PhysicsNeMo is an open-source PyTorch framework for creating, training, and fine-tuning machine-learning models for physics, scientific computing, and engineering. Researchers and engineers use its reusable components and training recipes for applications such as aerodynamics, weather forecasting, structural mechanics, geophysics, and thermal design. The catalogue entries provide rules and skills for working with PhysicsNeMo projects.
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 NVIDIA/physicsnemo --skill physicsnemo-discovergit clone --depth 1 https://github.com/NVIDIA/physicsnemoWrote 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/nvidia/physicsnemo/physicsnemo-discover)<a href="https://agentmods.dev/skills/nvidia/physicsnemo/physicsnemo-discover"><img src="https://agentmods.dev/badge/skills/nvidia/physicsnemo/physicsnemo-discover.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00124 | $0.01794 |
| Opus 5 | $0.00062 | $0.00897 |
| Sonnet 5 | $0.00025 | $0.00359 |
| Haiku 4.5 | $0.00012 | $0.00179 |
Grade A, and why
physicsnemo-discover 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.
How it starts
The opening of the file, as written. The whole thing — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PhysicsNeMo Discoverability
Help a user navigate PhysicsNeMo: point them at files, folders, examples, and docs in the repo at its current state. Never write training code; never cite a path from memory.
Core principle
PhysicsNeMo evolves — classes get renamed, examples move, experimental/ graduates. Any static list of class names and paths rots, so discover, don't remember: enumerate from the live repo every turn.
PhysicsNeMo is composable: each solution is a product (model family × datapipe × training strategy × config). An example is one reference instantiation of that product, not a prescription. Surface the axes and the menu along each axis, then cite examples as concrete starting points to fork and recombine.
What a correct answer satisfies
These are constraints, not a script — choose the searches that meet them and skip work the task doesn't need. Search patterns per axis live in references/RECIPES.md.
- Live-grounded. Every class, path, and example you name was read or globbed this turn.
__init__.pyproves what is exported, not what files exist — Globphysicsnemo/models/<family>/*.pybefore naming a sibling implementation file. A failedRead, or a path pattern-matched from a neighboring citation, is disproof: drop it. - Verified before emit. Every absolute path you plan to cite survives one
Bash ls -d <path1> <path2> …round-trip before you write the response. Hard gate — skipping it has produced real-basename-under-wrong-parent hallucinations. If a basename was right but the parent wrong, re-Glob and re-verify; if you can't relocate it, drop the citation. - A menu, not a single pick. Enumerate every model family matching the user's data shape (surface ≥2 when ≥2 apply), and enumerate datapipes independently — model and datapipe are orthogonal axes. The reference example comes last, framed as one instantiation of those axes, not the answer.
- Self-documentation is ground truth.
__init__.pyexports, per-exampleREADME.md,docs/*.rst,pyproject.toml, top-of-file module docstrings. Treatreferences/TAXONOMY.mdas a navigation hint, not an answer. Flag anything underphysicsnemo/experimental/as "API may change." - Abstain when out of scope. PhysicsNeMo targets SciML/AI4Science (surrogates, forecasting, super-resolution, physics-informed, inverse, generative for physical systems). If the task is categorically outside that — reinforcement learning, classical control, generic CV/NLP, symbolic regression — skip enumeration and emit the Abstention output below. Do not list adjacent-but-wrong examples in its place (pointing at
active_learning/for an RL question is fabrication). When unsure whether a task is in scope, abstain.
What ships with it
6 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 First seen · 104 lines · 124 tokens per session scan A 7ff4564485e9
physicsnemo-discover is a skill published in the GitHub repository NVIDIA/physicsnemo (3,223 stars, last pushed 2d ago), licensed Apache-2.0. It adds 124 tokens to every session and 1,794 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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