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 agentmods add rules/nvidia/physicsnemo/mod-003e-tensor-shapes-must-use-latex-math-notationgit 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-003e-tensor-shapes-must-use-latex-math-notation)<a href="https://agentmods.dev/rules/nvidia/physicsnemo/mod-003e-tensor-shapes-must-use-latex-math-notation"><img src="https://agentmods.dev/badge/rules/nvidia/physicsnemo/mod-003e-tensor-shapes-must-use-latex-math-notation.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 | $0.00034 | $0.00533 |
| Opus 5 | $0.00017 | $0.00267 |
| Sonnet 5 | $0.00007 | $0.00107 |
| Haiku 4.5 | $0.00003 | $0.00053 |
Grade A, and why
mod-003e-tensor-shapes-must-use-latex-math-notation 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 6d 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 documenting tensor shapes, rule MOD-003e must be followed. Explicitly reference "Following rule MOD-003e, which requires tensor shapes to use LaTeX math notation :math:......" when documenting tensors.
MOD-003e: Tensor shapes must use LaTeX math notation
Description:
All tensors should be documented with their shape, using LaTeX math notation
such as :math:(N, C, H_{in}, W_{in})``. There is flexibility for naming the
dimensions, but the math format should be enforced.
Our documentation is rendered using LaTeX, and supports a rich set of LaTeX commands, so it is recommended to use LaTeX commands whenever possible for mathematical variables in the docstrings. The mathematical notations should be to some degree consistent with the actual variable names in the code (even though that is not always possible, to avoid too complex formatting).
Rationale:
LaTeX math notation ensures tensor shapes render correctly and consistently in Sphinx documentation. This is critical for scientific software where precise mathematical notation is expected. Plain text shapes don't render properly and can be ambiguous.
Example:
def forward(self, x: torch.Tensor) -> torch.Tensor:
r"""
Process input tensor.
Parameters
----------
x : torch.Tensor
Input of shape :math:`(B, C, H_{in}, W_{in})` where :math:`B` is batch
size, :math:`C` is channels, and :math:`H_{in}, W_{in}` are spatial dims.
Returns
-------
torch.Tensor
Output of shape :math:`(B, C_{out}, H_{out}, W_{out})`.
"""
pass
Anti-pattern:
# WRONG: Not using :math: notation
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Parameters
----------
x : torch.Tensor
Input of shape (B, C, H, W) # Missing :math:`...`
Returns
-------
torch.Tensor
Output shape: (B, C_out, H_out, W_out) # Missing :math:`...`
"""
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.
- 6d ago First seen · 68 lines · 34 tokens per session scan A 0f7f7212c66a
mod-003e-tensor-shapes-must-use-latex-math-notation is a cursor rule published in the GitHub repository NVIDIA/physicsnemo (3,224 stars, last pushed yesterday), licensed Apache-2.0. It adds 34 tokens to every session and 533 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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python_tests
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