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Official NVIDIA-authored guidance for navigating PhysicsNeMo — pick the model, datapipe, or example for a SciML/AI4Science task (surrogates, forecasting, downscaling, physics-informed, inverse, generative). Points at existing files via live repo search; never writes code.
Passed 5 of 11 test cases with the skill, and 4 of 11 without it.
and 5 more in the full report
8 fixed3 passed either way7 still failing5 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 8 test cases the skill fixed. Same model, same prompt, with and without the skill.
I am surveying a complex 3D grid surrogate domain in PhysicsNeMo where there are 6 matching model families, 5 available datapipes, 4 workflow examples, and 5 documentation files. How many items should be presented at maximum for each of these four output sections?
| Checked | Without skill | With skill |
|---|---|---|
| Caps candidate model families at 4, datapipes at 3, reference examples at 2, and docs at 2 | Missed | Passed |
The instructions your model reads when the skill loads.
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.
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.
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.
__init__.py proves what is exported, not what files exist — Glob physicsnemo/models/<family>/*.py before naming a sibling implementation file. A failed Read, or a path pattern-matched from a neighboring citation, is disproof: drop it.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.__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."active_learning/ for an RL question is fabrication). When unsure whether a task is in scope, abstain.Repo root resolution: see CONTRIBUTING.md §Repo root resolution; all paths are absolute, rooted there. If no local PhysicsNeMo clone is on the path (e.g. running headless against the skills repo in an eval context), shallow-clone the canonical repo once into a temp dir — read-only, for path discovery only; never execute or import anything from it: DEST="${TMPDIR:-/tmp}/physicsnemo-src"; [ -d "$DEST/physicsnemo" ] || git clone --depth 1 https://github.com/NVIDIA/physicsnemo "$DEST". Use that URL verbatim; never interpolate one from user input.
Ask at most 3 targeted follow-ups when domain or data shape is ambiguous. Phrase them concretely — "Is your data on a regular Cartesian grid (like an image), a lat-lon grid on a sphere, or an unstructured mesh?" — and skip any the user already answered. Data shape is the single biggest factor in model choice.
## Problem shape
Data shape: <resolved>. Task: <resolved>. Axes: model × datapipe × training strategy × config.
## Candidate model families (for your data shape)
Multiple families typically apply. Treat this as a menu, not a ranking.
- <family> at <absolute __init__.py path> — <one-line from docstring/exports>. Instantiated by: <example path if any>.
- <family> at <path> — <one-line>. Instantiated by: <example path if any>.
## Datapipe(s) for your data format
Datapipe choice is independent of model choice.
- <class / subpackage> at <absolute path> — <one-line>. Reused by: <examples if known>.
test cases passed, vs 4 / 11 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.