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-002b-add-deprecation-warnings-to-model)<a href="https://agentmods.dev/rules/nvidia/physicsnemo/mod-002b-add-deprecation-warnings-to-model"><img src="https://agentmods.dev/badge/rules/nvidia/physicsnemo/mod-002b-add-deprecation-warnings-to-model/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/rules/nvidia/physicsnemo/mod-002b-add-deprecation-warnings-to-model"><img src="https://agentmods.dev/badge/rules/nvidia/physicsnemo/mod-002b-add-deprecation-warnings-to-model.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00033 | $0.00567 |
| Opus 5 | $0.00016 | $0.00283 |
| Sonnet 5 | $0.00007 | $0.00113 |
| Haiku 4.5 | $0.00003 | $0.00057 |
Grade A, and why
mod-002b-add-deprecation-warnings-to-model 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 9d 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 deprecating a model class, rule MOD-002b must be followed. Explicitly reference "Following rule MOD-002b, which requires adding deprecation warnings to both docstring and runtime..." when implementing deprecation.
MOD-002b: Add deprecation warnings to deprecating model class
Description:
For a model class in the pre-deprecation stage in physicsnemo/nn or
physicsnemo/models, the developer should start planning its deprecation. This
is done by adding a warning message to the model class, indicating that the
model class is deprecated and will be removed in a future release.
The warning message should be a clear and concise message that explains why the model class is being deprecated and what the user should do instead. The deprecation message should be added to both the docstring and should be raised at runtime. The developer is free to choose the mechanism to raise the deprecation warning.
A model class cannot be deprecated without staying in the pre-deprecation stage for at least 1 release cycle before it can be deleted from the codebase.
Rationale:
Ensures users have sufficient time to migrate to newer alternatives, preventing breaking changes that could disrupt their workflows. This graduated approach balances innovation with stability, a critical requirement for a scientific computing framework.
Example:
# Good: Pre-deprecation with warning
# File: physicsnemo/models/old_diffusion.py
class DiffusionModel(Module):
"""
Legacy diffusion model.
.. deprecated:: 0.5.0
``OldDiffusionModel`` is deprecated and will be removed in version 0.7.0.
Use :class:`~physicsnemo.models.NewDiffusionModel` instead.
"""
def __init__(self):
import warnings
warnings.warn(
"OldDiffusionModel is deprecated. Use NewDiffusionModel instead.",
DeprecationWarning,
stacklevel=2
)
super().__init__()
Anti-pattern:
# WRONG: No deprecation warning in code
# File: physicsnemo/models/old_model.py
class OldModel(Module):
"""Will be removed next release.""" # Docstring mentions it but no runtime warning
def __init__(self):
# Missing: warnings.warn(..., DeprecationWarning)
super().__init__()
# WRONG: Deprecation without sufficient warning period
# (Model deprecated and removed in same release)
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.
- 9d ago First seen · 70 lines · 33 tokens per session scan A 707ad3ee12c9
mod-002b-add-deprecation-warnings-to-model is a cursor rule published in the GitHub repository NVIDIA/physicsnemo (3,228 stars, last pushed yesterday), licensed Apache-2.0. It adds 33 tokens to every session and 567 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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