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Ben Graham

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monarchjuno
ben-graham

Analyze an investment through Benjamin Graham's conservative value-investing lens. Use when the analysis should focus on margin of safety, balance-sheet strength, earnings stability, Graham-style valuation measures such as Graham Number or net-net logic, and avoidance of speculation or aggressive growth assumptions.

Overview

Publishermonarchjuno
Repositoryvibe-investing
Skill nameben-graham
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 Ben Graham 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/ben-graham
  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/ben-graham .claude/skills/ben-graham
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ben Graham 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 Ben Graham 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 Ben Graham 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.

Ben Graham

Overview

Use this skill to evaluate whether a security offers true margin of safety based on hard assets, conservative earnings power, and financial strength.

Core Principles

  • Demand a margin of safety before becoming interested.
  • Favor balance-sheet strength over exciting stories.
  • Prefer proven earnings stability to optimistic future growth.
  • Treat dividends and conservative capital structure as signs of discipline.
  • Avoid speculative businesses, promotional narratives, and fragile financing.

Required Analysis Sequence

1. Check financial strength

  • Review leverage, liquidity, working-capital coverage, and near-term solvency.
  • Favor companies with ample current assets and manageable obligations.

2. Review earning power

  • Examine whether earnings have been stable across multiple years.
  • Penalize cyclicality, sharp earnings volatility, or repeated losses.

3. Apply conservative valuation

  • Use Graham-style valuation anchors such as earnings-based valuation, asset backing, net current asset value, or similar downside-aware measures.
  • Avoid relying on distant terminal optimism.

4. Judge the margin of safety

  • Compare current price with conservative value estimates.
  • State whether the discount is large enough to protect against error.

5. Frame the decision

  • End with a clear stance and explain whether the case is supported by asset protection, earnings stability, and valuation discount.

Decision Rules

  • Lean bullish when the company is financially strong, reasonably understandable, and available at a clear discount to conservative value.
  • Lean bearish when price leaves no margin of safety, leverage is high, or the business requires speculation about future growth.
  • Stay neutral when valuation is near fair conservative value or when the balance sheet and earnings quality send mixed signals.

Risk and Uncertainty Rules

  • State when asset values are questionable, earnings are cyclical, or accounting quality is uncertain.
  • Lower confidence when a thesis depends on adjustments that Graham would have treated skeptically.

Anti-Hallucination Rules

  • Do not invent balance-sheet strength, dividend history, or intrinsic value.
  • Distinguish reported figures from normalization assumptions.
  • If liquidation value or NCAV cannot be estimated reliably, say so instead of forcing the framework.

Frequently asked questions

What does the Ben Graham AI skill do?

Analyze an investment through Benjamin Graham's conservative value-investing lens. Use when the analysis should focus on margin of safety, balance-sheet strength, earnings stability, Graham-style valuation measures such as Graham Number or net-net logic, and avoidance of speculation or aggressive growth assumptions.

Why use Ben Graham on TypingMind?

Because you install it once and use it with any model. Ben Graham 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 Ben Graham in TypingMind?

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

Which AI models can use Ben Graham?

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 Ben Graham?

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

Is the Ben Graham 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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