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best-minds-optimizer最佳头脑优化器

Agent Skill

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。

总安装

514

周安装

21

GitHub Stars

公开资料未说明

下载量

166
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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/best-minds-optimizer
仓库路径:skills/best-minds-optimizer
安装命令:
npx skills add https://github.com/moghenry/best-minds-optimizer --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/best-minds-optimizer --skill best-minds-optimizer

简介

best-minds-optimizer 用于优化提示词,通过识别领域专家框架提升 LLM 响应质量。

  • 适用于需要精准、结构化输出的复杂任务,如架构决策或代码重构。
  • 自动将用户问题转化为专家视角的提示,提升模型回答的相关性和深度。
  • 安装前需确认是否支持联网及权限范围,避免触发未授权操作或数据访问。
  • 建议结合具体用例验证其优化效果与适用边界。

SKILL.md

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.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

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能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

35.69%
按下载量换算59

Claude

29.5%
按下载量换算49

Cursor

18.92%
按下载量换算31

Gemini CLI

8.62%
按下载量换算14

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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