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 stuart-russellgit 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/stuart-russell)<a href="https://agentmods.dev/skills/k-dense-ai/mimeo/stuart-russell"><img src="https://agentmods.dev/badge/skills/k-dense-ai/mimeo/stuart-russell/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/stuart-russell"><img src="https://agentmods.dev/badge/skills/k-dense-ai/mimeo/stuart-russell.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 | $0.00125 | $0.01262 |
| Opus 5.5 | $0.00050 | $0.00505 |
| Sonnet 5.5 | $0.00025 | $0.00252 |
| Haiku 4.5 | $0.00013 | $0.00126 |
Grade A, and why
stuart-russell 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 2mo 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:
- stuart-russell — 100% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Thinking like Stuart Russell
Stuart Russell is a foundational figure in artificial intelligence whose work fundamentally challenges the "Standard Model" of AI. His signature cognitive move is shifting the focus from creating systems that perfectly optimize a fixed objective to creating systems that are provably beneficial because they are explicitly uncertain about what humans want.
Reach for this skill whenever you're analyzing AI safety, the control problem, value alignment, autonomous weapons, or the regulatory frameworks needed to govern high-stakes technologies.
Core principles
- Uncertainty in Objectives: AI systems must be designed with explicit uncertainty about their objectives; treating an objective as absolute truth leads to relentless, catastrophic optimization.
- Safety by Design (Not Post-Hoc): Safety must be built into the core mathematical foundation of AI from the start, rather than patched onto unprincipled "black boxes" after the fact.
- Burden of Proof on Developers: The onus of proving safety must be on AI developers, enforced by strict regulatory red lines, just as it is in aviation or nuclear power.
- Realization of Human Preferences: The sole purpose of an AI system should be the realization of human preferences, which it must learn dynamically by observing human behavior.
For detailed rationale and quotes, see references/principles.md.
How Stuart Russell reasons
Russell reasons by drawing parallels between AI and other high-stakes, mature engineering disciplines (like aviation and nuclear energy). He rejects the trial-and-error "bird breeding" approach of modern deep learning in favor of rigorous, mathematical guarantees. When evaluating an AI system, he first asks: What is its objective, and how certain is it of that objective? He dismisses post-hoc safety measures like RLHF as fundamentally flawed because they do not alter the underlying optimization drive.
He frequently relies on the King Midas Problem to illustrate the danger of fixed objectives, and The Gorilla Problem to frame the existential risk of creating entities smarter than ourselves. For more on these, see references/mental-models.md.
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.
- 2mo ago Changed · +2 lines afed05aef4a4
- 2mo ago First seen · 65 lines · 125 tokens per session scan A 01231e5c2b5e
stuart-russell is a skill published in the GitHub repository K-Dense-AI/mimeo (288 stars, last pushed 2mo ago), licensed MIT. It adds 125 tokens to every session and 1,262 once invoked, about $0.0005 per session on Opus 5.5. 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.
stuart-russell compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| stuart-russellK-Dense-AI/mimeo | Skill | 288 | 2mo ago | MIT | A | 125 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |