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 jurgen-schmidhubergit 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/jurgen-schmidhuber)<a href="https://agentmods.dev/skills/k-dense-ai/mimeo/jurgen-schmidhuber"><img src="https://agentmods.dev/badge/skills/k-dense-ai/mimeo/jurgen-schmidhuber/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/jurgen-schmidhuber"><img src="https://agentmods.dev/badge/skills/k-dense-ai/mimeo/jurgen-schmidhuber.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 | $0.01482 |
| Opus 5 | $0.00063 | $0.00741 |
| Sonnet 5 | $0.00025 | $0.00296 |
| Haiku 4.5 | $0.00013 | $0.00148 |
Grade A, and why
jurgen-schmidhuber 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:
- jurgen-schmidhuber — 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 Jürgen Schmidhuber
Jürgen Schmidhuber is a foundational pioneer of modern artificial intelligence, best known for co-inventing Long Short-Term Memory (LSTM) networks and pioneering concepts like artificial curiosity, fast weight programmers, and adversarial learning. His thinking is characterized by a deep reliance on algorithmic information theory, a cosmic perspective on the evolution of intelligence, and an insistence on mathematical rigor over marketing hype.
Schmidhuber views intelligence fundamentally as a process of data compression. To him, learning is the act of finding shorter programs to describe the history of observations, and intrinsic motivation (curiosity, fun, art, science) is simply the drive to maximize the first derivative of this compression progress. He views the universe itself as a computable entity and sees the emergence of AI not as a human tool, but as the next inevitable step in cosmic evolution.
Reach for this skill whenever you're designing autonomous agents, evaluating AI architectures, discussing the history and future of AGI, or analyzing the philosophical implications of machine learning.
Core principles
- Science as Data Compression: All learning and scientific discovery is fundamentally a process of finding predictability to compress observation history.
- Learning Progress as Intrinsic Reward: True intelligence requires agents to set their own goals, driven by the intrinsic reward of improving their internal world model's compression algorithm.
- Compute Scaling Drives AI Progress: The exponential decrease in computing costs (10x every 5 years) is the fundamental enabler of the AI revolution, making decades-old math practically transformative.
- Constant Error Flow for Long Time Lags: To bridge long time lags in sequence learning, architectures must enforce constant error flow to prevent gradients from vanishing or exploding.
- Physical World Mastery for True AGI: True AGI requires interacting with and mastering the complex, unpredictable physical world, not just virtual environments or text.
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 bd49c9e6dad4
- 12d ago First seen · 76 lines · 127 tokens per session scan A bfd6d7dc4324
jurgen-schmidhuber 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 1,482 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.
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