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multi-brain-score多脑评分

Agent Skill

multi-brain-score 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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

371

周安装

15

GitHub Stars

4

下载量

116
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:multi-brain-score(多脑评分)
来源仓库:https://github.com/fatih-developer/fth-skills
仓库路径:skills/multi-brain-score
安装命令:
npx skills add https://github.com/fatih-developer/fth-skills --skill multi-brain-score
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/fatih-developer/fth-skills --skill multi-brain-score

简介

multi-brain-score 用于查找、检索和筛选相关信息。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词、任务场景或来源线索快速定位候选结果时使用。
  • 支持基于语义匹配、标签过滤和上下文相关性进行智能内容检索与排序。
  • 安装命令为 npx skills add https://github.com/fatih-developer/fth-skills --skill multi-brain-score。
  • 使用前需确认权限范围、维护状态,注意可能触发联网、命令执行或文件读写操作。

SKILL.md

Multi-Brain Score Protocol

Add quantified confidence scoring to any multi-brain decision. Each perspective rates its own confidence, and the consensus uses scores as decision weights. Uncertainty becomes visible instead of hidden.


Workflow

1. Run base multi-brain (3 perspectives)
2. Each instance scores its confidence (1-10)
3. Weighted consensus based on scores
4. Flag uncertainty zones
5. Produce full output with scores visible

Step 1: Perspectives with Scores

Each instance provides their perspective plus a confidence score:

## 🧠 Brainstorm (Scored)

**Instance A — Creative:** (Confidence: 6/10)
[2-3 sentences]
_Confidence rationale: Novel approach but limited precedent in production._

**Instance B — Pragmatic:** (Confidence: 9/10)
[2-3 sentences]
_Confidence rationale: Well-established pattern, used this successfully before._

**Instance C — Comprehensive:** (Confidence: 7/10)
[2-3 sentences]
_Confidence rationale: Good coverage of risks but missing data on edge case X._

Step 2: Score Analysis

Before consensus, analyze the confidence landscape:

## 📊 Confidence Analysis

| Instance | Score | Strength | Weakness |
|----------|-------|----------|----------|
| A — Creative | 6/10 | High potential impact | Unproven approach |
| B — Pragmatic | 9/10 | Battle-tested | May miss innovation |
| C — Comprehensive | 7/10 | Risk-aware | Incomplete data |

**Average Confidence:** 7.3/10
**Spread:** 3 points (moderate disagreement)
**Highest Confidence:** Instance B

Step 3: Weighted Consensus

Use confidence scores to weight the consensus:

  • High confidence (8-10): This perspective's core recommendation carries heavy weight.
  • Medium confidence (5-7): Consider as a modifier or secondary input.
  • Low confidence (1-4): Flag as an area needing more research before deciding. Do not ignore — surface it as a risk.
## ⚖️ Weighted Consensus

**Primary direction:** [Based on highest-confidence perspective]
**Modified by:** [Elements from medium-confidence perspectives]
**Flagged for research:** [Low-confidence areas that need validation]

**Overall Decision Confidence:** [Weighted average]/10

Step 4: Uncertainty Flags

If any perspective scores below 5, or if the spread between scores is > 4:

> ⚠️ **Uncertainty Alert:** [Description of what is uncertain and what would resolve it]

Step 5: Full Output

Mandatory: The final response must include all scored perspectives, the confidence analysis table, the weighted consensus, any uncertainty flags, and the complete deliverable.


Scoring Rubric

ScoreMeaningWhen to Use
9-10Near-certainStrong evidence, proven pattern, minimal unknowns
7-8ConfidentGood reasoning, some minor unknowns
5-6ModerateReasonable approach but notable gaps
3-4LowSpeculative, lacks supporting evidence
1-2GuessNo solid basis, flagging for transparency

Guardrails

  • Always show scores inline with perspectives — they are part of the deliverable.
  • Confidence rationale is mandatory — a bare number without explanation is useless.
  • Never inflate scores — honest uncertainty is more valuable than false confidence.
  • If all scores are below 5, recommend more research before deciding instead of forcing a weak consensus.
  • Scores should create action items — low scores become "things to validate."
  • This protocol can be combined with base multi-brain or multi-brain-experts.

References

  • See references/EXAMPLES.md for scored decision examples.

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

按任务关键词查找相关 Skills

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

34.86%
按下载量换算40

Claude

29.44%
按下载量换算34

Cursor

19.17%
按下载量换算22

Gemini CLI

8.47%
按下载量换算10

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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