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.
npx skills add danecwalker/agentskills --skill gauntletgit clone --depth 1 https://github.com/danecwalker/agentskillsWrote 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/danecwalker/agentskills/gauntlet)<a href="https://agentmods.dev/skills/danecwalker/agentskills/gauntlet"><img src="https://agentmods.dev/badge/skills/danecwalker/agentskills/gauntlet/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/danecwalker/agentskills/gauntlet"><img src="https://agentmods.dev/badge/skills/danecwalker/agentskills/gauntlet.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00120 | $0.04328 |
| Opus 5.5 | $0.00048 | $0.01731 |
| Sonnet 5.5 | $0.00024 | $0.00866 |
| Haiku 4.5 | $0.00012 | $0.00433 |
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 2mo 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 — 372 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/gauntlet — Gauntlet Loop
Run an ambitious goal through a Gauntlet Loop: split, build, score against a real bar with a separate critic, fix the biggest gap, repeat. Do not stop after one "pretty good" pass. Default stop for quality is 10/10 on critical pieces.
Method source of truth: https://somethingbig.ai/gauntlet-loop
Reference prompt style: https://github.com/mshumer/Claude-of-Duty/blob/main/prompt.md
Usage
/gauntlet <goal>
/gauntlet <goal> — bar: <reference and/or acceptance criteria>
Examples:
/gauntlet build a AAA-feeling FPS in Three.js — bar: modern Call of Duty screenshots/gauntlet rewrite the landing page — bar: linear.app and stripe.com homepage quality/gauntlet harden the auth service — bar: OWASP ASVS L2 + our test suite green + p99 < 50ms/gauntlet ship checkout — bar: acceptance criteria: guest checkout in <3 min; card + wallet; clear error copy; WCAG AA on payment form
If the user only says /gauntlet with no goal, ask once for the goal (and optional bar).
Core rules (never violate)
- Goal, not architecture. Tell workers what to achieve. Do not prescribe full architecture, exact file trees, or step-by-step implementation unless the user already constrained them.
- Hard bar only. "Make it amazing / production-ready / better" is not a bar.
The bar must be inspectable. It may be any of:
- Reference — screenshots, URLs, products, style samples, repos
- Acceptance criteria — explicit checklist, tests, SLOs, security bars
- Hybrid — both (common and preferred when available)
- Split into smallest improvable pieces. The lead agent decomposes. Vague "improve everything" loops fail; focused pieces succeed.
- Builder never grades itself. Every piece gets a separate critic with fresh context (no builder history, rationales, or excuses).
- Inspect the real artifact. Critics look at pixels, running product, rendered pages, test output, or finished text — never a builder summary.
- Score out of 10. Loop to 10/10. Every critic returns an integer SCORE: 0–10. PASS only at 10/10. Scores 0–9 are FAIL. Keep looping on each piece until it is 10/10, the user stops, or the user explicitly accepts a lower score after you show the board.
- Score the bar, not a contest. The only pass condition is 10/10 against the bar. A reference (URL, screenshot, product) is evidence and a quality standard — use it to judge gaps. Do not report OURS vs REFERENCE winners. When the bar is criteria-only, check the checklist; do not invent a fake competitor.
- Board first. Right after you parse the request, create
gauntlet-progress.htmlfrom the skill template and open it in the browser. Do this before bar debate, the lead plan, or any builders. Keep the open tab alive with edit-in-place updates.
What ships with it
4 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.
- 2mo ago First seen · 372 lines · 120 tokens per session scan A b4da858947a0
gauntlet is a skill published in the GitHub repository danecwalker/agentskills (2 stars, last pushed 2mo ago), licensed MIT. It adds 120 tokens to every session and 4,328 once invoked, about $0.0005 per session on Opus 5.5. 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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