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Agent Skill · 待分类

best-minds-optimizer(最佳头脑优化器)

best-minds-optimizer 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

best-minds-optimizer 是待分类类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描已通过,按安装量在待分类类的 11,209 个 Skill 里排第 9,612。

总安装 235周安装 10GitHub Stars 公开资料未说明下载 82核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:best-minds-optimizer(最佳头脑优化器)
来源仓库:https://github.com/moghenry/superminds
仓库路径:skills/best-minds-optimizer
安装命令:
npx skills add https://github.com/moghenry/superminds --skill best-minds-optimizer
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/moghenry/superminds --skill best-minds-optimizer

这个 Skill 是做什么的

这个 Skill 的分类还没最终确定,用法以来源仓库的说明为准。

用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息。适合在 Codex、Claude、Cursor、Gemini CLI 中围绕仓库状态和代码变更进行整理。通过 npx skills add 命令从指定仓库安装并使用。安装前需确认权限范围、维护状态,以及是否会触发联网或文件操作。

建议结合原始 README 核验具体用法和功能边界。

来源仓库内路径:skills/best-minds-optimizer

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/moghenry/superminds --skill best-minds-optimiz…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

best-minds-optimizer 怎么安装?

在终端执行 npx skills add https://github.com/moghenry/superminds --skill best-minds-optimizer。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

best-minds-optimizer 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

best-minds-optimizer 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

best-minds-optimizer 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

best-minds-optimizer 用的人多吗?

公开数据是累计安装 235 次,近一周安装 10 次,下载 82 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

best-minds-optimizer 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理待分类类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

待分类类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
infsh-cli

infsh CLI · Codex、Claude

4,374,576382权限未标注未明确未收录
ai-video-generation

AI 视频生成 · Codex、Claude

4,313,376382调用外部服务未明确有提醒
lark-openapi-explorer

云雀 OpenAPI 浏览器 · Codex、Claude

2,064,1209,011需要联网未明确有提醒
lark-workflow-standup-report

Lark 工作流程站立报告 · Codex、Claude

2,041,6328,978需要联网未明确通过
use-my-browser

使用我的浏览器 · Codex、Claude

1,848,49140操作浏览器未明确未通过
best-minds-optimizer

最佳头脑优化器 · 本页

235—只读未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Best Minds — Prompt Optimizer

A pre-processing layer that optimizes prompts before execution. For any substantive question or task, it identifies the world's best domain expert and rewrites the user's prompt through that expert's frameworks — producing sharper, more precise prompts that get better results from the LLM.

The core insight: LLMs are simulators. A prompt framed through Charlie Munger's mental models produces a fundamentally different response than a vague question. This skill applies that insight automatically.

Pipeline

Step 1: Triage — Skip, Polish, Clarify, or Optimize

Before doing anything, classify the user's prompt into one of four lanes:

Optimize — The prompt is substantive and clear enough that expert framing will sharpen it:

  • The question has a definite problem structure even if the user phrased it loosely
  • Clarification would just delay an obviously useful rewrite
  • The user explicitly says "just give me your take"
  • Read references/optimize.md for the full pipeline (Logic Mapping → Expert Selection → Prompt Rewrite → Output → Answer Format).

Polish — The prompt is multi-sentence or contains user-authored text that would benefit from a quick wording pass:

  • Any prompt where the user composed multiple sentences, specified requirements, or provided context — regardless of whether the task is mechanical, creative, or analytical
  • The user explicitly asks to "polish", "clean up", "improve wording", or similar
  • Rule of thumb: if the user wrote more than one short sentence, it's worth a polish. The user invested effort in composing the prompt — a quick wording pass respects that effort.
  • Read references/polish.md for detailed instructions. Do NOT run the full optimization pipeline.

Clarify — The prompt is substantive but could go in meaningfully different directions:

  • The user's situation, constraints, or goals are unclear
  • The answer would change significantly depending on unstated context
  • Read references/clarify.md for detailed instructions. After gathering context, proceed to Optimize.

Skip — The prompt is a short, simple instruction where polishing adds no value:

  • Single-sentence commands with no user-authored detail ("read this file", "commit this", "rename X to Y")
  • Follow-up messages in an ongoing conversation where the prompt is already refined (see Follow-up Handling below)
  • Only skip if the prompt is roughly one short sentence with no requirements, constraints, or context. If the user wrote more than one sentence, route to Polish instead.
  • Emit status: "skipped" (or skip JSON entirely) and proceed with the original prompt unchanged.

Step 2: Confirmation — Review Gate

After triage completes and the optimization pipeline runs (Steps 1.5–3), you must pause and present the Review Gate before executing. This is mandatory — never skip it.

Review Gate display format:

### Review Gate

**Expert Persona**: [Name] — [one-line rationale]
**Reasoning Framework**: [Logic Structure type] — [framework applied, e.g., First Principles / Inversion]
**Key Metrics**: [2–3 North Star metrics]

**Rewritten Prompt**:
> [The full optimized prompt from Step 3]

---
⏳ **Awaiting your authorization.** Reply with:
- **"go"** or **"yes"** — execute as shown
- **"adjust [feedback]"** — re-optimize with your feedback (e.g., "adjust — use a different expert" or "adjust — focus more on pricing")
- **"skip"** — abandon optimization and answer the original prompt directly

Rules:

  • Never execute the optimized prompt until the user explicitly authorizes. Silence is not consent — wait for a response.
  • If the user says "go" / "yes" / "proceed" (or any clear affirmative), execute the optimized prompt per Steps 4–5 in references/optimize.md.
  • If the user provides adjustment feedback, return to Step 2 (Expert Selection) or Step 3 (Rewrite) as appropriate, then present the Review Gate again with the revised output.
  • If the user says "skip", abandon the optimization and answer the original prompt directly without expert framing.
  • The Direction-shift pause in references/optimize.md Step 4 is now subsumed by this gate — the Review Gate already surfaces the reframe for user review, so no separate pause is needed.

Follow-up Handling

When the user follows up on an already-optimized answer (e.g., "tell me more about point 3", "what about the pricing angle?", "can you elaborate?"):

  • Do NOT re-optimize. The expert and framework are already established.
  • Stay in the same expert's voice and go deeper on the specific point requested.
  • Maintain plain-English delivery — the Bilingual Execution rule still applies.
  • Only re-optimize if the follow-up is a genuinely new question that shifts the problem domain (e.g., the original was about pricing and the follow-up is about hiring).

Reference Files

FileWhen to readWhat it contains
references/optimize.mdTriage → OptimizeSteps 1.5–5: Logic Mapping, Expert Selection, Prompt Rewrite, Output Format, Answer Format + examples + common mistakes
references/methodology.mdStep 3 of Optimize, Polish lane4-D prompt optimization methodology: Deconstruct → Diagnose → Develop → Deliver
references/polish.mdTriage → PolishPolish instructions + example
references/clarify.mdTriage → ClarifyClarify instructions + example, then routes to optimize.md

Common Mistakes (Quick Reference)

MistakeWhy it matters
Optimizing trivial tasksAdds friction where there's no value — users notice and get annoyed
Skipping multi-sentence promptsIf the user wrote more than one short sentence, they invested effort in composing the prompt — always route to Polish at minimum, never Skip
Assuming intent on ambiguous promptsAsk 2–3 targeted clarifying questions first — don't guess at context that changes the answer
Re-optimizing follow-upsWhen the user asks "tell me more about point 3," go deeper in the same expert's framework — don't re-run the full pipeline
Executing before Review Gate authorizationNever execute the optimized prompt until the user explicitly says "go", "yes", or equivalent — silence is not consent

See references/optimize.md for the full common mistakes table specific to the optimization pipeline.

平台分布

Codex

34.52%
按下载量换算28

Claude

29.78%
按下载量换算24

Cursor

20.48%
按下载量换算17

Gemini CLI

10.68%
按下载量换算9

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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