What is daphne-koller?

A set of reasoning principles based on Daphne Koller's work at the intersection of machine learning and biology, including drug discovery and experimental science.

Use it when planning AI for biology, drug research, experimental data generation, cross-disciplinary teamwork, or a move from academic research to industry.

It helps teams handle messy biological problems by combining computing with domain expertise and by creating data designed for the question being studied.

daphne-koller — Skill for Claude Code and Codex by K-Dense-AI

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add K-Dense-AI/mimeographs --skill daphne-koller

Clone the repo

git clone --depth 1 https://github.com/K-Dense-AI/mimeographs

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/k-dense-ai/mimeographs/daphne-koller/github.svg)](https://agentmods.dev/skills/k-dense-ai/mimeographs/daphne-koller)

Your own site

<a href="https://agentmods.dev/skills/k-dense-ai/mimeographs/daphne-koller"><img src="https://agentmods.dev/badge/skills/k-dense-ai/mimeographs/daphne-koller/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/k-dense-ai/mimeographs/daphne-koller"><img src="https://agentmods.dev/badge/skills/k-dense-ai/mimeographs/daphne-koller.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 131 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,316 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin 100% copy Near-identical to another mod in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00131	$0.01316
Opus 5	$0.00066	$0.00658
Sonnet 5	$0.00026	$0.00263
Haiku 4.5	$0.00013	$0.00132

Measured 12d ago against content hash 094c727c81dc, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

daphne-koller scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

100% identical to daphne-koller — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

mimeographs/daphne-koller/SKILL.md · 70 lines

How it starts

The opening of the file, as written. The whole thing — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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).

Read the full file on GitHub · 70 lines

Files

What ships with it

60 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

12d ago First seen · 70 lines · 131 tokens per session scan A 094c727c81dc

Subscribe to this mod's changes

daphne-koller is a skill published in the GitHub repository K-Dense-AI/mimeographs (123 stars, last pushed 23d ago), licensed MIT. It adds 131 tokens to every session and 1,316 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to daphne-koller, differing in 2 lines, and is treated as a copy.

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