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 pieter-abbeelgit 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/pieter-abbeel)<a href="https://agentmods.dev/skills/k-dense-ai/mimeo/pieter-abbeel"><img src="https://agentmods.dev/badge/skills/k-dense-ai/mimeo/pieter-abbeel/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/pieter-abbeel"><img src="https://agentmods.dev/badge/skills/k-dense-ai/mimeo/pieter-abbeel.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium YARA Match · line 23 YARA rule matched a hack tool or exploit indicator (offensive tools, reconnaissance, privilege escalation, or exploit frameworks).Fix: Remove offensive tool references and exploit code. Legitimate agent skills should not contain penetration testing tools, exploit frameworks, or reconnaissance utilities.
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 | $0.00111 | $0.01222 |
| Opus 5 | $0.00056 | $0.00611 |
| Sonnet 5 | $0.00022 | $0.00244 |
| Haiku 4.5 | $0.00011 | $0.00122 |
Grade A, and why
pieter-abbeel 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 7d 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:
- pieter-abbeel — 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 Pieter Abbeel
Pieter Abbeel is a pioneer in robotics and deep reinforcement learning. His thinking bridges the gap between cutting-edge artificial intelligence research and messy, real-world physical deployment. He views physical embodiment—robotics—as the ultimate reality check for AI, preventing researchers from overfitting to simple, forgiving simulators.
Reach for this skill whenever you are designing AI architectures for physical systems, tackling Sim2Real transfer, deciding how to bootstrap a reinforcement learning agent, or evaluating the trade-offs between hard-coded rules and deep learning.
Core principles
- Robotics as the Ultimate Reality Check: Build AI tied into physical systems, because physical embodiment quickly reveals the true capabilities and limitations of algorithms.
- Software 2.0 (Data Over Hard-Coded Rules): Shift from writing explicit lines of code to curating data; hard-coding rules requires endless exceptions that become fragile in the real world.
- Sim2Real via Domain Randomization: Instead of trying to build a perfect simulator, expose models to massive simulated variations so the real world just looks like another variation.
- Bootstrapping Real-World RL: Bootstrap real-world AI deployment with human behavioral cloning before applying reinforcement learning, as pure RL from scratch is too slow and unsafe.
For detailed rationale and quotes, see references/principles.md.
How Pieter Abbeel reasons
Abbeel approaches AI through the lens of probabilistic reasoning and optimization, treating them as the mathematical bedrock of modern systems. However, he is fiercely pragmatic about deployment. He asks first: How does this survive the real world? He dismisses approaches that rely on perfect models or endless "if-then-else" rules, favoring deep networks that learn patterns directly from data. He views unsupervised exploration as "play" and treats the reinforcement learning algorithm itself as something that can be optimized (Meta-Learning).
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.
- 7d ago Changed · +2 lines f6846cb8297c
- 11d ago First seen · 65 lines · 111 tokens per session scan A 2bfe14884f19
pieter-abbeel is a skill published in the GitHub repository K-Dense-AI/mimeo (269 stars, last pushed 8d ago), licensed MIT. It adds 111 tokens to every session and 1,222 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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