[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71MYZ092K5CGPVRNSQ40V5Q":3,"skill-same:01M71MYZ092K5CGPVRNSQ40V5Q":122},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":51,"commitSha":52,"skillDirectory":53,"forksCount":14,"repositoryHtmlUrl":54,"repositoryHomepageUrl":55,"ownerHtmlUrl":56,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":57,"install":58,"resources":71},"01M71MYZ092K5CGPVRNSQ40V5Q","earth2studio-deterministic-forecast",null,"Build deterministic forecast scripts with Earth2Studio (model, data source, IO, inference). Do NOT use for ensemble, diagnostics, data-only fetch, or install.","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\u002Fearth2studio-deterministic-forecast\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"forecast","earth2studio deterministic forecast","deterministic","build","python","source","for","with","scripts","io","diagnostics","do","NVIDIA","only","use","install","fetch","model","team","data","2026-11-10 10:56:23",false,"---\nname: earth2studio-deterministic-forecast\nversion: 0.16.0\nlicense: Apache-2.0\nmetadata:\n  author: NVIDIA Earth-2 Team\n  tags:\n    - earth2studio\n    - earth2\n    - python\n    - inference\n    - forecast\n    - deterministic\ndescription: >\n  Build deterministic forecast scripts with Earth2Studio (model, data source,\n  IO, inference). Do NOT use for ensemble, diagnostics, data-only fetch, or\n  install.\n---\n\n# Earth2Studio Deterministic Forecast Skill\n\nGuide users through building deterministic (single-member) weather forecast\ninference scripts using `earth2studio.run.deterministic`.\n\n## Prerequisites\n\n- Earth2Studio installed with CUDA-capable GPU\n- Python 3.10+, network access for model weights and data\n\n## Live Doc References\n\nFetch relevant docs to verify current APIs before recommending components:\n\n| Component | URL |\n|-----------|-----|\n| Prognostic models | \u003Chttps:\u002F\u002Fnvidia.github.io\u002Fearth2studio\u002Fmodules\u002Fmodels_px.html> |\n| Data sources (analysis) | \u003Chttps:\u002F\u002Fnvidia.github.io\u002Fearth2studio\u002Fmodules\u002Fdatasources_analysis.html> |\n| Data sources (forecast) | \u003Chttps:\u002F\u002Fnvidia.github.io\u002Fearth2studio\u002Fmodules\u002Fdatasources_forecast.html> |\n| IO backends | \u003Chttps:\u002F\u002Fnvidia.github.io\u002Fearth2studio\u002Fmodules\u002Fio.html> |\n| `run.deterministic` | \u003Chttps:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fearth2studio\u002Fblob\u002Fmain\u002Fearth2studio\u002Frun.py> |\n\n## Workflow\n\n### 1. Gather Requirements (skip what's already provided)\n\n- Time horizon (hours\u002Fdays\u002Fweeks)\n- Variables of interest (t2m, wind, geopotential, etc.)\n- Region (global or specific like CONUS)\n- GPU\u002FVRAM available\n\n### 2. Select Model\n\nFetch prognostic models page. Filter by time horizon, region, VRAM. Note model's:\n- Input variables (`input_coords[\"variable\"]`)\n- Time step size (`output_coords[\"lead_time\"]`)\n\n### 3. Select Data Source\n\nData source must provide all model input variables. Verify via lexicon at\n`earth2studio\u002Flexicon\u002F\u003Csource>.py`. Common pairings: Global models → GFS\u002FARCO\u002FIFS;\nRegional → HRRR.\n\n### 4. Select IO Backend\n\nDefault: `ZarrBackend`. Use `NetCDF4Backend` for legacy tools, `XarrayBackend`\nfor in-memory\u002Fsmall runs.\n\n### 5. Calculate nsteps\n\n`nsteps = forecast_hours \u002F model_step_hours`\n\nExample: 5-day forecast with 6h step → `nsteps = 120 \u002F 6 = 20`\n\n### 6. Decide: output_coords Filtering\n\n- **Filter variables** (`output_coords`) when user requests specific variables (e.g., \"t2m and wind\") - reduces output size\n- **Save all variables** (omit `output_coords`) when user says \"all variables\" or doesn't specify - preserves full model output\n\n### 7. Generate Script\n\n```python\nfrom collections import OrderedDict\nimport numpy as np\nimport torch\nfrom earth2studio.models.px import \u003CModelClass>\nfrom earth2studio.data import \u003CDataSourceClass>\nfrom earth2studio.io import \u003CIOBackendClass>\nfrom earth2studio.run import deterministic\n\nmodel = \u003CModelClass>.load_model(\u003CModelClass>.load_default_package())\ndata = \u003CDataSourceClass>()\nio = \u003CIOBackendClass>(\"\u003Coutput_path>\")\n\n# Include output_coords ONLY if user requested specific variables\noutput_coords = OrderedDict({\"variable\": np.array([\"t2m\", \"u10m\"])})\n\nio = deterministic(\n    time=[\"YYYY-MM-DDTHH:MM:SS\"],\n    nsteps=\u003CN>,\n    prognostic=model,\n    data=data,\n    io=io,\n    output_coords=output_coords,  # omit if saving all variables\n    device=torch.device(\"cuda\"),\n)\n```\n\n### 8. Manual Loop Alternative\n\nWhen user explicitly requests manual implementation (NOT using `earth2studio.run.deterministic`), follow this checklist in order:\n\n1. **fetch_data** - Get initial conditions: `x, coords = fetch_data(data, time, model.input_coords, device)`\n2. **Setup total_coords** - Build coordinate arrays for time and lead_time dimensions\n3. **io.add_array** - Initialize IO backend with total_coords before loop\n4. **create_iterator** - Create prognostic iterator: `model_iter = model.create_iterator(x, coords)`\n5. **Loop through nsteps** - `for step, (x, coords) in enumerate(model_iter): if step >= nsteps: break`\n6. **map_coords** - Filter output variables if needed: `x_out, coords_out = map_coords(x, coords, output_coords)`\n7. **split_coords** - Prepare for IO write: `x_out, coords_out = split_coords(x_out, coords_out)`\n8. **io.write** - Write each step to backend\n\n### 9. Explain Next Steps\n\n- How to change forecast time or run multiple initializations\n- How to read output (`xr.open_zarr(...)`)\n- Point to diagnostic workflow for post-processing\n\n## Ownership\n\n**Owns:** Model selection, data source compatibility, IO backend selection,\nnsteps calculation, generating `earth2studio.run.deterministic` scripts.\n\n**Does not own:** Ensemble workflows, diagnostics, data-only fetch, installation,\nmodel training.\n\n## Troubleshooting\n\nSee `references\u002Ftroubleshooting.md` for common errors and solutions.\n\n## Reminders\n\n- **Always fetch live docs** before recommending models or data sources - APIs change between releases\n- **Verify lexicon compatibility** - Model input variables must exist in data source's VOCAB\n- **Use `load_default_package()`** - This is the standard pattern for loading model weights\n- **Time format is ISO 8601** - Use `\"YYYY-MM-DDTHH:MM:SS\"` format for the `time` argument\n- **Wind speed needs both components** - If user asks for \"wind speed\", include both `u10m` and `v10m`\n- **nsteps is integer division** - `nsteps = total_hours \u002F\u002F model_step_hours`\n- **ZarrBackend is the default** - Only suggest alternatives if user has specific requirements\n- **GPU is required** - All prognostic models require CUDA; CPU inference is not supported\n","# Earth2Studio Deterministic Forecast Skill\n\nGuide users through building deterministic (single-member) weather forecast\ninference scripts using `earth2studio.run.deterministic`.\n\n## Prerequisites\n\n- Earth2Studio installed with CUDA-capable GPU\n- Python 3.10+, network access for model weights and data\n\n## Live Doc References\n\nFetch relevant docs to verify current APIs before recommending components:\n\n| Component | URL |\n|-----------|-----|\n| Prognostic models | \u003Chttps:\u002F\u002Fnvidia.github.io\u002Fearth2studio\u002Fmodules\u002Fmodels_px.html> |\n| Data sources (analysis) | \u003Chttps:\u002F\u002Fnvidia.github.io\u002Fearth2studio\u002Fmodules\u002Fdatasources_analysis.html> |\n| Data sources (forecast) | \u003Chttps:\u002F\u002Fnvidia.github.io\u002Fearth2studio\u002Fmodules\u002Fdatasources_forecast.html> |\n| IO backends | \u003Chttps:\u002F\u002Fnvidia.github.io\u002Fearth2studio\u002Fmodules\u002Fio.html> |\n| `run.deterministic` | \u003Chttps:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fearth2studio\u002Fblob\u002Fmain\u002Fearth2studio\u002Frun.py> |\n\n## Workflow\n\n### 1. Gather Requirements (skip what's already provided)\n\n- Time horizon (hours\u002Fdays\u002Fweeks)\n- Variables of interest (t2m, wind, geopotential, etc.)\n- Region (global or specific like CONUS)\n- GPU\u002FVRAM available\n\n### 2. Select Model\n\nFetch prognostic models page. Filter by time horizon, region, VRAM. Note model's:\n- Input variables (`input_coords[\"variable\"]`)\n- Time step size (`output_coords[\"lead_time\"]`)\n\n### 3. Select Data Source\n\nData source must provide all model input variables. Verify via lexicon at\n`earth2studio\u002Flexicon\u002F\u003Csource>.py`. Common pairings: Global models → GFS\u002FARCO\u002FIFS;\nRegional → HRRR.\n\n### 4. Select IO Backend\n\nDefault: `ZarrBackend`. Use `NetCDF4Backend` for legacy tools, `XarrayBackend`\nfor in-memory\u002Fsmall runs.\n\n### 5. Calculate nsteps\n\n`nsteps = forecast_hours \u002F model_step_hours`\n\nExample: 5-day forecast with 6h step → `nsteps = 120 \u002F 6 = 20`\n\n### 6. Decide: output_coords Filtering\n\n- **Filter variables** (`output_coords`) when user requests specific variables (e.g., \"t2m and wind\") - reduces output size\n- **Save all variables** (omit `output_coords`) when user says \"all variables\" or doesn't specify - preserves full model output\n\n### 7. Generate Script\n\n```python\nfrom collections import OrderedDict\nimport numpy as np\nimport torch\nfrom earth2studio.models.px import \u003CModelClass>\nfrom earth2studio.data import \u003CDataSourceClass>\nfrom earth2studio.io import \u003CIOBackendClass>\nfrom earth2studio.run import deterministic\n\nmodel = \u003CModelClass>.load_model(\u003CModelClass>.load_default_package())\ndata = \u003CDataSourceClass>()\nio = \u003CIOBackendClass>(\"\u003Coutput_path>\")\n\n# Include output_coords ONLY if user requested specific variables\noutput_coords = OrderedDict({\"variable\": np.array([\"t2m\", \"u10m\"])})\n\nio = deterministic(\n    time=[\"YYYY-MM-DDTHH:MM:SS\"],\n    nsteps=\u003CN>,\n    prognostic=model,\n    data=data,\n    io=io,\n    output_coords=output_coords,  # omit if saving all variables\n    device=torch.device(\"cuda\"),\n)\n```\n\n### 8. Manual Loop Alternative\n\nWhen user explicitly requests manual implementation (NOT using `earth2studio.run.deterministic`), follow this checklist in order:\n\n1. **fetch_data** - Get initial conditions: `x, coords = fetch_data(data, time, model.input_coords, device)`\n2. **Setup total_coords** - Build coordinate arrays for time and lead_time dimensions\n3. **io.add_array** - Initialize IO backend with total_coords before loop\n4. **create_iterator** - Create prognostic iterator: `model_iter = model.create_iterator(x, coords)`\n5. **Loop through nsteps** - `for step, (x, coords) in enumerate(model_iter): if step >= nsteps: break`\n6. **map_coords** - Filter output variables if needed: `x_out, coords_out = map_coords(x, coords, output_coords)`\n7. **split_coords** - Prepare for IO write: `x_out, coords_out = split_coords(x_out, coords_out)`\n8. **io.write** - Write each step to backend\n\n### 9. Explain Next Steps\n\n- How to change forecast time or run multiple initializations\n- How to read output (`xr.open_zarr(...)`)\n- Point to diagnostic workflow for post-processing\n\n## Ownership\n\n**Owns:** Model selection, data source compatibility, IO backend selection,\nnsteps calculation, generating `earth2studio.run.deterministic` scripts.\n\n**Does not own:** Ensemble workflows, diagnostics, data-only fetch, installation,\nmodel training.\n\n## Troubleshooting\n\nSee `references\u002Ftroubleshooting.md` for common errors and solutions.\n\n## Reminders\n\n- **Always fetch live docs** before recommending models or data sources - APIs change between releases\n- **Verify lexicon compatibility** - Model input variables must exist in data source's VOCAB\n- **Use `load_default_package()`** - This is the standard pattern for loading model weights\n- **Time format is ISO 8601** - Use `\"YYYY-MM-DDTHH:MM:SS\"` format for the `time` argument\n- **Wind speed needs both components** - If 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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",[210,211],{"categoryNo":131,"categoryName":132,"taxonomyType":133,"taxonomyTypeDesc":134},{"categoryNo":136,"categoryName":137,"taxonomyType":133,"taxonomyTypeDesc":134},[213,140,214,200,215,149,216,217,218,196,219,25,220,221,222,33,223,201,224],"vue testing best practices","testing best practices","best practices","test","testing patterns","component","vitest","mocking","e2e","utils","patterns","e2e testing",{"skillNo":226,"name":227,"nameZh":6,"description":228,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":184,"licenseFileUrl":229,"sourceHtmlUrl":230,"categories":231,"keywords":233,"publishedAt":154},"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",[232],{"categoryNo":136,"categoryName":137,"taxonomyType":133,"taxonomyTypeDesc":134},[234,200,235,215,236,218,237,196,223,238,239],"router","vue router best practices","route","guards","navigation","lifecycle",{"skillNo":241,"name":242,"nameZh":6,"description":243,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":184,"licenseFileUrl":244,"sourceHtmlUrl":245,"categories":246,"keywords":248,"publishedAt":154},"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",[247],{"categoryNo":136,"categoryName":137,"taxonomyType":133,"taxonomyTypeDesc":134},[249,200,215,250,193,251,252,253,234,254,255,256,257,258,259,25,260,33,261,262],"vue best practices","Composition","files","tasks","api","pinia","work","options","ssr","typescript","as","vite","load","standard"]