judged against — say so and fall back to the strongest one that can. Explain the chosen bar in one sentence.
What ships with it
2 files 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.
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.
- 2d ago Changed · +24 lines 4f65cba60b9f
- 6d ago First seen · 157 lines · 105 tokens per session scan A ffa833db8cf1
gauntlet is a skill published in the GitHub repository oprogramadorreal/optimus-claude (73 stars, last pushed yesterday), licensed MIT. It adds 105 tokens to every session and 2,268 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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