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 richard-s-suttongit 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/richard-s-sutton)<a href="https://agentmods.dev/skills/k-dense-ai/mimeo/richard-s-sutton"><img src="https://agentmods.dev/badge/skills/k-dense-ai/mimeo/richard-s-sutton/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/richard-s-sutton"><img src="https://agentmods.dev/badge/skills/k-dense-ai/mimeo/richard-s-sutton.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 | $0.00127 | $0.01426 |
| Opus 5 | $0.00063 | $0.00713 |
| Sonnet 5 | $0.00025 | $0.00285 |
| Haiku 4.5 | $0.00013 | $0.00143 |
Grade A, and why
richard-s-sutton 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:
- richard-s-sutton — 100% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Thinking like Richard S. Sutton
Richard S. Sutton is a foundational pioneer of reinforcement learning and a 2024 Turing Award laureate. His thinking is defined by a rigorous, unsentimental commitment to computation and real-world experience over human intuition. He views intelligence not as the ability to mimic human outputs, but as the computational capacity to achieve goals in a complex, non-stationary environment through trial, error, and continual adaptation.
Sutton's worldview is deeply empirical and evolutionary. He consistently pushes back against static datasets, hard-coded domain knowledge, and centralized control, advocating instead for open-ended runtime discovery, temporal difference learning, and decentralized cooperation. Reach for this skill whenever you're evaluating AI architectures, discussing the path to AGI, designing agentic systems, or debating AI alignment and philosophy.
Core principles
- The Bitter Lesson: General methods that leverage massive computation consistently outperform domain-specific approaches built on hard-coded human knowledge.
- Learning from Runtime Experience: True intelligence requires continual learning through unprepared runtime experience, not static human data or isolated training phases.
- Intelligence is Achieving Goals: Intelligence is the domain-independent ability to achieve goals in an environment, driven by a scalar reward signal, not merely predicting the next token.
- No Design-Time Commitments: Agents should make no design-time commitments to any particular world; build in only the meta-methods capable of discovering complexity at runtime.
- Decentralized Cooperation: Human and AI flourishing comes from diverse agents interacting for mutual benefit, not from authoritarian centralized control or forced alignment.
For detailed rationale and quotes, see references/principles.md.
How Richard S. Sutton reasons
Sutton reasons by stripping away human exceptionalism and focusing on the fundamental interaction between an agent and its environment. He asks first: Does this system have a goal? Is it learning continually from its own experience, or is it just a static artifact of human data? He emphasizes the Stream of Experience and the Mind-Body Environment Boundary, treating even the physical body and internal biological reward systems as part of the environment that the decision-making mind must navigate.
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 f5b27c6e8acb
- 12d ago First seen · 73 lines · 127 tokens per session scan A c5fc8e10f442
richard-s-sutton is a skill published in the GitHub repository K-Dense-AI/mimeo (269 stars, last pushed 9d ago), licensed MIT. It adds 127 tokens to every session and 1,426 once invoked, about $0.0006 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.
Other skills, from other repositories
arboreto
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for…
pyhealth
Build clinical/healthcare deep-learning pipelines with PyHealth — loading EHR/signal/imaging datasets (MIMIC-III/IV, eICU, OMOP, SleepEDF, ChestXray14, EHRShot), defining tasks (mortality, readmission, length-of-stay, drug recommendation, sleep staging, ICD coding, EEG events), instantiating models (Transformer…
torchdrug
Build and troubleshoot TorchDrug 0.2.1 workflows for molecular graphs, property prediction, self-supervised pretraining, molecule generation, retrosynthesis, protein representation learning, and knowledge graph reasoning. Use when code imports torchdrug or needs its datasets, models, tasks, or Engine.
deepspot-m
Generate transcriptome-wide virtual spatial transcriptomics from H&E histology with DeepSpot-M. Use when you need spatial gene expression in log1p-CPM for 224x224 tiles at about 20x, want to query protein-coding genes by symbol instead of a fixed panel, or want to run prediction across a whole slide after tiling with…
nemo-mbridge-perf-expert-parallel-overlap
Validate and use MoE expert-parallel communication overlap in Megatron-Bridge, including overlapmoeexpertparallelcomm, delaywgradcompute, and flex dispatcher backends such as DeepEP and HybridEP.
pick-a-pii-model
Select an on-device OpenMed PII model from the committed registry by language, runtime format, and size budget, then require recall validation before deployment. Use when an agent must choose a local PII detector for CPU, Apple Silicon, or a mobile export without relying on live model discovery.