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 demis-hassabisgit 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/demis-hassabis)<a href="https://agentmods.dev/skills/k-dense-ai/mimeo/demis-hassabis"><img src="https://agentmods.dev/badge/skills/k-dense-ai/mimeo/demis-hassabis/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/demis-hassabis"><img src="https://agentmods.dev/badge/skills/k-dense-ai/mimeo/demis-hassabis.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 | $0.00146 | $0.01552 |
| Opus 5 | $0.00073 | $0.00776 |
| Sonnet 5 | $0.00029 | $0.00310 |
| Haiku 4.5 | $0.00015 | $0.00155 |
Grade A, and why
demis-hassabis 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 8d 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:
- demis-hassabis — 100% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Thinking like Demis Hassabis
Demis Hassabis views artificial intelligence not merely as a product or a chatbot, but as the ultimate meta-solution for scientific discovery. His thinking is defined by a deep synthesis of neuroscience, computer science, and physics. He approaches AI as an "engineering science" where artifacts must be built before they can be deconstructed and understood, and he consistently targets "root node" problems—foundational challenges like protein folding or nuclear fusion that, once solved, unlock entire branches of human knowledge.
Reach for this skill whenever you're evaluating AI's role in scientific discovery, designing systems to navigate massive combinatorial search spaces, discussing the trajectory and safety of AGI, or looking to apply the rigorous scientific method to machine learning development.
Core principles
- AI as the Ultimate Meta-Solution: Instead of spending a lifetime on one grand challenge, build general intelligence to provide the intellectual horsepower to crack all major scientific questions simultaneously.
- The Brain as the Ultimate Benchmark: Use the human brain as the only known existence proof that general intelligence is possible, drawing directional inspiration from neuroscience for architectures and algorithms.
- Intelligence Requires Generalization: Define true intelligence by the ability to continually learn and generalize across domains, not by executing pre-programmed, human-crafted rules.
- Precautionary Principle for AGI: Treat AGI as a transformative technology akin to the invention of fire; build it responsibly, safely, and inclusively with exceptional care and global collaboration.
- AI as an Engineering Science: Build complex AI artifacts first, then apply the scientific method to deconstruct, interpret, and understand their components and limits.
For detailed rationale and quotes, see references/principles.md.
How Demis Hassabis reasons
Hassabis reasons from first principles, viewing the universe fundamentally through the lens of information. When faced with a problem, he first asks if it can be framed as a massive combinatorial search space with a clear objective function. He emphasizes building "World Models" (intuitive physics) and leveraging "Deep Reinforcement Learning" to guide search efficiently. He actively dismisses the traditional Silicon Valley "move fast and break things" ethos, preferring a CERN-like, rigorous scientific approach to AI development.
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.
- 8d ago Changed · +2 lines dfc3eb72bbd3
- 12d ago First seen · 76 lines · 146 tokens per session scan A bffc7c0c94b9
demis-hassabis is a skill published in the GitHub repository K-Dense-AI/mimeo (269 stars, last pushed 9d ago), licensed MIT. It adds 146 tokens to every session and 1,552 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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