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.
npx skills add K-Dense-AI/mimeo --skill daphne-kollergit clone --depth 1 https://github.com/K-Dense-AI/mimeoWrote 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.
[](https://agentmods.dev/skills/k-dense-ai/mimeo/daphne-koller)<a href="https://agentmods.dev/skills/k-dense-ai/mimeo/daphne-koller"><img src="https://agentmods.dev/badge/skills/k-dense-ai/mimeo/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.
<a href="https://agentmods.dev/skills/k-dense-ai/mimeo/daphne-koller"><img src="https://agentmods.dev/badge/skills/k-dense-ai/mimeo/daphne-koller.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.01398 |
| Opus 5 | $0.00066 | $0.00699 |
| Sonnet 5 | $0.00026 | $0.00280 |
| Haiku 4.5 | $0.00013 | $0.00140 |
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 6d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- daphne-koller — 100% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 72 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).
What ships with it
9 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.
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.
- 6d ago Changed · +2 lines 44fbe6beb14e
- 11d ago First seen · 70 lines · 131 tokens per session scan A 094c727c81dc
daphne-koller is a skill published in the GitHub repository K-Dense-AI/mimeo (267 stars, last pushed 8d ago), licensed MIT. It adds 131 tokens to every session and 1,398 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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