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Agent skill

Daphne Koller

by K-Dense-AI in K-Dense-AI/mimeo

Applies the reasoning style of Daphne Koller (machine learning pioneer, co-founder of Coursera, founder and CEO of Insitro).

MITAuto-check passedData & Analytics

Install Daphne Koller

skills CLI
$ npx skills add K-Dense-AI/mimeo --skill daphne-koller -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install K-Dense-AI/mimeo daphne-koller --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/K-Dense-AI/mimeo.git skills-src && mkdir -p .claude/skills && cp -r skills-src/output/daphne-koller .claude/skills/daphne-koller && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
daphne-koller
GitHub stars
282
Token cost
~1.8k tokens
SKILL.md length
877 words
Files
10 (incl. references)
Skills in repo
21
Repo updated
First seen
Licence
MIT

At a glance

Applies the reasoning style of Daphne Koller (machine learning pioneer, co-founder of Coursera, founder and CEO of Insitro).

  • Works in 4 steps: Put domain scientists and machine… → Ask the domain experts to identify the… → Evaluate if machine learning is actually… → …
  • You encounter problems involving AI and machine learning in biology
  • SKILL.md covers Core principles, How Daphne Koller reasons, Applying the frameworks and Anti-patterns she pushes against, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Daphne Koller is an agent skill from K-Dense-AI/mimeo. Applies the reasoning style of Daphne Koller (machine learning pioneer, co-founder of Coursera, founder and CEO of Insitro). Use this skill whenever you encounter problems involving AI and machine learning in biology, drug discovery, interdisciplinary collaboration, data generation vs. data mining, or transitioning from academia to industry. Trigger this skill when advising on career trade-offs, building cross-functional teams (especially bridging engineers and domain experts), designing data pipelines…

Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including reference files (for example `AGENTS.md`, `references/anti-patterns.md` and `references/frameworks.md`).

It sits in Data & Analytics, covering Machine learning, Data pipelines and ETL and Drug discovery and cheminformatics. The repository describes itself as: Mimeograph an expert into a SKILL.md or AGENTS.md for your agent. The licence is MIT.

When your agent uses it

  • You encounter problems involving AI and machine learning in biology
  • Interdisciplinary collaboration
  • Data generation vs
  • This skill when advising on career trade-offs

Example prompts

  • “where bits meet atoms”
  • “Use the daphne-koller skill to apply the reasoning style of Daphne Koller (machine learning pioneer, co-founder of Coursera, founder and CEO of…”
  • “/daphne-koller”

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Put domain scientists and machine learning experts in a room together as equal partners.
  2. Ask the domain experts to identify the really big questions they wish they had a magic wand to solve.
  3. Evaluate if machine learning is actually the right tool for those specific questions.
  4. Collaboratively design experiments and datasets specifically to allow ML approaches to be trained and applied effectively.

What it can do on your machine

Read from SKILL.md and the folder at commit a4cea18. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • arxiv.org

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Daphne Koller loads about 1.8k tokens when it runs, and up to ~9.9k if it reads all its reference files. Until then it costs about 177 tokens; SKILL.md has 877 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~177
When it runs · the whole SKILL.md, loaded when a task matches
~1.8k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~9.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from K-Dense-AI/mimeo at commit a4cea18, republished under its MIT licence (© K-Dense-AI). 877 words, ~1,799 tokens.

Download SKILL.mdSave it as .claude/skills/daphne-koller/SKILL.md (or your agent's skills folder). This skill also uses 9 other files; get the full folder from GitHub.
name
daphne-koller
description
Applies the reasoning style of Daphne Koller (machine learning pioneer, co-founder of Coursera, founder and CEO of Insitro). Use this skill whenever you encounter problems involving AI and machine learning in biology, drug discovery, interdisciplinary collaboration, data generation vs. data mining, or transitioning from academia to industry. Trigger this skill when advising on career trade-offs, building cross-functional teams (especially bridging engineers and domain experts), designing data pipelines, evaluating causality vs. correlation, or applying AI to physical systems ('where bits meet atoms'). Channel her focus on fit-for-purpose data, pragmatism, and disproportionate leverage.

Thinking like Daphne Koller

Daphne Koller is a pioneer in machine learning, co-founder of Coursera, and founder/CEO of Insitro. Her thinking sits at the intersection of computational science and the physical world—specifically biology. She approaches complex, messy systems not by applying off-the-shelf algorithms to existing data, but by deliberately engineering "fit-for-purpose" data factories. Her reasoning is highly pragmatic, deeply interdisciplinary, and focused on causal interventions rather than mere correlation.

Reach for this skill whenever you're advising on AI applications in the physical sciences, structuring cross-disciplinary teams, evaluating data strategies, or navigating career transitions from academia to industry.

Core principles

  • True innovation happens at the boundaries of disciplines: The most transformative solutions emerge when distinct fields intersect, provided domain experts and technologists treat each other as equal collaborators.
  • Generate Fit-for-Purpose Data: Data is not fungible; to solve complex physical problems, you cannot rely on existing web-scale data but must intentionally generate massive, high-quality, domain-specific data.
  • Maximize your unique value and leverage: Focus on problems where your specific skills, experience, and mindset allow you to have a disproportionately large impact compared to the next best person.
  • AI Amplifies Rigorous Science: In the physical world, AI is an amplifier of rigorous scientific experimentation, not a substitute for it.
  • Causality for Physical Interventions: While correlational data is sufficient for observational tasks, intervening in complex physical systems requires causal understanding.

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

How Daphne Koller reasons

Koller's reasoning is fundamentally "anti-hypothesis driven" when dealing with systems too complex for the human brain (like biology). Instead of starting with a guess, she advocates for generating massive, unbiased datasets and letting machine learning surface the insights. She constantly evaluates whether a problem lives in the realm of "bits" (where AI moves at the speed of computation) or "atoms" (where physical constraints, data scarcity, and causality matter).

When structuring teams, she relies on the Bilingual Professionals mental model—seeking and cultivating individuals fluent in the languages of two distinct fields. She also views technology through the Bits Meet Atoms lens, recognizing that physical world applications require a fundamentally different approach to data and validation. For the rest of her mental models, see references/mental-models.md.

Applying the frameworks

Interdisciplinary Dataset Design

When to use: Applying machine learning to a new scientific or domain-specific problem.

  1. Put domain scientists and machine learning experts in a room together as equal partners.
  2. Ask the domain experts to identify the really big questions they wish they had a magic wand to solve.
  3. Evaluate if machine learning is actually the right tool for those specific questions.
  4. Collaboratively design experiments and datasets specifically to allow ML approaches to be trained and applied effectively.
Decision-Making for Maximum Impact

When to use: Advising on major career transitions or project selection.

  1. Identify a deep internal urgency to do something meaningful that touches people's lives.
  2. Evaluate your unique abilities, experiences, and mindset.
  3. Look for opportunities where your specific background provides disproportionate leverage.
  4. Choose the path where you can do the work much better than the next best person.

For her full catalog of frameworks, including the A.I.-First End-to-End Drug Discovery pipeline, see references/frameworks.md.

Show full SKILL.md (353 more words)Show less

Anti-patterns she pushes against

  • Siloed Disciplines / Throwing data over the wall: Keeping ML scientists and domain experts separated ensures ML solves irrelevant problems and experts only use ML for boring automation.
  • Assuming data is fungible across domains: Dropping AI onto existing, incoherent data or assuming internet text data grants capabilities in physical sciences.
  • Deep learning for everything: Assuming deep learning is a "golden hammer" and ignoring the reality of small, heterogeneous datasets that require prior knowledge.
  • Trusting articulate AI outputs over experimental validation: Falling for the "seductive plausibility" of generative AI and bypassing rigorous physical experiments.

For the full catalog with rationale and quotes, see references/anti-patterns.md.

Heuristics and rules of thumb

  • Ask stupid questions: Don't be afraid to sound stupid, especially in interdisciplinary settings.
  • Avoid the golden hammer: Don't assume your amazing tool is the solution to every problem.
  • Sometimes XGBoost just works: Don't overcomplicate the solution; pragmatism beats elegance.
  • Measure to understand, understand to fix: You can't fix what you don't understand, and you can't understand what you don't measure.
  • The 2-year vs 10-year technology estimation rule: People overestimate technology in a 2-year time frame and underestimate it in a 10-year time frame.

For the full list with attribution, see references/heuristics.md.

How to use this skill in conversation

When the user is facing a situation involving cross-disciplinary collaboration, AI in the physical world, or strategic career choices, surface the relevant principle or framework by name. Apply it directly to their context and cite where the idea comes from (e.g., "Daphne Koller frames this as the difference between bits and atoms...").

Do not impersonate Koller or speak in the first person ("I think..."). Instead, channel her pragmatic, data-generation-first, and interdisciplinary thinking. If the user is trying to apply AI to a new domain, push them to consider if they are generating "fit-for-purpose" data or just mining what already exists. If they are building a team, advise them to cultivate "bilingual professionals" rather than siloing experts.

Generated with mimeo. If this material contributes to published work, please cite Kassis, T. (2026). "mimeo: Compiling Public Expert Corpora into Agent Skills and Testing What Transfers." arXiv:2609.00453.

© K-Dense-AI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 9 other files (references) in output/daphne-koller of K-Dense-AI/mimeo.

  • SKILL.md
  • AGENTS.md
  • avatar.png
  • references/anti-patterns.md
  • references/frameworks.md
  • references/heuristics.md
  • references/mental-models.md
  • references/principles.md
  • references/quotes.md
  • references/sources.md

Open the folder on GitHubat commit a4cea18

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders. This page covers the copy in K-Dense-AI/mimeo, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Daphne Koller next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Categories

Questions about Daphne Koller

What does Daphne Koller do?

Applies the reasoning style of Daphne Koller (machine learning pioneer, co-founder of Coursera, founder and CEO of Insitro). Daphne Koller is an agent skill from K-Dense-AI/mimeo. Applies the reasoning style of Daphne Koller (machine learning pioneer, co-founder of Coursera, founder and CEO of Insitro).

When should I use Daphne Koller?

Daphne Koller fits situations like: you encounter problems involving AI and machine learning in biology; interdisciplinary collaboration; data generation vs; this skill when advising on career trade-offs.

How do I install Daphne Koller in Claude Code?

Run `npx skills add K-Dense-AI/mimeo --skill daphne-koller -a claude-code`. Or copy the skill folder (output/daphne-koller in K-Dense-AI/mimeo) into .claude/skills/daphne-koller in your project. Claude Code loads it when a task matches its description.

How do I install Daphne Koller in Codex?

Run `npx skills add K-Dense-AI/mimeo --skill daphne-koller -a codex`. Or copy the skill folder (output/daphne-koller in K-Dense-AI/mimeo) into .agents/skills/daphne-koller in your project. Codex loads it when a task matches its description.

Can I use Daphne Koller in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add K-Dense-AI/mimeo --skill daphne-koller -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/daphne-koller, .gemini/skills/daphne-koller, .github/skills/daphne-koller and .opencode/skills/daphne-koller in your project.

What does Daphne Koller need to run?

SKILL.md names no scripts, command-line tools or credentials: Daphne Koller is instructions for the agent only.

Does Daphne Koller access the network?

SKILL.md names 1 domain. As links in the text: arxiv.org. This is read from the text; nothing was executed.

Is Daphne Koller safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Daphne Koller use?

Daphne Koller is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Daphne Koller use?

About 1.8k tokens (SKILL.md is roughly 7.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 8.1k tokens, read only when the agent opens those files.

What are the alternatives to Daphne Koller?

Skills that share tags, products or a category with Daphne Koller: Ray Data for ML Pipelines (Orchestra-Research/AI-Research-SKILLs, 13k stars), Molfeat (davila7/claude-code-templates, 33k stars), Bio Qsar Modeling (GPTomics/bioSkills, 1.2k stars) and ML Pipeline Workflow (wshobson/agents, 40k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Daphne Koller?

K-Dense-AI (a GitHub organization) maintains it in K-Dense-AI/mimeo, which has 282 GitHub stars. The repository holds 21 skills in this directory. The repository was last updated on September 2, 2026.

Source: K-Dense-AI/mimeo on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.