Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add simbajigege/book2skills --skill munger-mental-models-worldly-wisdomgit clone --depth 1 https://github.com/simbajigege/book2skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/simbajigege/book2skills/munger-mental-models-worldly-wisdom)<a href="https://agentmods.dev/skills/simbajigege/book2skills/munger-mental-models-worldly-wisdom"><img src="https://agentmods.dev/badge/skills/simbajigege/book2skills/munger-mental-models-worldly-wisdom/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/simbajigege/book2skills/munger-mental-models-worldly-wisdom"><img src="https://agentmods.dev/badge/skills/simbajigege/book2skills/munger-mental-models-worldly-wisdom.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Memory Poisoning · line 156 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00036 | $0.03625 |
| Opus 5 | $0.00018 | $0.01813 |
| Sonnet 5 | $0.00007 | $0.00725 |
| Haiku 4.5 | $0.00004 | $0.00363 |
Grade A, and why
munger-mental-models-worldly-wisdom scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 351 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
This skill turns Charlie Munger's Poor Charlie's Almanack into an executable decision tool for investment evaluation, business judgment, and psychological misjudgment diagnosis. It helps users combine multidisciplinary mental models, inversion, circle-of-competence discipline, and bias checklists into structured recommendations.
When to Use This Skill
- The user asks to analyze an investment, company, business decision, or strategy using Charlie Munger's approach.
- The user asks which mental models apply to a situation.
- The user wants to diagnose cognitive biases, market manias, frauds, scandals, or irrational behavior.
- The user wants a checklist for decision quality, preparation, patience, concentration, or opportunity cost.
- The user asks what could go wrong and how to invert a plan before acting.
HOW TO USE THIS SKILL
- Classify the user's request as investment evaluation, bias diagnosis, general decision review, or Munger-style synthesis.
- Apply only the dimensions required by the query. Do not force every checklist into a narrow question.
- Start with competence boundaries and inversion whenever the user asks for a recommendation.
- Use citations for major Munger-derived claims by loading the closest relevant quote file and citing an anchor.
- If the user needs current market or company facts, state what data is missing or browse current sources if available.
CITATION RULES
Every substantive claim based on Munger's methodology must include a citation to the original text.
Quote files (load the relevant one):
main-framework-quotes.md: broad framework statements, latticework language, and general book framing.mental-models-quotes.md: multiple mental models, multidisciplinary analysis, and business assessment.worldly-wisdom-quotes.md: worldly wisdom, circle of competence, checklists, and learning discipline.
Citation format - always use this exact structure:
"Author's exact words here."
- Poor Charlie's Almanack, topic anchor: https://github.com/simbajigege/book2skills/blob/main/skills/munger-mental-models-worldly-wisdom/quotes/FILENAME.md#ANCHOR
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 351 lines · 36 tokens per session scan A 8ac27b52ec8b
munger-mental-models-worldly-wisdom is a skill published in the GitHub repository simbajigege/book2skills (163 stars, last pushed 17d ago), licensed MIT. It adds 36 tokens to every session and 3,625 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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