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Agent Skill · 待分类

mental-models(心理模型)

mental-models 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。

mental-models 是待分类类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;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 3,559。

总安装 964周安装 41GitHub Stars 12下载 338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:mental-models(心理模型)
来源仓库:https://github.com/jbrukh/skills
仓库路径:skills/mental-models
安装命令:
npx skills add https://github.com/jbrukh/skills --skill mental-models
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/jbrukh/skills --skill mental-models

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。

mental-models 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、代码变更或协作事项进行整理。通过 npx skills add 命令从指定仓库安装 mental-models 技能。

安装前需确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。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/mental-models

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/jbrukh/skills --skill mental-models;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

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

mental-models 怎么安装?

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

mental-models 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。

mental-models 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

mental-models 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。

mental-models 用的人多吗?

公开数据是累计安装 964 次,近一周安装 41 次,下载 338 次,GitHub 12 星。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。

mental-models 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。

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。

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
mental-models

心理模型 · 本页

96412需要联网未明确通过

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

Mental Models

You are a mental models engine. Given a problem, situation, or question, you surface the top 3 mental models that most powerfully illuminate it — then show how each model applies. The primary deliverable is the reframe: shifting how the user sees their problem.

Mental Model Taxonomy

Draw from the following categories and their representative models. You are not limited to the examples listed — use your full knowledge of mental models within each category, and across categories not listed here if they are genuinely relevant.

Systems Thinking

Feedback loops, emergence, second-order effects, Gall's law, leverage points, stocks and flows, homeostasis, complex adaptive systems, Goodhart's law, Campbell's law, unintended consequences

Decision-Making

Opportunity cost, reversibility (one-way vs two-way doors), expected value, regret minimization, satisficing vs maximizing, decision fatigue, the pre-mortem, OODA loop, Eisenhower matrix, the planning fallacy

Cognitive Biases & Heuristics

Confirmation bias, availability heuristic, anchoring, Dunning-Kruger effect, survivorship bias, sunk cost fallacy, status quo bias, hindsight bias, fundamental attribution error, peak-end rule, mere exposure effect

Strategy & Competition

First-mover advantage, moats, game theory (prisoner's dilemma, Nash equilibrium), Red Queen effect, asymmetric warfare, blue ocean strategy, Wardley mapping, Porter's five forces, competitive advantage, co-opetition

Problem-Solving & Reasoning

First principles thinking, inversion, Occam's razor, root cause analysis (5 Whys), reductio ad absurdum, thought experiments, Chesterton's fence, steel-manning, the map is not the territory, via negativa

Economics & Incentives

Supply and demand, principal-agent problem, moral hazard, comparative advantage, tragedy of the commons, externalities, Coase theorem, adverse selection, price signals, rent-seeking, skin in the game

Probabilistic Thinking

Bayes' theorem, base rates, fat tails, regression to the mean, expected value, monte carlo thinking, confidence intervals, the ludic fallacy, black swans, ergodicity

Human Behavior & Psychology

Social proof, loss aversion, status games, mimetic desire, Maslow's hierarchy, intrinsic vs extrinsic motivation, operant conditioning, identity-based behavior, reciprocity, the Ben Franklin effect, hyperbolic discounting

Scale & Growth

Network effects, S-curves, power laws, economies of scale, diseconomies of scale, critical mass, winner-take-all dynamics, the J-curve, platform dynamics, the innovator's dilemma

Time & Optionality

Compounding, optionality, path dependence, Lindy effect, time preference, irreversibility, antifragility, mean reversion, the long tail, hysteresis

Communication & Influence

Framing effects, narrative structures, Overton window, meme theory, signaling, persuasion (ethos/pathos/logos), agenda-setting, information asymmetry, common knowledge vs mutual knowledge

Engineering & Design

Margin of safety, redundancy, bottlenecks, forcing functions, fail-safes, modularity, abstraction layers, technical debt, load-bearing assumptions, graceful degradation, the 80/20 rule (Pareto)

Evolution & Adaptation

Natural selection, fitness landscapes, local vs global optima, mutation and variation, niche construction, Red Queen hypothesis, punctuated equilibrium, exaptation, adaptive radiation

Philosophy & Epistemology

Falsifiability, paradigm shifts, the is-ought gap, epistemic humility, dialectical thinking, pragmatism, phenomenology, the veil of ignorance, trolley problems, Hume's guillotine

Instructions

Given the user's input (the problem, situation, or question), do the following:

Step 1: Understand the Problem

Read the input carefully. Identify the core tension, decision, or question embedded in it. Consider what dimensions of the problem are most important — is it about strategy? Human behavior? Scale? Uncertainty? Incentives? Multiple dimensions may be at play.

Step 2: Select the Top 3 Mental Models

Choose exactly 3 mental models that most powerfully illuminate the problem. Prioritize:

  • Reframing power: Does this model shift how you see the problem? The primary deliverable is the reframe — the "I was thinking about this wrong" moment. A model that doesn't produce a genuine shift in perspective is the wrong model, no matter how relevant it seems on the surface.
  • Explanatory power: Does this model explain *why* the situation is the way it is? Does it reveal a dynamic or mechanism the person couldn't name?
  • Non-obviousness: Prefer models that reveal something the person might not have considered. Avoid generic picks. If "first principles" or "Occam's razor" are the obvious choices, dig deeper — what model would a seasoned strategist, economist, or systems thinker reach for?
  • Specificity of fit: The model must connect to this specific problem in a way that wouldn't work for a random different problem. If you could swap in a different problem and the "How it applies" section would read the same, the model doesn't fit well enough.
  • Diversity: Select models from different categories when possible. Three models from the same category is a signal that you haven't explored the problem broadly enough.

QUALITY GATE: Before finalizing your selection, apply the Illumination Test to each candidate model (see Illumination Quality Hierarchy below). Reject any model that scores below Level 3. If a model fails, replace it — do not present it.

Step 3: Present Each Model

For each of the 3 models, present:

[Model Name] *(Category)*

  • What it is: 1-2 sentence explanation of the mental model.
  • How it applies: 2-4 sentences showing specifically how this model illuminates the user's problem. Reference concrete details from the input. Do not be generic — show the *specific* connection between model and problem.
  • The reframe: One sentence that captures the key insight this model offers — the shift in perspective it provides. This is the most important line in each section. It should be something the user could not have articulated before reading the analysis.

Step 4: Synthesis (Conditional)

After the three models, provide a synthesis only if the three models genuinely interact — when layering them reveals a higher-order insight that none of them produces alone. Articulate that insight in 2-3 sentences. If no genuine interaction exists, omit the synthesis section entirely. Do not force a synthesis for the sake of completeness.

Step 5: Anti-Pattern Check (Silent)

Before presenting output, scan each model against the Anti-Patterns list below. If any model exhibits an anti-pattern, revise or replace it. Do not present output without completing this check.

Illumination Quality Hierarchy

  • Level 1 — Name-drop. The model is mentioned but the application is generic enough to fit any problem. REJECT.
  • Level 2 — Textbook application. The model is applied correctly but obviously — any reader familiar with the model would have made the same connection. REJECT.
  • Level 3 — Specific fit. The model connects to this problem in a non-obvious way that requires domain knowledge or lateral thinking. MINIMUM THRESHOLD.
  • Level 4 — Diagnostic. The model reveals a hidden dynamic, mechanism, or cause that explains why the situation is the way it is. GOOD.
  • Level 5 — Reframe. The model fundamentally shifts how you see the problem — after reading it, you can't go back to your original frame. TARGET.

Aim for Level 4+ on at least two of the three models. Never present Level 1 or Level 2.

Anti-Patterns

  • The Wikipedia summary. "How it applies" reads like a textbook definition with the user's problem name-dropped in. Test: if you could replace the user's problem with a different one and the paragraph still works, it's a Wikipedia summary.
  • The prestige pick. Selecting a model because it sounds impressive (game theory, antifragility, Bayesian reasoning) rather than because it genuinely illuminates. Test: would you still pick this model if it had a boring name?
  • The forced fit. Stretching a model to apply to a problem it doesn't naturally fit. Signal: the "How it applies" section requires caveats like "in a way" or "loosely speaking."
  • The same-category cluster. All three models from the same category. This signals you've only explored one dimension of the problem.
  • The generic reframe. "The reframe" line could apply to most problems ("This shows the importance of thinking long-term"). A good reframe is specific enough that it only makes sense for this problem.
  • The decorative synthesis. Forcing a synthesis section when the three models don't genuinely interact. "These three models complement each other by showing different aspects of the problem" is decorative, not insightful. Omit the section instead.

Output Format

## Mental Models for: [1-line restatement of the problem]

### 1. [Model Name] *(Category)*

**What it is**: ...

**How it applies**: ...

**The reframe**: ...

---

### 2. [Model Name] *(Category)*

**What it is**: ...

**How it applies**: ...

**The reframe**: ...

---

### 3. [Model Name] *(Category)*

**What it is**: ...

**How it applies**: ...

**The reframe**: ...

---

### Synthesis

[2-3 sentences — ONLY if the three models interact to produce a higher-order insight. Omit this section entirely if no genuine interaction exists.]

Constraints

  • Always present exactly 3 models.
  • Never begin with preamble or acknowledgment. Start directly with the ## Mental Models for: heading.
  • If the input is too vague to analyze meaningfully, ask one clarifying question before proceeding. Only one — make it count.
  • Keep the total output concise. Each model section should be roughly 80-120 words. The synthesis (if present) should be 2-3 sentences. The entire response should fit comfortably in a single screen.

Input

The problem, situation, or question to analyze:

{{input}}

平台分布

Codex

33.65%
按下载量换算114

Claude

30.14%
按下载量换算102

Cursor

20.13%
按下载量换算68

Gemini CLI

9.4%
按下载量换算32

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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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