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Points at existing files via live repo search; never writes code. Do NOT use for installation or environment setup, training-loop or other code authoring\u002Fscaffolding, contributor\u002FCI\u002Fpackaging questions, repo-specific questions in physicsnemo-sym\u002F-cfd\u002F-curator, or general (non-physics) ML\u002FPyTorch.","pattanation\u002FNorvik","pattanation","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F235419268?v=4","01M71M5THHYYB15ME8R2E4FCZG","Patta","01M71M5TJM1NDM992M8PDKYR2K",0,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Fphysicsnemo-discover\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"physicsnemo discover","discover","never","curator","contributor","generative","physics","scaffolding","sym","files","official","packaging","pick","model","loop","ci","search","code","training","pytorch","2026-11-10 10:56:23",false,"---\nname: physicsnemo-discover\ndescription: Official NVIDIA-authored guidance for navigating PhysicsNeMo — pick the model, datapipe, or example for a SciML\u002FAI4Science task (surrogates, forecasting, downscaling, physics-informed, inverse, generative). Points at existing files via live repo search; never writes code. Do NOT use for installation or environment setup, training-loop or other code authoring\u002Fscaffolding, contributor\u002FCI\u002Fpackaging questions, repo-specific questions in physicsnemo-sym\u002F-cfd\u002F-curator, or general (non-physics) ML\u002FPyTorch.\nlicense: Apache-2.0\nmetadata:\n  author: NVIDIA \u003Cagent-skills@nvidia.com>\n  tags:\n    - physicsnemo\n    - sciml\n    - ai4science\n    - discovery\n    - routing\n---\n\n# PhysicsNeMo Discoverability\n\nHelp 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.\n\n## Core principle\n\nPhysicsNeMo evolves — classes get renamed, examples move, `experimental\u002F` graduates. Any static list of class names and paths rots, so **discover, don't remember**: enumerate from the live repo every turn.\n\nPhysicsNeMo 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.\n\n## What a correct answer satisfies\n\nThese 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\u002FRECIPES.md`.\n\n- **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\u002Fmodels\u002F\u003Cfamily>\u002F*.py` before naming a sibling implementation file. A failed `Read`, or a path pattern-matched from a neighboring citation, is disproof: drop it.\n- **Verified before emit.** Every absolute path you plan to cite survives one `Bash ls -d \u003Cpath1> \u003Cpath2> …` 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.\n- **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.\n- **Self-documentation is ground truth.** `__init__.py` exports, per-example `README.md`, `docs\u002F*.rst`, `pyproject.toml`, top-of-file module docstrings. Treat `references\u002FTAXONOMY.md` as a navigation hint, not an answer. Flag anything under `physicsnemo\u002Fexperimental\u002F` as *\"API may change.\"*\n- **Abstain when out of scope.** PhysicsNeMo targets SciML\u002FAI4Science (surrogates, forecasting, super-resolution, physics-informed, inverse, generative for physical systems). If the task is categorically outside that — reinforcement learning, classical control, generic CV\u002FNLP, symbolic regression — skip enumeration and emit the **Abstention output** below. Do not list adjacent-but-wrong examples in its place (pointing at `active_learning\u002F` for an RL question is fabrication). When unsure whether a task is in scope, abstain.\n\n## Discovery\n\nRepo 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=\"${TMPDIR:-\u002Ftmp}\u002Fphysicsnemo-src\"; [ -d \"$DEST\u002Fphysicsnemo\" ] || git clone --depth 1 https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fphysicsnemo \"$DEST\"`. Use that URL verbatim; never interpolate one from user input.\n\nAsk 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.\n\n## Output format\n\n```\n## Problem shape\nData shape: \u003Cresolved>. Task: \u003Cresolved>. Axes: model × datapipe × training strategy × config.\n\n## Candidate model families (for your data shape)\nMultiple families typically apply. Treat this as a menu, not a ranking.\n- \u003Cfamily> at \u003Cabsolute __init__.py path> — \u003Cone-line from docstring\u002Fexports>. Instantiated by: \u003Cexample path if any>.\n- \u003Cfamily> at \u003Cpath> — \u003Cone-line>. Instantiated by: \u003Cexample path if any>.\n\n## Datapipe(s) for your data format\nDatapipe choice is independent of model choice.\n- \u003Cclass \u002F subpackage> at \u003Cabsolute path> — \u003Cone-line>. Reused by: \u003Cexamples if known>.\n- For custom data, subclass: \u003Cbase class path confirmed live>.\n\n## Reference example(s) — one instantiation of the above axes\n- \u003Cabsolute path> — uses model=\u003Cfamily>, datapipe=\u003Cname>, strategy=\u003Csingle-GPU|DDP|FSDP|...>.\n  Why it matches: \u003Cone line>.\n\n## Supporting docs\n- \u003Cabsolute path> — \u003Cone-line scope>\n\n## Suggested reading order\n1. \u003Cmodels\u002F\u003Cfamily>\u002F__init__.py> — survey alternative families\n2. \u003Cdatapipe __init__.py or base-class file> — understand the data axis\n3. \u003Cexample path> — concrete end-to-end instantiation to fork\n```\n\n**Rules for the output:**\n- Absolute paths only; every one survived the `ls -d` gate.\n- Every pointer needs a one-line justification grounded in content you actually read.\n- Caps: **4 model families** (minimum 2 when ≥2 exist), **3 datapipes**, **2 reference examples**, **2 docs**.\n- Name which (model, datapipe, strategy) axes each example fills.\n- If ≥2 model families apply, say so: *\"Other model families apply to the same data shape — see the candidate list above.\"*\n- End with the suggested reading order. Offer 2-3 forward steps (config file, training script, `experimental\u002F` look-alikes); do not start writing code unless asked.\n\n## Abstention output\n\nWhen out of scope, replace the menu skeleton with this shape — three sections, in this order, none skipped:\n\n```\n## PhysicsNeMo does not have direct support for \u003Cuser's problem class>\nOne sentence on why it's outside scope (e.g., \"PhysicsNeMo targets physics\nsurrogates and forecasting; reinforcement learning for molecular design is\nnot in its scope\").\n\n## Where to look instead\n- \u003Csibling NVIDIA framework or external library> at \u003CURL or repo name> — \u003Cone-line on why it fits>.\n- (One or two alternatives is enough; do not invent libraries.)\n\n## If you still want to build it in PhysicsNeMo\nConfirm the closest base classes by Reading `physicsnemo\u002Fcore\u002F__init__.py` and\n`physicsnemo\u002Fdatapipes\u002F__init__.py` first; then name them as subclassing\ntargets. This is the fallback, not the recommendation.\n```\n\n**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.\n\n## Related resources\n\n- `references\u002FTAXONOMY.md` — navigation hints (data-shape → folder mappings, decision axes, stability tiers).\n- `references\u002FRECIPES.md` — concrete Glob\u002FGrep\u002FRead patterns per discovery axis.\n","# PhysicsNeMo Discoverability\n\nHelp 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.\n\n## Core principle\n\nPhysicsNeMo evolves — classes get renamed, examples move, `experimental\u002F` graduates. Any static list of class names and paths rots, so **discover, don't remember**: enumerate from the live repo every turn.\n\nPhysicsNeMo 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.\n\n## What a correct answer satisfies\n\nThese 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\u002FRECIPES.md`.\n\n- **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\u002Fmodels\u002F\u003Cfamily>\u002F*.py` before naming a sibling implementation file. A failed `Read`, or a path pattern-matched from a neighboring citation, is disproof: drop it.\n- **Verified before emit.** Every absolute path you plan to cite survives one `Bash ls -d \u003Cpath1> \u003Cpath2> …` 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.\n- **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.\n- **Self-documentation is ground truth.** `__init__.py` exports, per-example `README.md`, `docs\u002F*.rst`, `pyproject.toml`, top-of-file module docstrings. Treat `references\u002FTAXONOMY.md` as a navigation hint, not an answer. Flag anything under `physicsnemo\u002Fexperimental\u002F` as *\"API may change.\"*\n- **Abstain when out of scope.** PhysicsNeMo targets SciML\u002FAI4Science (surrogates, forecasting, super-resolution, physics-informed, inverse, generative for physical systems). If the task is categorically outside that — reinforcement learning, classical control, generic CV\u002FNLP, symbolic regression — skip enumeration and emit the **Abstention output** below. Do not list adjacent-but-wrong examples in its place (pointing at `active_learning\u002F` for an RL question is fabrication). When unsure whether a task is in scope, abstain.\n\n## Discovery\n\nRepo 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:-\u002Ftmp}\u002Fphysicsnemo-src\"; [ -d \"$DEST\u002Fphysicsnemo\" ] || git clone --depth 1 https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fphysicsnemo \"$DEST\"`. Use that URL verbatim; never interpolate one from user input.\n\nAsk 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.\n\n## Output format\n\n```\n## Problem shape\nData shape: \u003Cresolved>. Task: \u003Cresolved>. Axes: model × datapipe × training strategy × config.\n\n## Candidate model families (for your data shape)\nMultiple families typically apply. Treat this as a menu, not a ranking.\n- \u003Cfamily> at \u003Cabsolute __init__.py path> — \u003Cone-line from docstring\u002Fexports>. Instantiated by: \u003Cexample path if any>.\n- \u003Cfamily> at \u003Cpath> — \u003Cone-line>. Instantiated by: \u003Cexample path if any>.\n\n## Datapipe(s) for your data format\nDatapipe choice is independent of model choice.\n- \u003Cclass \u002F subpackage> at \u003Cabsolute path> — \u003Cone-line>. Reused by: \u003Cexamples if known>.\n- For custom data, subclass: \u003Cbase class path confirmed live>.\n\n## Reference example(s) — one instantiation of the above axes\n- \u003Cabsolute path> — uses model=\u003Cfamily>, datapipe=\u003Cname>, strategy=\u003Csingle-GPU|DDP|FSDP|...>.\n  Why it matches: \u003Cone line>.\n\n## Supporting docs\n- \u003Cabsolute path> — \u003Cone-line scope>\n\n## Suggested reading order\n1. \u003Cmodels\u002F\u003Cfamily>\u002F__init__.py> — survey alternative families\n2. \u003Cdatapipe __init__.py or base-class file> — understand the data axis\n3. \u003Cexample path> — concrete end-to-end instantiation to fork\n```\n\n**Rules for the output:**\n- Absolute paths only; every one survived the `ls -d` gate.\n- Every pointer needs a one-line justification grounded in content you actually read.\n- Caps: **4 model families** (minimum 2 when ≥2 exist), **3 datapipes**, **2 reference examples**, **2 docs**.\n- Name which (model, datapipe, strategy) axes each example fills.\n- If ≥2 model families apply, say so: *\"Other model families apply to the same data shape — see the candidate list above.\"*\n- End with the suggested reading order. Offer 2-3 forward steps (config file, training script, `experimental\u002F` look-alikes); do not start writing code unless asked.\n\n## Abstention output\n\nWhen out of scope, replace the menu skeleton with this shape — three sections, in this order, none skipped:\n\n```\n## PhysicsNeMo does not have direct support for \u003Cuser's problem class>\nOne sentence on why it's outside scope (e.g., \"PhysicsNeMo targets physics\nsurrogates and forecasting; reinforcement learning for molecular design is\nnot in its scope\").\n\n## Where to look instead\n- \u003Csibling NVIDIA framework or external library> at \u003CURL or repo name> — \u003Cone-line on why it fits>.\n- (One or two alternatives is enough; do not invent libraries.)\n\n## If you still want to build it in PhysicsNeMo\nConfirm the closest base classes by Reading `physicsnemo\u002Fcore\u002F__init__.py` and\n`physicsnemo\u002Fdatapipes\u002F__init__.py` first; then name them as subclassing\ntargets. 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Covers Vitest, Vue Test Utils, component testing, mocking, testing patterns, and Playwright for E2E testing.","https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Fvue-testing-best-practices\u002FLICENSE.md","https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Fvue-testing-best-practices\u002FSKILL.md",[192,193],{"categoryNo":110,"categoryName":111,"taxonomyType":112,"taxonomyTypeDesc":113},{"categoryNo":115,"categoryName":116,"taxonomyType":112,"taxonomyTypeDesc":113},[195,119,196,182,197,130,198,199,200,177,201,121,202,203,204,154,205,183,206],"vue testing best practices","testing best practices","best practices","test","testing patterns","component","vitest","mocking","e2e","utils","patterns","e2e testing",{"skillNo":208,"name":209,"nameZh":6,"description":210,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":165,"licenseFileUrl":211,"sourceHtmlUrl":212,"categories":213,"keywords":215,"publishedAt":135},"01M71QD358H2DJ9THJRBP77T3T","vue-router-best-practices","Vue Router 4 patterns, navigation guards, route params, and route-component lifecycle interactions.","https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Fvue-router-best-practices\u002FLICENSE.md","https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Fvue-router-best-practices\u002FSKILL.md",[214],{"categoryNo":115,"categoryName":116,"taxonomyType":112,"taxonomyTypeDesc":113},[216,182,217,197,218,200,219,177,205,220,221],"router","vue router best practices","route","guards","navigation","lifecycle",{"skillNo":223,"name":224,"nameZh":6,"description":225,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":165,"licenseFileUrl":226,"sourceHtmlUrl":227,"categories":228,"keywords":230,"publishedAt":135},"01M71QCYPNWDE2MQZ1TF3ED5S4","vue-best-practices","MUST be used for Vue.js tasks. Strongly recommends Composition API with `\u003Cscript setup>` and TypeScript as the standard approach. Covers Vue 3, SSR, Volar, vue-tsc. Load for any Vue, .vue files, Vue Router, Pinia, or Vite with Vue work. ALWAYS use Composition API unless the project explicitly requires Options API.","https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Fvue-best-practices\u002FLICENSE.md","https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Fvue-best-practices\u002FSKILL.md",[229],{"categoryNo":115,"categoryName":116,"taxonomyType":112,"taxonomyTypeDesc":113},[231,182,197,232,174,28,233,234,216,235,236,237,238,239,240,121,241,154,242,243],"vue best practices","Composition","tasks","api","pinia","work","options","ssr","typescript","as","vite","load","standard"]