Frontier models are smart. They are not anyone in particular.

Ask Claude, GPT, or Gemini how to price a SaaS product and you get a coherent, reasonable, completely forgettable answer. Ask Warren Buffett the same question and you get an answer about pricing power, moats, owner earnings, and a blunt warning that most SaaS businesses do not deserve the multiples they are trading at. Two very different conversations. One of them changes what you actually do on Monday.

The gap between those two conversations is the thing we have spent the last several months trying to close.

Today we are open-sourcing two projects that attempt to close it:

mimeo: a tool that takes a name, reads the internet on your behalf, and distills how that person thinks into a SKILL.md or AGENTS.md file your agent can load.
mimeographs: a catalog of 60 ready-to-use experts produced by mimeo, free to drop into any agent that speaks the open Agent Skills standard (Claude Code, Cursor, Codex, Gemini CLI, Copilot CLI, and the rest).

Both are MIT-licensed. Install the catalog in a single line. Clone a new expert with one command. The rest of this post is why we think this is worth your attention.

Intelligence is not the same as a way of thinking

Every field has people who have spent decades publicly working out how to think about it.

Feynman on physics and first-principles reasoning. Darwin on slow, obsessive observation. Turing on what it means to compute something. Walter Willett on what separates a real nutritional signal from noise. Steve Jobs on craftsmanship and what you cut. Iris Murdoch on why you cannot reason clearly about ethics without first seeing the other person clearly. Wittgenstein on why your confusion is almost always about language, not the world.

Their lectures, essays, interviews, letters, and papers contain genuinely useful mental models: the kind of durable frameworks that outlast any specific technology cycle. The frameworks are scattered across thousands of pages and hundreds of hours of content that no one has time to absorb, let alone apply consistently.

A frontier model has read most of it. And somehow, when you talk to one, you do not get Feynman or Darwin or Willett. You get a kind of agreeable, middle-of-the-road synthesis that refuses to take a position, reaches for the safest answer in the training distribution, and has no strong opinions about what is actually interesting. That is not a bug in the model. It is what "average of the internet" looks like in conversation.

For a huge range of work (code review, research design, hiring calls, product trade-offs, investment memos, ethics questions), average is not what you want. You want a specific person's stance, with their frameworks, their anti-patterns, and their blind spots clearly labeled. You want a second brain in the room that is not yours.

Why `SKILL.md` and `AGENTS.md` are the right place to put that

Somewhere between "raw model" and "finished product," there is a surprisingly small text file that controls an enormous amount of an agent's behavior.

In the Agent Skills world, that file is SKILL.md: a piece of Markdown with a few lines of YAML frontmatter that the agent loads when its description matches the task. In the AGENTS.md world, supported by Cursor, Codex, Gemini CLI, Copilot CLI, Aider, and a growing list of others, it is a plain Markdown file the agent reads every time it works in a given directory. No frontmatter. No triggering logic. Always on.

Both formats are doing the same underlying job: shaping the default stance an intelligent-but-generic model takes when it sits down to do your work.

A good SKILL.md or AGENTS.md is a lever. It changes:

What the agent notices first. Jobs notices the onboarding flow before the feature list. Buffett notices the balance sheet before the growth rate. Wittgenstein notices that your question contains three different meanings of the word "fair" and refuses to move until you pick one.
Which trade-offs it weighs. A Langer-flavored agent reasoning about a drug delivery vehicle will weigh biocompatibility and manufacturability in ways a generic agent will not.
Which patterns it reaches for by default. An epidemiologist-flavored agent reaches for a nested case-control design; an AI-safety-flavored agent reaches for red-teaming first; a Walt Disney-flavored agent reaches for "what does the guest feel the moment they walk in?"
Which anti-patterns it pushes back on. This is the part that is hardest to get from a generic model. You want an agent that will actually tell you "no, this is the committee-driven thinking Jobs specifically warned against" instead of politely shipping whatever you asked for.

The problem is that writing one of these files by hand (reading everything, synthesizing frameworks, surfacing the non-obvious moves, finding the right quotes) is itself a multi-week research project. Most people never do it. So most agents run on a default personality that belongs to nobody.

That is the gap mimeo fills.

mimeo: a research pipeline for one person

mim·e·o: to reproduce, to copy, to imitate.

You give mimeo a name. It gives you back a production-ready SKILL.md or AGENTS.md that encodes how that person actually reasons about problems.

Under the hood, it is not a single prompt. It is a small research pipeline:

Disambiguation. "John Smith" is not one person. mimeo runs a quick Parallel Search + LLM classification pass before burning any serious budget, so you do not silently end up with a skill that is one-third economist, one-third basketball coach, one-third novelist. Ambiguous names prompt you to pick the right one; scripted runs can pin it with --disambiguator "head coach, Michigan State".
Discovery. mimeo searches across eight intent buckets (essays, talks and lectures, interviews, podcasts, frameworks, books, papers, and letters), so it works equally well for a modern operator whose legacy lives in YouTube talks and a historical scientist whose legacy lives in archival correspondence.
Fetch. Full web extract, YouTube captions via youtube-transcript-api, and optional local Whisper transcription for podcasts.
Distill. Each source goes through a frontier model (Claude Opus 4.7 by default via OpenRouter) and comes back as a structured extraction: principles, frameworks, mental models, quotes, anti-patterns.
Cluster and synthesize. Ideas that show up across many sources are promoted; single-source curiosities are demoted; duplicates are merged. The result is a ranked, cross-source skill rather than a transcript of the last thing the model happened to read.
Author. The final step writes either a SKILL.md with a references/ folder (good for global libraries of on-demand experts) or a self-contained AGENTS.md (good for baking one expert's defaults into a specific project), or both.

The whole point of a pipeline rather than "ask the model to pretend to be Warren Buffett" is that the output is grounded. Every cluster has a source. Every quote has a citation in references/sources.md. You can audit where a particular framework came from, which is exactly what you want before you give an agent an opinionated default stance.

Getting started with mimeo

git clone https://github.com/K-Dense-AI/mimeo
cd mimeo
uv sync

Add an OpenRouter key and a Parallel key to .env, then:

# Generate a SKILL.md (on-demand) for an expert
uv run mimeo "Naval Ravikant"

# Generate an AGENTS.md (always-on) instead
uv run mimeo "Naval Ravikant" --format agents

# Generate both at once (they share the expensive stages)
uv run mimeo "Naval Ravikant" --format both

# Pin an ambiguous name up front for scripted runs
uv run mimeo "John Smith" -d "head basketball coach, Michigan State"

Intermediate artifacts (identity, discovery, raw fetches, distillations) cache under _workspace/, so re-runs and format switches are cheap. The full flag list (mode, max-sources, deep-research, model, concurrency) is in the README.

mimeographs: 60 experts you can install right now

mimeo is the machine. mimeographs is what we generated with it: a curated collection of 60 skills covering founders, philosophers, and scientists, each one distilled from hours of real sources.

Founders and operators: Steve Jobs, Elon Musk, Bill Gates, Mark Zuckerberg, Warren Buffett, Andrew Carnegie, John D. Rockefeller, Henry Ford, Thomas Edison, Walt Disney, Oprah Winfrey, Sara Blakely, Whitney Wolfe Herd, Anne Wojcicki, Judy Faulkner, Kiran Mazumdar-Shaw, Diane Hendricks, Marian Ilitch, Lynda Resnick, Thai Lee.

Philosophers: Aristotle, Plato, Socrates, Confucius, Descartes, Hume, Kant, Nietzsche, Wittgenstein, Heidegger, Hannah Arendt, Simone de Beauvoir, Iris Murdoch, Mary Midgley, Elizabeth Anscombe, Judith Butler, Mary Wollstonecraft, Martha Nussbaum, Hildegard of Bingen, Hypatia of Alexandria.

Scientists and researchers: Aviv Regev, Eric S. Lander, Robert Langer, Shizuo Akira, Stacey Gabriel, Virginia M.-Y. Lee, Zhenan Bao, Zhong Lin Wang, and a cohort of leading epidemiologists including Walter C. Willett, Frank B. Hu, Graham A. Colditz, JoAnn E. Manson, Julie E. Buring, Kay-Tee Khaw, Meir J. Stampfer, Ronald C. Kessler, Tamara B. Harris, Terrie E. Moffitt, Dorret I. Boomsma, and Albert Hofman.

Every folder contains both a SKILL.md (with a references/ directory of principles, frameworks, mental models, quotes, and sources) and an AGENTS.md, so you can pick whichever fits your workflow.

The general pattern we suggest: many SKILL.mds in your global library, one AGENTS.md per project. Install Wittgenstein, Aristotle, and Buffett globally so they fire whenever the task matches; then drop Jobs's AGENTS.md at the root of a consumer app repo, or Regev's at the root of a genomics repo, so every code review, every design decision, every PR description is filtered through the right defaults.

Getting started with mimeographs

The simplest way, works with Claude Code, Cursor, Codex, Gemini CLI, and anything else that speaks the open Agent Skills standard:

# Install a single expert
npx skills add K-Dense-AI/mimeographs/steve-jobs

# Install several at once
npx skills add K-Dense-AI/mimeographs/warren-buffett K-Dense-AI/mimeographs/ludwig-wittgenstein

# Install the whole collection (all 60 experts)
npx skills add K-Dense-AI/mimeographs

npx skills add figures out the right install location for your agent (for example, ~/.claude/skills/ for Claude Code, .cursor/skills/ for Cursor) and drops the files there. If you have the GitHub CLI v2.90.0+, gh skill install K-Dense-AI/mimeographs steve-jobs works too. If you would rather install by hand, every mimeograph is plain Markdown, so you can copy the folder to wherever your agent looks for skills.

Prefer the always-on flavor? Drop the AGENTS.md directly at the root of your project:

cp mimeographs/steve-jobs/AGENTS.md ./AGENTS.md

Restart your agent after installing so it picks up the new files.

What it feels like to use one

Once installed, the SKILL.mds auto-trigger whenever the agent decides the task matches. You do not have to explicitly reach for them; you describe the problem you are actually working on, and the right expert shows up.

"I'm designing the onboarding flow for a new consumer iOS app. Help me think through it."
  → triggers steve-jobs

"Should we acquire this SaaS company trading at 14x ARR?"
  → triggers warren-buffett

"I can't tell if my definition of 'fairness' is actually coherent or just vibes."
  → triggers ludwig-wittgenstein

"Design a prospective cohort study for a dietary intervention."
  → triggers walter-c-willett

The conversations genuinely feel different. The Buffett skill drags every SaaS-acquisition conversation back to owner earnings and durability of the moat. The Wittgenstein skill refuses to let you move on until you have untangled which sense of a word you mean. The Willett skill pushes on confounding and measurement error in a way a generic model never quite does on its own. None of this is magic. It is what happens when you tell a capable model to adopt one specific, well-documented stance instead of averaging across all of them.

A few honest caveats

These are personas, not people. A SKILL.md distilled from public writing is a good approximation of someone's reasoning patterns; it is not them. Buffett on the mimeographs shelf is not going to call you out of the blue. More importantly, the output is only as good as the sources. People whose best work is behind paywalls, unpublished, or preserved mostly in private correspondence will come out thinner than people who wrote a lot of public essays.

We also take skill security seriously, and you should too. A skill is executable research code with a personality; it shapes what an agent does on your behalf. Skim a mimeograph before you install it, prefer specific sub-folders over the whole repo if you only need one or two, and pin versions in anything that touches sensitive data. Every quote in the references is cited, so you can spot-check the reasoning before you trust it.

Finally, SKILL.md and AGENTS.md are load-bearing text files, not magic. They work best when the task actually benefits from a specific stance: product design, research design, investment calls, ethics questions, writing. For pure mechanical tasks ("convert this CSV to JSON"), a philosopher is just going to slow you down.

Why we built this

We build K-Dense Web, a platform for autonomous scientific research, and we maintain Scientific Agent Skills, the largest open catalog of skills for scientific work. Both are built on the same underlying bet: that the most important artifact in the current AI stack is not the model, but the small, auditable, editable file that tells the model what kind of thinking to do.

SKILL.md and AGENTS.md are that file. mimeo is a way to generate them at research-grade quality for any person whose thinking you care about. mimeographs is proof that you can do it 60 times and get something useful out the other side.

We think the interesting next step is not us generating another hundred of these ourselves. It is researchers, engineers, and labs running mimeo on the people whose thinking actually matters to their work (a founder's old blog posts, a PI's lecture series, a mentor's recorded talks) and then PR-ing the result back so everyone else benefits. That is how a skill library for expert thinking compounds.

If that sounds worth trying, both repos are live:

mimeo: github.com/K-Dense-AI/mimeo
mimeographs: github.com/K-Dense-AI/mimeographs

Install a mimeograph. Run mimeo on someone whose thinking you wish you could borrow. Open a PR. Tell us who we got wrong.

Questions, feedback, or a mimeograph you'd like to contribute? Find us in the K-Dense Slack community or email contact@k-dense.ai.

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