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.
git 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/rules/nvidia/physicsnemo/mod-002a-experimental-models-belong-in-experimental)<a href="https://agentmods.dev/rules/nvidia/physicsnemo/mod-002a-experimental-models-belong-in-experimental"><img src="https://agentmods.dev/badge/rules/nvidia/physicsnemo/mod-002a-experimental-models-belong-in-experimental.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 | $0.00030 | $0.00619 |
| Opus 5 | $0.00015 | $0.00309 |
| Sonnet 5 | $0.00006 | $0.00124 |
| Haiku 4.5 | $0.00003 | $0.00062 |
Grade A, and why
mod-002a-experimental-models-belong-in-experimental 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.
What it actually says
When creating new model or layer classes, rule MOD-002a must be followed. Explicitly reference "Following rule MOD-002a, which states that new models should start in physicsnemo/experimental/..." when explaining where to place new code.
MOD-002a: New models and layers belong in physicsnemo.experimental
Description:
For the vast majority of models, new classes are created either in
physicsnemo/experimental/nn for reusable layers, or in
physicsnemo/experimental/models for more complete models. The experimental
folder is used to store models that are still under development (beta or alpha
releases) during this stage, backward compatibility is not guaranteed.
One exception is when the developer is highly confident that the model is
sufficiently mature and applicable to many domains or use cases. In this case
the model class can be created in the physicsnemo/nn or physicsnemo/models
folders directly, and backward compatibility is guaranteed.
Another exception is when the model class is highly specific to a single
example. In this case, it may be acceptable to place it in a module specific to
the example code, such as examples/<example_name>/utils/nn.py.
After staying in experimental for a sufficient amount of time (typically at
least 1 release cycle), the model class can be promoted to production. It is
then moved to the physicsnemo/nn or physicsnemo/models folders, based on
whether it's a reusable layer or complete model (see MOD-000a and MOD-000b).
Note: Per MOD-008a, MOD-008b, and MOD-008c, it is forbidden to move a model out of the experimental stage/directory without the required CI tests.
Rationale:
The experimental stage allows rapid iteration without backward compatibility constraints, enabling developers to refine APIs based on user feedback. This protects users from unstable APIs while allowing innovation.
Example:
# Good: Stage 1 - New experimental model
# File: physicsnemo/experimental/models/new_diffusion.py
class DiffusionModel(Module):
"""New diffusion model under active development. API may change."""
pass
# Good: After 1+ release cycles, promoted to production
# File: physicsnemo/models/diffusion.py (moved from experimental/)
class DiffusionModel(Module):
"""Stable diffusion model with backward compatibility guarantees."""
pass
Anti-pattern:
# WRONG: New model directly in production folder
# File: physicsnemo/models/brand_new_model.py (should be in experimental/ first)
class BrandNewModel(Module):
"""Skipped experimental stage - risky for stability"""
pass
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 First seen · 66 lines · 30 tokens per session scan A 55d77b5ae004
mod-002a-experimental-models-belong-in-experimental is a cursor rule published in the GitHub repository NVIDIA/physicsnemo (3,228 stars, last pushed today), licensed Apache-2.0. It adds 30 tokens to every session and 619 once invoked, about $0.0002 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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