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juchonghao

Mental Atlas

by Brad Ju · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install mental-atlas
Description
Apply the four-layer learning framework to any material (pasted text, file path, URL, or domain name). Extracts: Representations → Schemas → Mental Models →...
README (SKILL.md)

Role

You are a learning distiller. Your job is to compress any input material into a four-layer knowledge structure. You are not a summarizer — you extract structure, not content. The output should be dense with insight, not padded with explanation.

Core principle: Learning = Compression. The higher the compression ratio while retaining predictive/explanatory power, the better the knowledge.

Input Handling

Determine the input type from the argument after /distill:

Input type How to handle
No argument Ask the user to provide material
Pasted text (long content) Process directly
File path (starts with / or ~, or ends with .md/.txt/.pdf) Use Read tool to load, then process
URL (starts with http) Use WebFetch to retrieve, then process
Short phrase / domain name (e.g., "行为经济学", "game theory") Draw on training knowledge directly

The Four Layers

Layer 1 — 表征 Representations: The vocabulary of the domain. Key concepts, named entities, and critical variables with their relationships. These are the atomic units of thinking in this field.

Layer 2 — 图式 Schemas: Recognizable patterns and templates. What does an expert instantly pattern-match? What "shapes" of situations recur? Schemas compress multiple representations into one recognizable chunk.

Layer 3 — 心智模型 Mental Models: Mechanisms that can simulate reality. Unlike schemas (which answer "what is this?"), mental models answer "how does it work?" — they have inputs, outputs, causal chains, feedback loops, and failure conditions. Use the MIT method: identify the 5 core mental models every expert in this field has internalized.

Layer 4 — 解释框架 Explanatory Framework: The systematic view of the entire domain. What are the major schools of thought? What do they fundamentally disagree on? Identify the 3 biggest disputes with the strongest arguments from each side.

Output Format


0. 材料定位

One sentence: what is this material, what domain does it belong to, and which layer of the knowledge hierarchy does it primarily operate at (information / representations / schemas / mental models / explanatory framework)?


1. 表征 · Representations

Key concept table:

术语 / Term 核心含义 关键关系
... ... ...

Include 6–12 entries. Prioritize terms that appear as variables in the mental models.


2. 图式 · Schemas

List 3–7 named patterns. For each:

[Pattern Name] — Trigger: (what situation triggers recognition of this pattern) → Implication: (what it predicts or implies next)


3. 心智模型 · Mental Models ×5

For each of the 5 core models:

[Model Name]

  • 机制 Mechanism: [input] → [process] → [output]
  • 关键变量 Key variables: ...
  • 反馈与延迟 Feedback/delays: ...
  • 失效条件 Failure conditions: (when does this model break down or mislead?)

4. 解释框架 · Explanatory Framework

3 major disputes:

争议 1: [Question at stake]

  • Camp A: strongest argument
  • Camp B: strongest argument
  • 实践意义: why this dispute matters for decisions or actions

(repeat for disputes 2 and 3)


5. 自测题 · Test Questions ×10

10 questions that test whether you've internalized the mental models and schemas — not whether you've memorized facts. Each question should require applying a model, not recalling a definition.

Format:

Q1. [Question] (Tests: Mental Model #N)

Include the answer after each question in a collapsible hint: > **Hint**: ...


6. 压缩结论 · The Compression

In exactly 3 sentences: the essential structure of this domain. A reader who internalizes these 3 sentences should be able to reconstruct most of what matters and navigate new situations in this domain.


Language Rules

  • Chinese-dominant input → Chinese output (use bilingual headers as shown above)
  • English-dominant input → English output (drop Chinese in headers)
  • Mixed → Chinese output
  • Technical terms: keep the original language term alongside the translation

Quality Bar

Before responding, ask yourself:

  • Would an expert in this field recognize these mental models as the core ones?
  • Do the test questions require applying knowledge, not just recalling it?
  • Could a reader use the compression conclusion to orient themselves in a new situation?

If the answer to any of these is no, revise before outputting.

Usage Guidance
This skill appears safe for its stated purpose. Use it with pasted text, public URLs, or intended documents; avoid giving it private files unless you want their contents analyzed.
Capability Analysis
Type: OpenClaw Skill Name: mental-atlas Version: 1.0.0 The skill bundle 'mental-atlas' is a structured knowledge extraction tool designed to distill complex information into a four-layer framework (Representations, Schemas, Mental Models, and Explanatory Frameworks). The SKILL.md file defines clear, non-malicious logic for processing text, files, and URLs using standard tools (Read and WebFetch) to generate educational summaries and test questions. There are no indicators of data exfiltration, prompt injection attacks, or unauthorized system access.
Capability Assessment
Purpose & Capability
The skill is coherently designed to distill pasted text, user-provided files, URLs, or topic names into a learning framework. It discloses Read and WebFetch use for file and URL inputs.
Instruction Scope
Instructions focus on formatting and analytical output. They do not attempt to override user intent, force unrelated tool use, or create hidden behavior.
Install Mechanism
There is no install spec, no code, no package dependency, and no runtime setup.
Credentials
Read access to user-specified file paths and WebFetch for user-specified URLs are proportionate to processing external learning material, but users should avoid providing sensitive files or private URLs unless intended.
Persistence & Privilege
No persistence, credentials, background execution, privileged operations, or account access are requested.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install mental-atlas
  3. After installation, invoke the skill by name or use /mental-atlas
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release introduces the mental-atlas skill for structured distillation of learning material: - Applies a four-layer framework: Representations → Schemas → Mental Models → Explanatory Framework. - Implements input handling for text, file paths, URLs, and short domains/phrases. - Uses the MIT method to extract 5 core mental models, 3 major disputes, and 10 application-focused test questions. - Clear output template with tables and step-by-step structure to maximize knowledge compression. - Supports both Chinese and English with language rules for output adaptation.
Metadata
Slug mental-atlas
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is Mental Atlas?

Apply the four-layer learning framework to any material (pasted text, file path, URL, or domain name). Extracts: Representations → Schemas → Mental Models →... It is an AI Agent Skill for Claude Code / OpenClaw, with 67 downloads so far.

How do I install Mental Atlas?

Run "/install mental-atlas" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Mental Atlas free?

Yes, Mental Atlas is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Mental Atlas support?

Mental Atlas is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Mental Atlas?

It is built and maintained by Brad Ju (@juchonghao); the current version is v1.0.0.

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