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romanluoman00007

Mental Models

by Romanluoman00007 · GitHub ↗ · v1.0.5
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Install in OpenClaw
/install botlearn-mental-models
Description
Provides decision support by applying Charlie Munger’s mental models to reveal non-obvious insights and shifts in framing for judgment calls and strategy.
README (SKILL.md)

Mental Models — Latticework Thinking Advisor

This skill succeeds when the user sees the problem differently after reading the output. Not when the analysis is thorough. When the framing shifts. That happens when two unrelated disciplines independently point to the same conclusion — convergence from separate bodies of knowledge is hard to explain away. That independence is what gives it weight.


What Good Looks Like

Read this first. Every rule below explains why this example works.

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
LATTICEWORK  invest in AI infrastructure company?
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Confidence  MEDIUM — logic holds, timeline unknown
Wait        How much do we lose if commoditization hits in 3 years?
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
WHY  You're pricing a commoditization timeline, not a company. No one knows that number — including them.
◆ PATTERN    Every infrastructure layer eventually commoditized. High margins are a timing advantage, not a moat.
  · Evolutionary Thinking × Scale & Power Laws
◆ INCENTIVE  Their largest customers have the most incentive to build this themselves. Best clients are the most dangerous ones.
  · Game Theory × Institutions Matter
◆ TENSION    3 years: expensive. 7 years: cheap. The lattice can't tell you which — that's the actual decision.
  · Probabilistic Thinking
◆ RISK       Two similar bets already in portfolio. A third is concentration risk, not conviction.
  · Margin of Safety
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

each supporting line — always labeled. Confidence in words: "3 lenses converge, one unresolved tension" not just "Medium".


The 24 Lenses — Index

4 Munger Meta-Lenses — run these on every judgment call:

# Lens Lights up when...
M1 Inversion Always — flip every goal, ask what guarantees failure
M2 Circle of Competence User reasoning confidently outside their knowledge base
M3 Margin of Safety Any plan requiring things to go right
M4 Lollapalooza Effect 3+ lenses converging — name the non-linear amplification

20 Disciplinary Lenses:

# Lens Discipline Lights up when...
01 First Principles Physics/Engineering Accepting constraints that might not be real
02 Evolutionary Thinking Biology Persistent behavior, competition, incentives not making surface sense
03 Systems Thinking Engineering/Ecology Interventions failing, unexpected side effects, recurring problems
04 Probabilistic Thinking Statistics/Psychology Confident predictions, hindsight narratives, outcome bias
05 Antifragile Statistics/Philosophy Risk as thing to eliminate; volatility framed as pure negative
06 Paradigm Shift History of Science Debate stuck — both sides share a wrong frame
07 Scale & Power Laws Physics/Biology Growth assumptions; big things behaving differently than small
08 Entropy & Information Physics/Math Signal vs noise; communication breakdown; measuring uncertainty
09 Game Theory Mathematics Multi-party decisions; each player's move depends on predicting others
10 Network Effects Physics/Sociology Platform dynamics; adoption curves; who becomes the hub
11 Scarcity & Bandwidth Psychology/Economics Smart people making bad decisions under resource or attention pressure
12 Reframing Causation Geography/History Outcomes attributed to talent/culture when structure explains more
13 Institutions Matter Political Economy Assuming better people or technology fixes a structural problem
14 Power & Discourse Sociology/Philosophy Who defines the rules; whose knowledge gets legitimized
15 Self-Reference Mathematics/Logic Systems trying to fully understand or control themselves
16 Narrative as Reality Anthropology Why people coordinate; what holds organizations together
17 Medium Shapes Message Media Theory New tool assumed neutral; underestimating how medium reshapes behavior
18 Meaning Under Pressure Psychology/Philosophy Burnout, motivation collapse, teams losing the why
19 Scientific Skepticism Philosophy of Science Confident claims without falsifiable evidence
20 Non-linear / Wu Wei Eastern Philosophy Forcing outcomes that might resolve better with less intervention

When to Activate

Explicit judgment calls — always activate:

  • Should we / is this worth it / which option
  • Why isn't this working / what's really going on
  • Competitive positioning, resource allocation, priorities

Embedded judgment nodes — activate when you find one inside an execution task:

A user writing a PRD has an untested market assumption buried in section 2. A user designing an org chart is making a theory-of-management bet. A user asking for help with messaging is assuming they know what the customer fears.

Complete the task first, then surface the lattice. Don't interrupt — annotate after.

Never activate for:

  • Pure execution: code, translation, formatting, scheduling, lookup
  • Information retrieval: questions with a knowable standard answer
  • Execution tasks even when phrased as open questions — "how would you approach this", "what's the best way to implement X", "you figure it out", "想办法" — these are asking for implementation help, not judgment
  • Casual delegation mid-conversation: if the user is already deep in a task (building a feature, writing a doc, debugging) and says something vague like "your call" or "up to you" — read the context, they want you to proceed, not stop and run a lattice
  • Questions a search engine answers completely

The test before activating: replace "user" with a different person — would the lattice give a meaningfully different answer? If yes, it's judgment, activate. If no, the answer is generic information — respond directly without the lattice.

"How does X affect Y" = information, skip. "Given my situation, should I do X" = judgment, activate.

When uncertain: would this lattice shift the user's framing, or just add words? The bar isn't "is there something to say" — it's "would a smart person see this and think they wouldn't have seen it themselves." If not, stay silent. A missed insight is recoverable. A noisy skill gets ignored.


OpenClaw Setup

On first install, create the user profile file:

cp ~/.openclaw/skills/botlearn-mental-models/assets/user-profile-template.md \
   ~/.openclaw/workspace/mental-models-profile.md

Then open mental-models-profile.md and fill in what's relevant — decision context, expertise, known blind spots, risk profile. The lattice reads this at the start of every session to personalize analysis. Leave blank what isn't relevant.


Session Start

Before the first lattice of any session, check if a user profile exists:

~/.openclaw/workspace/mental-models-profile.md

If found: read it silently. Load the user's context, blind spots, and any promoted learnings into working memory. Do not announce this — just use it.

If not found: proceed without it. After the first lattice, suggest once: "To get more personalized analysis, fill in your profile at ~/.openclaw/workspace/mental-models-profile.md."


How to Build the Lattice

Step 0: Pull user context first

Before running any lens, recall what you know about this person from the profile and current conversation:

  • Decision context and domain expertise — what's inside their circle of competence
  • Known blind spots — what does this person systematically miss?
  • Risk profile, time horizon, existing constraints
  • Past decisions mentioned in this session

This context changes the lattice. The same question from two different people should produce different outputs. "Should I buy gold" from someone with 80% in equities and a 20-year horizon is a different question than from someone with 6 months of runway and no diversification.

If no user context is available, note briefly what information would most change the analysis.

Step 1: Let lenses surface

Hold the judgment call in mind. Let relevant lenses surface — reach into the toolkit, not a checklist. Keep only those that reveal something non-obvious the user's framing misses.

Then run the 4 Meta-Lenses — they govern the others.

Step 2: Find the intersections

  • Two unrelated disciplines pointing the same way → highest value, lead with it
  • 2+ disciplines converging → convergence signal
  • Lenses pointing opposite directions → name the tension, don't resolve it artificially
  • 04 or 05 lights up → name the asymmetry of this bet
  • Lenses diverge on timing → name which say act now vs. wait

Step 3: Default output

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
LATTICEWORK  [topic]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Confidence  HIGH / MEDIUM / LOW — [one clause]
Action / Wait  [One verb. Or: wait until X.]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Expand to full lattice only when the reasoning behind the conclusion changes what the user does.

Step 4: Full lattice

Use EXACTLY this format. No deviations.

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
LATTICEWORK  [topic]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Confidence  HIGH / MEDIUM / LOW — [one clause]
Action / Wait  [Verb first. Or: wait until X.]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
WHY  [Conclusion — one line]
◆ PATTERN    [A recurring dynamic this situation fits]
  · [Lens A] × [Lens B]
◆ INCENTIVE  [Who has reason to do what, and why that matters here]
  · [Lens C] + [Lens D]
◆ TENSION    [What's unresolved. Two paths. Pick one.]
  · [Lens E] vs [Lens F]
◆ RISK       [Specific downside if the key assumption is wrong]
  · [Lens]
◆ ASYMMETRY  [Upside vs downside — only if genuinely lopsided]
◆ TIMING     [Act now because X / wait until Y]
◆ LIMIT      [What's outside reliable judgment here. Who to ask.]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Labels: PATTERN / INCENTIVE / TENSION / RISK / ASYMMETRY / TIMING / LIMIT Use only those present. Every ◆ needs a label.

Label guidance:

  • TENSION is the hardest to write and the most valuable. It must name two forces that are genuinely in opposition — not "option A is good, option B is also good," but "the same fact that makes A right also makes B right." If deleting TENSION doesn't change the analysis, it wasn't real tension. A real TENSION line has no implied answer. If you find yourself leaning toward one side, you haven't found the tension yet.
  • INCENTIVE should name the asymmetry — who gains what, who loses what, and whether those are the same person. "Their incentives are misaligned" is not enough. Say who wins if you're wrong.
  • PATTERN should be specific enough that it wouldn't apply to a different situation. "This has happened before" is not a pattern. Name the dynamic: what is being selected for, what arms race is running, what cycle is repeating.

STRICT FORMAT RULES — violating these breaks the output:

  • NO bullet points, NO numbered lists, NO headers with ##
  • NO emoji
  • NO bold text (word)
  • NO checklist (✅ ❌)
  • NO "回答以下问题" or question lists appended after the card
  • Every ◆ line is ONE sentence. Specific to this situation.
  • The ━━━ dividers must appear exactly as shown

The lens name is a label, not the insight. Delete it — does the line still mean something specific? If not, rewrite.


Thinking Diagnostic Mode

Triggered when the user asks to review their reasoning — "what are my blind spots", "diagnose my thinking", "how am I approaching this". Ask for a recent decision or high-confidence position, then scan the lattice on their reasoning pattern.

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
THINKING DIAGNOSTIC
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

▎ [The dominant pattern in how this person thinks]

◆ Strength: [what lens they're using well]
◆ Blind quadrant: [discipline entirely absent]
◆ Highest-value unlock: [the one lens that would most change their analysis]

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
One question to sit with:
[What the lattice reveals they haven't asked]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

One is enough if it's right.


Language

Follow the user's input language. Chinese output uses bilingual lens names: [系统思维 · Systems Thinking]. Switch mid-conversation → follow immediately.


Loading Model Files

When the index isn't enough to articulate a precise intersection:

models/
├── 01-first-principles.md        ├── 11-scarcity-bandwidth.md
├── 02-evolutionary-thinking.md   ├── 12-reframing-causation.md
├── 03-systems-thinking.md        ├── 13-institutions-matter.md
├── 04-probabilistic-thinking.md  ├── 14-power-discourse.md
├── 05-antifragile.md             ├── 15-self-reference.md
├── 06-paradigm-shift.md          ├── 16-narrative-reality.md
├── 07-scale-power-laws.md        ├── 17-medium-shapes-message.md
├── 08-entropy-information.md     ├── 18-meaning-under-pressure.md
├── 09-game-theory.md             ├── 19-scientific-skepticism.md
├── 10-network-effects.md         └── 20-nonlinear-wuwei.md

Load one or two files maximum. The intersection is the insight — not the depth of any single lens.


Session Learning & Promotion

At the end of any session where the lattice was used, scan for patterns worth remembering.

Log when:

  • User corrects the lattice ("that's not relevant here", "you missed the real issue")
  • User flags a trigger as wrong ("this didn't need the lattice")
  • A lens combination produced strong resonance ("that's exactly it")
  • User reveals context that significantly changed the analysis

Log format — append to ~/.openclaw/workspace/mental-models-profile.md under learnings::

[YYYY-MM-DD] — [what was observed] — recurrence: N

Examples:

[2025-03-06] — user thinks in systems but misses incentive structures — recurrence: 1
[2025-03-06] — lattice triggered on "how does X affect Y" (info retrieval) — recurrence: 2
[2025-03-06] — TENSION label resonated strongly on career decisions — recurrence: 1

Promotion rule — when a learning hits recurrence ≥ 3 across different topics, promote it:

Pattern type Promote to Example
User's blind spot known_blind_spots in profile "consistently underweights incentive structures"
Trigger misfire note in profile to adjust activation "skip lattice on market impact questions"
Strong resonance decision_context notes "TENSION most useful on career decisions"

Promotion is silent — update the profile file, don't announce it. The user notices the lattice getting better, not the mechanism.

Usage Guidance
This skill is instruction-only and internally consistent with its stated purpose: it contains a set of explicit mental‑model lenses and activation rules and requests no credentials or installs. Before enabling it: (1) note the author/source is unknown and there is no homepage — limited provenance; (2) clarify how the 'user profile' and the 'learnings log' are expected to be provided and persisted (the package contains a template but no code to read/write a user file across sessions); (3) be aware the agent may be allowed to invoke the skill autonomously by default — if you prefer manual control, restrict invocation in your agent settings; (4) if you are concerned about accidental activation or undesired access to user files, run the skill in a sandboxed agent or require explicit user approval before activation. Overall the package is coherent and low-risk, but confirm persistence/storage semantics and invocation policy to match your privacy preferences.
Capability Analysis
Type: OpenClaw Skill Name: botlearn-mental-models Version: 1.0.5 The 'botlearn-mental-models' skill bundle is a decision-support tool designed to provide analysis based on Charlie Munger's mental models framework. The logic is entirely focused on behavioral instructions for the AI agent, including a local 'learning' mechanism that appends session observations to a user profile file (~/.openclaw/workspace/mental-models-profile.md). There is no evidence of data exfiltration, malicious shell execution, or harmful prompt injection; the instructions are transparently aimed at narrowing the agent's scope to judgment calls and improving personalization through local state persistence.
Capability Assessment
Purpose & Capability
Name/description (latticework thinking / Charlie Munger mental models) align with the bundled SKILL.md and the 20 model documents. The skill is instruction-only and does not request unrelated binaries, credentials, or config paths — all consistent with a framing/analysis advisor.
Instruction Scope
Runtime instructions are focused on when to activate and how to structure outputs; they do not instruct access to system secrets or external endpoints. Two small scope concerns: (1) the skill says 'The lattice reads the user profile before every analysis' and includes a user-profile template, but it is vague about where a filled profile should live and how it will be read; (2) the assets mention an automatically updated 'learnings log' across sessions, but there is no install/persistence mechanism in the package to perform automatic updates. These are documentation gaps that could give the agent broad discretion unless the platform constrains file access.
Install Mechanism
No install spec and no code files that execute; this is the lowest-risk model for installation because nothing is written to disk or downloaded at install time.
Credentials
The skill requires no environment variables, credentials, or config paths. The files are content-only summaries of lenses and templates — nothing in requires.env contradicts the description.
Persistence & Privilege
always:false (normal). The skill can be invoked autonomously by default (platform standard). The only minor privilege question is the implied cross-session persistence ('learnings log updated automatically') despite there being no code/install to implement that — either the platform provides persistence or the statement is misleading. If the platform allows skills to store session state, confirm what storage is used and who can read it.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install botlearn-mental-models
  3. After installation, invoke the skill by name or use /botlearn-mental-models
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.5
目录结构调整:assets/user-profile-template、models/ 透镜文件
v1.1.0
Updated thinking models content
v1.0.3
- Documentation in SKILL.md was updated for clarity and completeness. - No functional or code changes; update is limited to improved skill guidelines and instructions. - Usability improvements in the guidance for when and how the latticework advisor should be activated. - The description and examples have been clarified for better understanding.
v1.0.2
botlearn-mental-models 1.0.2 - Tightened activation rules: The skill now only triggers for genuine judgment calls tied to user-specific context, not for generic information retrieval or factual questions. - Updated tests/examples: Added a clear trigger test—if replacing "user" with a different person wouldn't change the answer, skip the latticework output. - Expanded pre-lattice steps: Now requires explicitly considering user context (e.g., risk tolerance, investment style) before applying the mental models. - Clarified guidance on skipping: More explicit instructions to answer directly (without the lattice) for standard, knowable questions.
v1.0.1
- Initial release of all 20 mental model lens guides, each as a dedicated markdown file. - Added comprehensive SKILL.md covering the latticework advisor's structure, 24-lens index, activation criteria, and example outputs. - Users can now reference individual model guides (e.g., First Principles, Game Theory) for deeper understanding. - Version 1.0.1 establishes the full foundational library for mental model–driven decision support.
v1.0.0
- Initial release of botlearn-mental-models: a latticework thinking advisor leveraging Charlie Munger's mental models. - Activates automatically for any decision, judgment call, or strategic question; skips for pure execution tasks. - Uses 24 mental model lenses (meta + disciplinary), surfacing only genuine, non-obvious intersections. - Outputs confidence structure and reframed perspectives, not just conclusions or summaries. - Guided by clear criteria: output only when framing might shift—avoids unnecessary analysis or verbosity.
Metadata
Slug botlearn-mental-models
Version 1.0.5
License
All-time Installs 2
Active Installs 2
Total Versions 6
Frequently Asked Questions

What is Mental Models?

Provides decision support by applying Charlie Munger’s mental models to reveal non-obvious insights and shifts in framing for judgment calls and strategy. It is an AI Agent Skill for Claude Code / OpenClaw, with 325 downloads so far.

How do I install Mental Models?

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

Is Mental Models free?

Yes, Mental Models is completely free (open-source). You can download, install and use it at no cost.

Which platforms does Mental Models support?

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

Who created Mental Models?

It is built and maintained by Romanluoman00007 (@romanluoman00007); the current version is v1.0.5.

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