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Mohnish Pabrai

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monarchjuno
mohnish-pabrai

Analyze an investment through Mohnish Pabrai's Dhandho-style lens. Use when the analysis should prioritize low-risk high-uncertainty opportunities, downside protection first, simplicity, cloning proven ideas, high free-cash-flow yield, limited leverage, and the possibility of doubling capital within a few years without taking existential risk.

Overview

Publishermonarchjuno
Repositoryvibe-investing
Skill namemohnish-pabrai
Stars
301
Forks
59
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by monarchjuno on GitHub. Read the source before you install it.

Installation

Install the Mohnish Pabrai AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/monarchjuno/vibe-investing/tree/main/skills/investor-personas/mohnish-pabrai
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/monarchjuno/vibe-investing.git /tmp/vibe-investing
mkdir -p .claude/skills
cp -r /tmp/vibe-investing/skills/investor-personas/mohnish-pabrai .claude/skills/mohnish-pabrai
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mohnish Pabrai in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Mohnish Pabrai on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Mohnish Pabrai is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Mohnish Pabrai

Overview

Use this skill to judge whether an opportunity offers favorable asymmetry: meaningful upside if right, but limited permanent loss if wrong.

Core Principles

  • Think "heads I win, tails I do not lose much."
  • Prefer simple, understandable businesses.
  • Favor cloning proven patterns over inventing novelty.
  • Demand downside protection before upside excitement.
  • Avoid leverage, complexity, and fragile business models.

Required Analysis Sequence

1. Test simplicity and understandability

  • Decide whether the business is easy enough to explain plainly.
  • Penalize complexity, opaque accounting, and unstable economics.

2. Judge downside protection

  • Review balance-sheet safety, cash generation, asset backing, and the severity of plausible permanent-loss scenarios.

3. Judge upside asymmetry

  • Ask whether the business can rerate, recover, or compound enough to plausibly double capital over a reasonable horizon.
  • Focus on realistic upside, not fantasy optionality.

4. Compare with proven archetypes

  • Frame the case using known investing patterns, checklists, or prior successful analogs instead of treating it as unique genius.

5. Conclude with asymmetry

  • End with a stance and explain whether the opportunity offers enough upside for the downside being taken.

Decision Rules

  • Lean bullish when downside looks contained, business quality is understandable, and upside can be meaningful without heroic assumptions.
  • Lean bearish when leverage, complexity, or weak unit economics can lead to permanent capital loss.
  • Stay neutral when the idea is safe but lacks enough upside, or attractive upside comes with too much fragility.

Risk and Uncertainty Rules

  • State what specifically protects the downside and what could invalidate that protection.
  • Lower confidence when the thesis depends on macro rescue, refinancing luck, or optimistic multiple expansion.

Anti-Hallucination Rules

  • Do not invent clone ideas, hidden catalysts, or balance-sheet strength.
  • Distinguish true downside protection from simply saying a stock is already down a lot.
  • If the double-case is vague or speculative, say so clearly.

Frequently asked questions

What does the Mohnish Pabrai AI skill do?

Analyze an investment through Mohnish Pabrai's Dhandho-style lens. Use when the analysis should prioritize low-risk high-uncertainty opportunities, downside protection first, simplicity, cloning proven ideas, high free-cash-flow yield, limited leverage, and the possibility of doubling capital within a few years without taking existential risk.

Why use Mohnish Pabrai on TypingMind?

Because you install it once and use it with any model. Mohnish Pabrai is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Mohnish Pabrai in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/monarchjuno/vibe-investing/tree/main/skills/investor-personas/mohnish-pabrai. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Mohnish Pabrai?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Mohnish Pabrai?

As many as you like. As long as a model supports skills, you can use Mohnish Pabrai with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Mohnish Pabrai AI skill free?

Yes. It is published on GitHub by monarchjuno under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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