gauntlet-loop

robonuggets/gauntlet-loop · Agent Skill

生成一個簡短、可直接貼上的提示,供代理設定品質標準、分解任務、執行任務並重複執行直到達成目標。

前往安裝 ↓

總覽

發布者robonuggets社群
儲存庫robonuggets/gauntlet-loop ↗
技能資料夾gauntlet-loop
星827
附帶檔案1
授權CC-BY-4.0
最近更新2026年8月4日
連結技能原始碼 ↗ · 儲存庫 ↗

Mozgi 檢查

我們在技能檔案中發現的內容。詳細資訊需訂閱。

安全性
中等風險 · 發現 1 個需要注意的地方
相容性
僅含說明 — 適用於多數代理
相似技能
找到 51 個相似技能
訂閱另含

安裝

技能就是一個含 SKILL.md 的資料夾。選擇你的代理,在專案根目錄的終端機(macOS、Linux 或 Windows 的 Git Bash)執行指令。

把提示貼到 Claude、Codex 或其他助理:它會自行檢查並安裝技能。

Install the “gauntlet-loop” skill from mozgi.io (https://mozgi.io/skills/gauntlet-loop/) for me.

1. Download the archive: https://mozgi.io/skills/gauntlet-loop/download
   If it fails, take the skill folder from the source: https://github.com/robonuggets/gauntlet-loop/tree/main/.claude/skills/gauntlet-loop
2. Read SKILL.md and the other files. Check the skill is safe: no hidden commands, no sending data elsewhere, no scripts doing something other than described. If anything looks suspicious, stop and tell me.
3. Put the “gauntlet-loop” folder into your agent's skills folder: .claude/skills/ for Claude Code, .agents/skills/ for Codex, .cursor/skills/ for Cursor, .gemini/skills/ for Gemini CLI. Inside the project if we are in one, otherwise in the home folder (~/.claude/skills/ etc.).
4. Make sure <skills folder>/gauntlet-loop/SKILL.md exists.
5. Briefly tell me what this skill does and offer to try it on my task.

If you cannot do a step yourself, tell me what to click or type.

技能檔案

1 個檔案 下載 ZIP

安裝前請先檢視 SKILL.md 與相關檔案。點擊檔案即可預覽。

SKILL.md 內容

作者原文 · 唯讀

Gauntlet Loop

The user gives a goal. You give back ONE short prompt they can paste into a fresh agent session.

You are not doing the work. You are writing the prompt that makes another agent grind on the work until it beats a real reference.

Flow

1. Read the goal. One line restatement in your head, not on screen.

2. Set the bar. If the user supplied a reference, use it. If not, offer 2 or 3 candidate bars, one line each, and stop. Wait for their pick. Do not write the prompt yet.

3. Write the prompt. One block, paste-ready, no preamble, no headings inside it, no narration after it.

4. Offer to run it. One flat line under the prompt: "I can run this here." Not a question.

If they say run it, you become the lead agent and follow the prompt you just wrote.

The bar is the whole trick

Everything else in a gauntlet loop is scaffolding. The loop only produces quality if the thing it compares against is real.

A bar has to pass three tests:

  • Named. A specific thing, not a category. "Stripe's pricing page" works. "Award-winning SaaS sites" does not.
  • Fetchable. The critic can actually get it - screenshot the live page, read the published piece, run the binary, open the repo, watch the footage. If the agent cannot obtain it, it will hallucinate the comparison.
  • Comparable. Both can sit side by side and a judge can pick one. If you cannot imagine the A/B, it is not a bar.

Bars by goal type:

| Goal | Bar that works |

|---|---|

| Website, app, UI | The live site of a specific best-in-class product, screenshotted at the same viewport |

| Game, 3D, visual | Real footage or screenshots from a named shipped title |

| Writing | A specific published piece by a named author or publication, same length and format |

| Code, tooling | A named repo's implementation, plus its benchmark or test suite as the measurable half |

| Research, analysis | A named analyst report or a paper's methods section, judged on rigour and coverage |

| Deck, doc, deliverable | A real artifact from a firm known for it, same page count |

When you propose bars, prefer the hardest one the agent can genuinely reach. A bar that is too easy makes the loop exit on round one.

If the goal has a measurable half (load time, token cost, benchmark score, word count, pass rate), name it alongside the reference. Taste plus a number beats taste alone.

Prompt template

Adapt the wording every time. Fill the brackets, keep it short, keep the last line.

Build [GOAL].

The bar is [BAR]. Get the real thing first and compare against it directly, not against a description of it.

Break this into the smallest pieces that can be improved and judged on their own. For each piece, fan out a builder and a separate critic with fresh context. The critic inspects the actual output, puts it next to the bar blind with the labels stripped, says which one is better, and names the single biggest remaining gap. Then it goes back to the builder.

The critic should be a harsh critic. Praise is not useful. If ours does not win, it keeps going.

/loop on each piece until the critic picks ours blind. Do not stop before that.

Keep a live progress page updating as the work evolves so I can watch it.

Fan out subagents and ultracode.

Rules for what you fill in:

  • Bake the bar in as a concrete, fetchable thing. URL, product name, repo, title.
  • Add a budget or cost ceiling line only if the user named one. No default cap.
  • Add tool names only if the goal needs them (image or video generation, a browser, a deploy target).
  • Everything else stays out. No architecture, no file layout, no decomposition, no round count, no stack choice unless the user demanded it. The agent decides those, and it decides better than a spec written before the work started.

Length and voice

Short. Around 120 to 180 words. If the prompt needs a heading to stay readable, it is too long.

Plain sentences. No bullet lists inside the prompt. It should read like someone telling an agent what perfect looks like and refusing to accept less.

Portability

/loop and ultracode are Claude Code features. /loop reruns the prompt on an interval or lets the model pace itself. ultracode opts the turn into multi-agent orchestration.

For any other agent, swap the last two lines for: "Keep looping until the critic picks ours. Run the builders and critics as parallel subagents." The structure carries over unchanged.

Two filled examples

Visual goal. User: "landing page for my running brand, athletic, green and dark, has to feel alive."

Bars offered: A) Nike's current running campaign page B) On Running's homepage C) Gymshark's product landing page. User picks A.

Build a landing page for a running brand. Athletic, peak performance, green and dark, energetic, aimed at a young healthy audience. It needs to be interactive and visually unmistakable.

The bar is Nike's current running campaign page. Screenshot it at desktop and mobile and compare against those directly, not against a description of them.

Break this into the smallest pieces that can be improved and judged on their own - hero, motion, type, colour, imagery, interaction, mobile. For each piece, fan out a builder and a separate critic with fresh context. The critic opens the real page in a browser, puts our screenshot next to Nike's blind with the labels stripped, says which is better, and names the single biggest remaining gap. Then it goes back to the builder.

The critic should be a harsh critic. Praise is not useful. If ours does not win, it keeps going.

/loop on each piece until the critic picks ours blind. Do not stop before that.

Keep a live progress page updating as the work evolves so I can watch it.

Fan out subagents and ultracode.

Non-visual goal. User: "a 2000-word explainer on vector databases for non-engineers."

Bars offered: A) a specific Stripe engineering blog explainer B) a named Julia Evans post C) the Wikipedia article plus a comprehension test. User picks B.

Write a 2000-word explainer on vector databases for readers who are smart but not engineers.

The bar is Julia Evans' writing on hard technical topics. Pull three of her actual posts and compare against them directly, not against a description of her style.

Break this into the smallest pieces that can be judged on their own - the opening, each explanation, the diagrams, the analogies, the ending. For each piece, fan out a writer and a separate critic with fresh context. The critic reads ours and hers blind with the bylines stripped, says which one a non-engineer would understand faster, and names the single biggest remaining gap. Then it goes back to the writer.

The critic should be a harsh critic. Praise is not useful. If ours does not win, it keeps going.

/loop on each piece until the critic picks ours blind. Do not stop before that.

Keep a live progress page updating as the work evolves so I can watch it.

Fan out subagents and ultracode.

What breaks a gauntlet loop

  • A vague bar. The critic invents a comparison and approves everything. Most common failure by far.
  • The builder judging its own work. The critic must be a separate agent with fresh context. It should not know how hard the builder tried.
  • A soft critic. Say "harsh" in the prompt and give it a binary job: which one is better, A or B. Scores out of 10 drift upward every round.
  • Named exit after N rounds. The exit is winning the comparison, or the user stopping the run. Never a round count.
  • Over-specifying. Every extra instruction is one fewer decision the agent makes with its own judgment. Minimal wins.

常見問題

gauntlet-loop 技能有什麼用途?

生成一個簡短、可直接貼上的提示,供代理設定品質標準、分解任務、執行任務並重複執行直到達成目標。

如何安裝 gauntlet-loop?

打開本頁的「安裝」區塊並選擇你的代理 — Claude Code、Codex、Cursor 或 Gemini CLI:那裡有把技能資料夾複製到正確位置的現成指令。也可以複製「透過助理」分頁中的提示,讓助理自行檢查並安裝技能。

gauntlet-loop 適用哪些 AI 模型?

技能是純 Markdown 指示(Agent Skills 格式),因此 gauntlet-loop 適用任何讀取 SKILL.md 的代理:Claude Code、Codex、Cursor、Gemini CLI、GitHub Copilot、OpenCode 等。只有在任務符合技能描述時,模型才會載入指示。

gauntlet-loop 是免費的嗎?

此技能由 robonuggets 公開發布,採用 CC-BY-4.0 授權。技能本身免費 — 你只需向 AI 供應商支付實際使用的 token 費用。

安裝技能安全嗎?

技能是代理會遵循的指示,安裝前請閱讀 SKILL.md 及相關檔案。技能資料夾中的腳本只有在代理執行時才會運行 — 留意它的動作,不要授予多餘權限。

技能和 MCP 伺服器或外掛有何不同?

MCP 伺服器或外掛為模型提供新工具 — 會執行並回傳結果的程式碼。技能提供的是知識與流程:如何處理任務、要遵循哪些步驟、好的成果長什麼樣子。它不需要伺服器或 API 金鑰。

相關技能

所有技能 →