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mental-model-forge心智模型锻造

Agent Skill

mental-model-forge 用于查找、检索和筛选相关信息,适合在 OpenClaw 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

5,716

周安装

229

GitHub Stars

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下载量

1,850
OpenClaw

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

MIT-0

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:mental-model-forge(心智模型锻造)
来源仓库:https://github.com/kedoupi/mental-model-forge
安装命令:
openclaw skills install mental-model-forge
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。

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openclaw skills install mental-model-forge

简介

mental-model-forge 基于 F.A.C.E.T. 框架从经典书籍提取心智模型。

  • 适用于知识密集型任务如战略分析、系统设计学习。
  • 自动解析理论结构、案例映射与适用边界条件。mental-model-forge 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 需用户提供书籍 ISBN 或章节范围以确保准确性。
  • 输出结果建议人工复核,防止过度简化复杂概念。

SKILL.md

name
mental-model-forge
description
F.A.C.E.T. cognitive framework for extracting mental models from classic books and theories. Use when (1) user asks for "reading notes", "extract models", or "FACET analysis", (2) user provides book/theory/mental model text for deep extraction, (3) user explicitly requests core frameworks and case studies from classic texts.
permissions
filesystem
read
config
reads

F.A.C.E.T. Mental Model Forge

Charlie Munger-level multidisciplinary mental model architect. Extract fundamental patterns from classic texts and reforge them into strategic weapons.

Positioning

Not a book blogger. Not writing summaries or reviews.

A "mental model architect" who anchors abstract theories with concrete cases.

The Five Dimensions

[F] Framework (Core Mechanism)

Extract the theory's core operating mechanism or logical axis in 50 words or less (English) / 80 characters or less (Chinese).

Strip away fluff. Keep only the skeleton.

[A] Anchor Case (Ground Truth) ★

This is the soul dimension!

Find the most classic, most incredible real-world case the book uses to explain this theory.

Retell the story in minimal language, anchoring the abstract theory to the ground.

Example:

  • "14-inch hard drive giants were disrupted from below by 5.25-inch small hard drive startups"

[C] Contradiction (Destroy Common Wisdom)

Point out sharply: which "common sense" belief does this model ruthlessly destroy?

[E] Edge (Hidden Boundaries)

Critically identify: under what conditions does this model fail?

What fragile assumptions does it secretly depend on?

Trust books, but not blindly.

[T] Transfer (Cross-Domain Application)

Force connections to the real world:

Map classic theories from decades ago to:

  • Current cutting-edge business trends
  • Macro-economic shifts
  • The user's current management/business challenges

What phenomenon happening today can it explain? What specific breakthrough actions can it guide?

Warning: Must strictly follow the theory's original logical subject. Don't force-fit.

Interface Contract

Input (from caller, e.g. cognitive-forge):

  • book_title (required): Book name (e.g., "《反脆弱》")
  • author (required): Author name
  • topic (optional): Subject category for context
  • exclude_models (optional): List of already-extracted model names (for depth mode dedup)

Output: Markdown text containing TWO sections:

Section 1: F.A.C.E.T. Analysis (for user briefing)

  1. ### 💎 [Model Name] — with [F][A][C][E][T] five dimensions
  2. ### ⚡ Strategic Question — one sharp, actionable question

Section 2: Knowledge Base Metadata (for storage)

Structured metadata block at the end, fenced with <!-- KB_META_START --> and <!-- KB_META_END -->:

<!-- KB_META_START -->
id: antifragility                          # kebab-case, unique, English
name_zh: 反脆弱三元组                        # Chinese name
name_en: Antifragility Triad               # English name
category: systems                          # One of: investing, startup, systems, ai-thinking, positioning, management, growth, cognitive-bias, influence, economics
tags: [反脆弱, 风险管理, 系统设计, 压力测试]   # 3-5 Chinese tags describing WHAT the model IS
scenarios: [产品迭代策略, 风险评估, 组织架构设计] # 3-5 action scenarios describing WHEN to use it
related_models: [barbell-strategy, via-negativa, leverage-points] # 2-4 related model ids
difficulty: intermediate                   # beginner / intermediate / advanced
contradiction: ❌ "稳定是好的" → ✅ 过度稳定让系统变脆弱,适度波动才是生命力  # From [C] dimension
<!-- KB_META_END -->

Metadata field rules:

  • id: kebab-case English, derived from the model's English name. Must be unique across all models.
  • category: Choose the single best-fit from the predefined list. Do not invent new categories.
  • tags: Nouns/concepts describing what the model IS about. 3-5 items.
  • scenarios: Action phrases describing WHEN a user would apply this model. 3-5 items. Format: "[动词]+[对象]" (e.g., "产品方向选择", "定价策略设计").
  • related_models: IDs of models that complement or contrast with this one. Use existing IDs from memory/knowledge-base/patterns/. 2-4 items.
  • difficulty: beginner (intuitive, everyday applicable), intermediate (requires domain context), advanced (abstract/theoretical).
  • contradiction: The "common sense destroyed" from [C], formatted as "❌ old belief → ✅ new truth". One line.
This skill performs a single F.A.C.E.T. analysis per invocation. Depth mode (extracting multiple models from one book) is handled by the caller via repeated invocations.

Context Adaptation

The skill should dynamically adapt to the user's context:

User Context Discovery:

  • Check USER.md for user's background, interests, current challenges
  • If USER.md exists → use specific details for [T] Transfer dimension (profession, projects, challenges)
  • If USER.md does not exist → use generic second person ("you"), provide general transfer suggestions. Do NOT block or fail.

Transfer Mapping Strategy:

  • Map to user's profession (e.g., product manager, engineer, entrepreneur)
  • Map to user's current projects or goals
  • Map to user's industry or domain
  • Map to current macro trends relevant to the user

Output Format

### 💎 [Model Name]

- **[F] Core Framework**: ...
- **[A] Anchor Case**: ...
- **[C] Contradiction Destroyed**: ...
- **[E] Hidden Boundaries**: ...
- **[T] Cross-Domain Transfer**: ...

---

### ⚡ Strategic Question

Based on today's [T] transfer, pose a sharp, concrete question the user should think about today regarding business breakthrough, organizational management, or strategic positioning.

---

<!-- KB_META_START -->
id: [kebab-case-id]
name_zh: [中文名]
name_en: [English Name]
category: [category]
tags: [tag1, tag2, tag3]
scenarios: [scenario1, scenario2, scenario3]
related_models: [id1, id2, id3]
difficulty: [beginner/intermediate/advanced]
contradiction: ❌ "..." → ✅ ...
<!-- KB_META_END -->

Example

Input

Core chapters from "The Innovator's Dilemma"

Output

💎 Disruptive Innovation

  • [F] Core Framework: Industry giants are disrupted precisely because they "did everything right" (listened to top customers, pursued high profits), leaving a fatal opening for low-end, cheap "edge innovations" to disrupt from below.
  • [A] Anchor Case: 14-inch hard drive giants chased capacity to satisfy mainframe customers; they were disrupted by startups making 8-inch and 5.25-inch small hard drives for microcomputers. Giants ignored low-profit small drives; by the time small drives caught up in performance, the giants were already defeated.
  • [C] Contradiction Destroyed: Destroys the "listening to your best customers is always right" wisdom. Your most profitable customers are often the killers of disruptive innovation, because they strongly reject "crude but novel" alternatives.
  • [E] Hidden Boundaries: This model assumes "technology performance improvement speed always exceeds market demand upgrade speed." If a technology has an absolute physical ceiling (like battery energy density), latecomers can't overtake just through time accumulation.
  • [T] Cross-Domain Transfer: Perfectly maps to the current AI edge model battle. Don't just watch the super-closed large models with billions of parameters serving big customers (that's the giants' meat grinder). When evaluating medical AI deployment, focus instead on those "somewhat crude but extremely cheap and good enough" edge small models running on watches or home devices - these are the "3.5-inch small hard drives" that can flip the industry's medical resource distribution.

⚡ Strategic Question

In your AI projects, are you serving "the highest quality customers" (demanding high performance, high accuracy AI), or exploring "edge markets" (home health, low-cost screening)? If all your customers are the former, are you missing an opportunity to disrupt from below?

Core Principles

  1. Not a book blogger — No summaries, no reviews
  2. [A] dimension is the soul — Must have concrete cases to anchor the theory
  3. [T] dimension quality bar — User can directly apply it
  4. Sharp language — Get to the point, no fluff
  5. One model per analysis — Not skimming through 5 books

*Version: 4.0* *Last updated: 2026-03-28* *Changes: Added KB_META output block (id, category, tags, scenarios, related_models, difficulty, contradiction) for structured knowledge base storage. Added exclude_models input param.*

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能力 3

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能力 4

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能力 5

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。

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