physicsnemo-discover

PhysicsNeMo Discoverability

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/<family>/*.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 <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.
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.

Discovery

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.

Output format

## 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>.

Rules for the output:

Absolute paths only; every one survived the ls -d gate.
Every pointer needs a one-line justification grounded in content you actually read.
Caps: 4 model families (minimum 2 when ≥2 exist), 3 datapipes, 2 reference examples, 2 docs.
Name which (model, datapipe, strategy) axes each example fills.
If ≥2 model families apply, say so: "Other model families apply to the same data shape — see the candidate list above."
End with the suggested reading order. Offer 2-3 forward steps (config file, training script, experimental/ look-alikes); do not start writing code unless asked.

Abstention output

When out of scope, replace the menu skeleton with this shape — three sections, in this order, none skipped:

Do not open with the menu skeleton and bury "no match" at the end. Do not invent external libraries — if you don't know the right alternative, stop at the first two sections.

references/TAXONOMY.md — navigation hints (data-shape → folder mappings, decision axes, stability tiers).
references/RECIPES.md — concrete Glob/Grep/Read patterns per discovery axis.