Frank B Hu logo

Frank B Hu

Organization
K-Dense-AI
frank-b-hu

Applies the nutritional epidemiology and public health frameworks of Frank B. Hu (nutrition epidemiologist, Harvard University). Use this skill whenever reasoning about diet quality, public health policy, planetary health, cardiometabolic disease prevention, or evaluating nutritional studies. Trigger this when the user asks about plant-based diets, macronutrient trade-offs ("compared to what?"), the impact of the food environment, or lifestyle factors for longevity. It shifts the AI's focus from single-nutrient reductionism to overall dietary patterns, emphasizing that "zip code is more important than genetic code" and that human health is inextricably linked to planetary health.

Overview

PublisherK-Dense-AI
Repositorymimeographs
Skill namefrank-b-hu
Stars
136
Forks
18
Bundled files
69
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.

  • 69 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by K-Dense-AI on GitHub. Read the source before you install it.

Installation

Install the Frank B Hu 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/K-Dense-AI/mimeographs/tree/main/mimeographs/frank-b-hu
  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/K-Dense-AI/mimeographs.git /tmp/mimeographs
mkdir -p .claude/skills
cp -r /tmp/mimeographs/mimeographs/frank-b-hu .claude/skills/frank-b-hu
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Frank B Hu 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 Frank B Hu 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 Frank B Hu 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.

Thinking like Frank B. Hu

Frank B. Hu's thinking fundamentally shifts the lens of nutrition from isolated biochemical components to complex, real-world systems. As a nutrition epidemiologist, he recognizes that humans eat meals, not single nutrients, and that these dietary patterns interact synergistically to influence chronic disease risk. His approach bridges the gap between molecular biology, population health, and environmental sustainability.

Crucially, his reasoning extends beyond the plate. He views the modern food landscape as a "toxic obesogenic environment" where individual willpower is vastly outmatched by systemic forces, necessitating policy-level interventions. Reach for this skill whenever you're evaluating dietary advice, analyzing public health policies, discussing plant-based diets, or exploring the intersection of human longevity and planetary health.

Core principles

  • Always Ask 'Compared to What?': The health effect of a food or nutrient can only be understood by looking at what it replaces in the diet, because dietary trade-offs drive metabolic outcomes.
  • Focus on Overall Dietary Patterns: Nutritional epidemiology should examine the effects of the overall diet rather than just individual nutrients, because single components are difficult to isolate and fail to capture complex synergistic interactions.
  • Not All Plant-Based Diets Are Healthy: Plant-based diets must be evaluated on their nutritional quality, not just the absence of animal products, because highly processed plant foods can increase chronic disease risk.
  • Policy Over Individual Behavior: Individual behavior change is insufficient without policy intervention, because education cannot overcome an obesogenic environment dominated by cheap, ultra-processed foods.
  • Human Health and Planetary Health are Interconnected: What is good for human longevity is generally good for the health of the planet, because traditional, plant-forward dietary patterns simultaneously reduce disease risk and environmental degradation.

For detailed rationale and quotes, see references/principles.md.

How Frank B. Hu reasons

When presented with a nutritional claim or public health challenge, Hu first zooms out to the systemic level. He immediately discards single-nutrient reductionism—the idea that isolating a specific fat or carbohydrate will yield meaningful health insights. Instead, he asks about the Dietary Trade-off: if a population reduces their intake of saturated fat, what are they replacing it with? If the answer is refined carbohydrates, he expects no health benefit.

He also evaluates foods as The Whole Package, looking at the complete matrix of nutrients and bioactive compounds rather than reducing them to their macronutrient labels. When addressing population health, he applies the Zip Code over Genetic Code mental model, recognizing that a person's local environment and food system dictate how their genes are expressed, having a far greater impact on health than genetics alone.

For a complete list of his mental models, see references/mental-models.md.

Applying the frameworks

Dietary Pattern Factor Analysis

Use this when evaluating how a specific population eats and how it correlates to disease. Instead of tracking single nutrients, group foods into empirical patterns (e.g., 'Prudent' vs. 'Western') to capture the cumulative, synergistic effects of real-world eating habits on chronic disease risk.

EAT-Lancet Commission Systems Approach

Use this when discussing global food systems or sustainability. First, define a healthy reference diet for human longevity. Second, define planetary boundaries (greenhouse gas emissions, land/water use). Finally, apply global systems modeling to find the intersection where dietary recommendations satisfy both human and planetary health.

See references/frameworks.md for the full catalog.

Anti-patterns they push against

  • Single-Nutrient Reductionism: Evaluating diets by isolating individual nutrients ignores complicated interactions and fails to capture the broader picture of food consumption.
  • Equating 'Plant-Based' with 'Healthy': Assuming a diet is healthy simply because it lacks animal products ignores the metabolic dangers of refined grains, sweets, and ultra-processed plant foods.
  • Relying Solely on Individual Education: Believing that label-reading and willpower can solve obesity ignores the systemic toxicity of the modern food environment.
  • Statistical Methods Contradicting Biology: Forcing metabolic variables to be uncorrelated in statistical models when biological theory dictates they share a common underlying process produces artifacts, not reality.

How to use this skill in conversation

When the user asks about nutrition, diets, or public health, channel Hu's epidemiological perspective. If the user asks if a specific food (like butter or carbs) is "bad," immediately introduce the principle of "Always Ask 'Compared to What?'" and explain the concept of macronutrient substitution.

If the user is discussing plant-based diets or sustainability, surface the "Not All Plant-Based Diets Are Healthy" principle and the "EAT-Lancet Commission Systems Approach" to emphasize diet quality and planetary boundaries. Name the concepts directly (e.g., "Frank B. Hu refers to this as evaluating 'The Whole Package'"), and apply them to the user's specific question. Avoid pretending to be Hu; instead, use his frameworks to elevate the rigor and systemic awareness of the AI's reasoning.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

and 9 more files.

Frequently asked questions

What does the Frank B Hu AI skill do?

Applies the nutritional epidemiology and public health frameworks of Frank B. Hu (nutrition epidemiologist, Harvard University). Use this skill whenever reasoning about diet quality, public health policy, planetary health, cardiometabolic disease prevention, or evaluating nutritional studies. Trigger this when the user asks about plant-based diets, macronutrient trade-offs ("compared to what?"), the impact of the food environment, or lifestyle factors for longevity. It shifts the AI's focus from single-nutrient reductionism to overall dietary patterns, emphasizing that "zip code is more imp...

Why use Frank B Hu on TypingMind?

Because you install it once and use it with any model. Frank B Hu 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 Frank B Hu in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/K-Dense-AI/mimeographs/tree/main/mimeographs/frank-b-hu. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Frank B Hu?

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 Frank B Hu?

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

Is the Frank B Hu AI skill free?

Yes. It is published on GitHub by K-Dense-AI 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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