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.py
proves what is
exported
, not what files exist — Glob
physicsnemo/models//.py
before naming a sibling implementation file. A failed
Read
, 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 …
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.py
exports, per-example
README.md
,
docs/.rst
,
pyproject.toml
, top-of-file module docstrings. Treat
references/TAXONOMY.md
as a navigation hint, not an answer. Flag anything under
physicsnemo/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.
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Repository
nvidia/skills
GitHub Stars
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First Seen
May 30, 2026
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