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Cathie Wood

Community
monarchjuno
cathie-wood

Analyze an investment through Cathie Wood's disruptive-innovation lens. Use when the analysis should focus on breakthrough technology, large total addressable markets, multi-year growth optionality, platform scalability, and management willingness to invest in R&D even when near-term volatility or valuation looks uncomfortable.

Overview

Publishermonarchjuno
Repositoryvibe-investing
Skill namecathie-wood
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 Cathie Wood 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/cathie-wood
  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/cathie-wood .claude/skills/cathie-wood
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cathie Wood 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 Cathie Wood 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 Cathie Wood 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.

Cathie Wood

Overview

Use this skill to evaluate whether a business is riding genuine disruptive innovation with enough market expansion and execution potential to justify a long-duration growth thesis.

Core Principles

  • Prioritize disruptive innovation over mature steady-state economics.
  • Focus on large TAM expansion and winner-take-most potential.
  • Extend the time horizon when the technology curve is still early.
  • Accept volatility when it is the price of exposure to exponential upside.
  • Favor management teams that reinvest aggressively in innovation.

Required Analysis Sequence

1. Identify the innovation

  • Define the technology, platform, or business-model innovation at the center of the thesis.
  • Judge whether it is incremental or genuinely disruptive.

2. Size the opportunity

  • Assess TAM, adoption runway, category expansion, and adjacent optionality.
  • Explain how the company could scale into that opportunity.

3. Review growth evidence

  • Examine revenue acceleration, usage trends, product expansion, R&D intensity, and operating leverage potential.
  • Focus on forward growth capacity more than current accounting neatness.

4. Judge execution and vision

  • Assess whether management has the ambition, product discipline, and capital strategy to stay ahead.

5. Frame valuation and conclusion

  • Explain whether current price still leaves room for outsized long-term returns.
  • End with a stance and a clearly stated multi-year thesis.

Decision Rules

  • Lean bullish when the innovation is real, the market opportunity is very large, adoption signals are strong, and the upside from category leadership can outweigh current volatility.
  • Lean bearish when the disruption claim is weak, the TAM is overstated, execution is slipping, or valuation already assumes near-perfect success.
  • Stay neutral when the technology is promising but evidence of scaled adoption or durable economics remains too limited.

Risk and Uncertainty Rules

  • State whether the thesis depends on regulatory change, technological breakpoints, or sustained external funding.
  • Lower confidence when current evidence is mostly narrative and pilot-stage validation.

Anti-Hallucination Rules

  • Do not invent TAM numbers, product breakthroughs, or adoption milestones.
  • Distinguish verified innovation evidence from optimistic scenario language.
  • If the thesis is mostly aspiration rather than demonstrated traction, say so directly.

Frequently asked questions

What does the Cathie Wood AI skill do?

Analyze an investment through Cathie Wood's disruptive-innovation lens. Use when the analysis should focus on breakthrough technology, large total addressable markets, multi-year growth optionality, platform scalability, and management willingness to invest in R&D even when near-term volatility or valuation looks uncomfortable.

Why use Cathie Wood on TypingMind?

Because you install it once and use it with any model. Cathie Wood 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 Cathie Wood in TypingMind?

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

Which AI models can use Cathie Wood?

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 Cathie Wood?

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

Is the Cathie Wood 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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