Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/trilwu/gauntlet-loop-skillsnpx agentmods add skills/trilwu/gauntlet-loop-skills/gauntlet-loopWrote 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/trilwu/gauntlet-loop-skills/gauntlet-loop)<a href="https://agentmods.dev/skills/trilwu/gauntlet-loop-skills/gauntlet-loop"><img src="https://agentmods.dev/badge/skills/trilwu/gauntlet-loop-skills/gauntlet-loop.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 | $0.00227 | $0.02153 |
| Opus 5 | $0.00113 | $0.01077 |
| Sonnet 5 | $0.00045 | $0.00431 |
| Haiku 4.5 | $0.00023 | $0.00215 |
Grade A, and why
gauntlet-loop 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 6d 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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Gauntlet Loop — core skill (domain-agnostic)
A disciplined loop for producing high-quality work in any domain: build → blind critique → revise → repeat, against a bar the agent cannot talk its way past. Coding, writing, design, data, research, prompt engineering and detection all use the same loop — they differ only in how you pick the bar, what the critic inspects, and the checklist. Those live in references/domains/.
Origin: the Gauntlet Loop method is Matt Shumer's, from "How to Run a Gauntlet Loop" (https://somethingbig.ai/gauntlet-loop), with the companion repo
mshumer/Claude-of-Duty. This skill is an independent, attributed adaptation — see../../CREDITS.md.
Prerequisite: a real agent harness
This loop requires an agentic harness that can open files, run code, render output, take screenshots, call tools, and spawn sub-agents with their own clean context (Claude Code, Cowork, Codex, or equivalent). A single-turn chat cannot produce an independent blind critic — if you can only reply in one context, say so and fall back to a single self-review pass instead of pretending the loop ran.
For large multi-agent runs, use the harness's parallel-agent facilities (in Claude Code: sub-agents, or /ultracode when available).
The four pillars
- A bar the agent cannot argue around. The strongest form: the artifact must match or beat something real — a reference implementation, a test suite, a top product's screenshot, a model essay, an eval set, an ATT&CK technique, a working PoC. The bar may be aspirational: it does not have to be realistically reachable. An unreachable bar keeps the loop pulling upward instead of stopping at "good enough".
- Give the goal, not the implementation. State what the finished thing must achieve; let the agent choose the route. Prescribing architecture replaces the model's judgment with yours and caps the result at your imagination.
- Let the agent split the work. The lead breaks the goal into the smallest pieces that can be improved and graded independently. Independent pieces can run as parallel loops.
- The builder never grades itself. The builder and the judge are different agents with separate context. The critic is blind to the builder's reasoning and inspects the real artifact — running code, rendered pixels, actual test output — never the builder's summary.
What ships with it
12 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.
- references/choosing-the-bar.md 4.1 KB
- references/critic-design.md 3.5 KB
- references/domains/coding.md 8.0 KB
- references/domains/data-analysis.md 3.6 KB
- references/domains/design.md 4.5 KB
- references/domains/detection.md 8.3 KB
- references/domains/prompt-eval.md 4.9 KB
- references/domains/research.md 4.1 KB
- references/domains/writing.md 5.0 KB
- references/methodology.md 5.9 KB
- references/prompt-templates.md 6.8 KB
- references/running-the-loop.md 4.8 KB
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.
- 6d ago First seen · 116 lines · 227 tokens per session scan A ad913e43b34b
gauntlet-loop is a skill published in the GitHub repository trilwu/gauntlet-loop-skills (3 stars, last pushed 1mo ago), licensed MIT. It adds 227 tokens to every session and 2,153 once invoked, about $0.0011 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 2026-08-31.
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