What makes Munger Think Partner notable?

1.1K downloads. Trust evidence available

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Munger Think Partner

基于查理·芒格多元思维模型，构建跨学科决策框架，识别认知偏误，辅助投资、商业及风险管理判断。 Skill: Munger Think Partner Owner: gechengling Summary: 基于查理·芒格多元思维模型，构建跨学科决策框架，识别认知偏误，辅助投资、商业及风险管理判断。 Tags: latest:2.1.2, munger-think-partner:2.1.2 Version history: v2.1.2 | 2026-09-18T05:46:43.698Z | user v2.1.2: 五个模型各增补落地举例与新维度表（四类模型提问清单、反向vs正向对照、误判倾向新增11-15项及高发场景自检、投资清单新增两问及执行对照、能力圈判定表）；合并去重重复的2026版小节并新增跨框架交叉验证矩阵与实例；Advanced章节新增四类模型联合用法表与联动举例；新增场景D市场狂热中的仓位决策；新增能力圈与仓位、关于等待两组语录 v2.1.1 | 2026-06-28T13:21:05.417Z |

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Rank

Safety

Downloads

1.1k

Updated

Oct 11, 2026

Version

2.1.2

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.1K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1.1K downloadsadoption · observed Oct 11, 2026
Latest release: 2.1.2release · observed Sep 18, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17ewqc4f2s6gpcbm88hy7fgvn85kg1g:munger-think-partner

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-gechengling-munger-think-partner/snapshot"

Documentation

CLAWHUB

45,961 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

# SKILL.md



## Identity



- **Skill Name**: 芒格多元思维模型专家 (Charlie Munger Latticework Analyst)

- **Slug**: finance-munger-mind

- **Version**: 2.1.2

- **Language**: 中文为主，英文关键术语保留

- **Author**: 葛成 (@gechengling)

- **Description**: 以查理·芒格多元思维模型为核心，帮助用户构建跨学科决策框架，识别认知偏误，避免愚蠢决策。适用于投资分析、商业决策、风险管理等场景。2026更新：新增芒格逝世（2023）后伯克希尔的接班人战略解读，以及芒格对AI/大模型的质疑与价值投资在AI时代的适用性分析。关键词：芒格，伯克希尔，价值投资，多元思维模型，逆向思考，认知偏误，跨学科思维，智慧决策.



---



### 最新动态 [2026-09-18更新]

| 动态类型 | 内容摘要 | 发布时间 | 影响范围 | 用哪个模型看 |
|---------|---------|---------|---------|-----------|
| 逆向思维 | 达利欧AI泡沫警告与芒格反过来想——在市场狂热中如何保持逆向思维 | 2026-06 | 价值投资/AI投资 | 模型二 反向思维 + 模型三 误判心理学 |
| **外部变量权重上升** | 多家机构9月报告将美元流动性、地缘局势与议息会议列为风险资产关键变量 | 2026-09-02 | 全球风险偏好 | 模型一：宏观是格栅中的一层，不可省略 |
| **产业景气与价格背离** | 半导体等产业景气数据强劲，但相关板块出现回撤 | 2026-09 | 科技主线 | 模型五：景气不等于能力圈 |
| **AI监管路径分化** | G20期间美方主张轻触式AI监管，与欧盟AI法案、中国分类分级治理形成三条路径（以官方最新发布为准） | 2026-09-02 | AI产品与合规 | 模型四：合规风险要计入风险清单 |

> **数据截止**: 2026-09-18 | 来源：Bridgewater、路透社、G20公开报道、AI行业研究
> **声明**: 以上动态供参考，具体以官方最新发布为准



## Core Thinking Models



### 模型一：多元思维格栅（Latticework of Mental Models）



**核心思想**：

把多学科的底层规律拼成一张"思维格栅"，用来看透商业、投资、人生。



**芒格原话**：

> "80%～90%的复杂问题，靠几十个基础模型就能解决"



**四大类核心模型**：



**格栅怎么用（本版新增）**：格栅不是把模型背下来，而是遇到问题时**逐个模型过一遍**，

看哪一个能给出别人看不到的解释。通常用 3–5 个模型交叉，比用 1 个模型深挖更可靠。



**举例一（一个问题，四个模型）**：某公司营收连续下滑——

心理学视角看是否存在**激励错位**（销售提成结构导致压货）；

经济学视角看**机会成本**（继续投入是否优于转向）；

生物学视角看**生态位**（它的位置是否已被替代者占据）；

数学视角看**期望值**（扭转的概率 × 收益 vs 失败的概率 × 损失）。

四个模型给出的结论往往不一致，**不一致之处正是需要再查证的地方**。



**举例二（只用单一模型的风险）**：只用经济学模型会高估理性、忽略人的非理性；

只用心理学模型会高估主观、忽略成本约束。格栅的价值正在于**让模型互相纠错**。



**四类模型的提问清单（本版新增维度）**：



| 模型类别 | 核心提问 | 典型误判 |
|---------|---------|---------|
| 心理学 | 谁被什么激励驱动？有哪些偏误在起作用 | 把动机想得过于理性 |
| 经济学 | 机会成本是多少？规模效应是否存在 | 忽略沉没成本与激励扭曲 |
| 生物学 | 它在生态位中是适应还是被淘汰 | 线性外推，忽视竞争替代 |
| 数学 | 概率与期望值是多少？是否可重复 | 用个案代替概率 |



```

┌─────────────────────────────────────────────────────┐

│                 多元思维格栅                        │

├─────────────┬─────────────┬─────────────┬──────────┤

│  心理学模型  │  经济学模型  │  生物学模型  │ 数学模型  │

├─────────────┼─────────────┼─────────────┼──────────┤

│ 社会认同     │ 机会成本     │ 自然选择     │ 复利原理  │

│ 激励反应     │ 规模效应     │ 生态位       │ 排列组合  │

│ 确认偏误     │ 沉没成本     │ 适应性       │ 概率论    │

│ 损失厌恶     │ 竞争优势     │ 共生关系     │ 贝叶斯    │

│ 嫉妒心理     │ 边际效用     │ 冗余设计     │ 期望值    │

└─────────────┴─────────────┴─────────────┴──────────┘

```



### 模型二：反向思维（Invert, Always Invert）



**核心方法**：

> "如果知道我会死在哪里，我就永远不去那个地方"



**实操步骤**：

```

第一步：正向思考"如何成功"

第二步：反过来问"如何必然失败"

第三步：避免那些导致失败的行为

第四步：剩下的就是相对可靠的路径

```



**适用场景**：

- 投资决策：先分析什么会让这笔投资血本无归

- 职业选择：先看什么会让这个职业毁掉你

- 创业分析：先研究为什么大多数创业公司会失败



**举例一（反向思维的完整走法）**：目标是让产品留得住用户。

反向问：**怎样做一定会让用户流失？** 答：启动慢、首屏无价值、频繁打扰、出错无反馈。

于是正向动作变成：把首屏加载压到最低、首次使用即给出结果、默认关闭推送、错误给出可执行提示。

**反向清单比正向愿望更容易转成待办事项**，这是它的实操价值。



**举例二（反向思维的盲区）**：反向思维擅长**排除已知失败路径**，

_meta.json

{
  "ownerId": "kn74e704j3ygjcygnpf02rdvd185js13",
  "slug": "munger-think-partner",
  "version": "2.1.2",
  "publishedAt": 1789710403698
}

skill-card.md

## Description:

Provides a Chinese-first Charlie Munger latticework decision framework for investment, business, career, and risk-analysis questions.

This skill is ready for commercial/non-commercial use.

## Publisher:

[gechengling](https://clawhub.ai/user/gechengling)

### License/Terms of Use:

MIT-0

## Use Case:

External users, investors, operators, and career decision makers use this skill to structure decisions with Munger-style mental models, inversion, bias checks, and ability-circle analysis. It is an educational decision aid, not licensed financial advice or trading instruction.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Finance-related guidance could be mistaken for licensed financial advice or trading instruction.

Mitigation: Treat outputs as educational analysis, verify current market claims independently, and do not rely on the skill as instructions to trade.

Risk: The artifact includes market and policy context that may become outdated.

Mitigation: Check current official or authoritative sources before using the analysis for investment, business, or compliance decisions.

## Reference(s):

- [ClawHub skill page](https://clawhub.ai/gechengling/skills/munger-think-partner)
- [Publisher profile](https://clawhub.ai/user/gechengling)
- [Skill source artifact](artifact/SKILL.md)

## Skill Output:

**Output Type(s):** [Guidance, Markdown, Text]

**Output Format:** [Markdown with structured Chinese-language decision-analysis sections]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Finance-related educational reasoning; users should independently verify current market claims.]

## Skill Version(s):

2.1.2 (source: server release evidence and artifact Identity section)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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