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Do NOT use for ensemble, diagnostics, data-only fetch, or install.","headmktlucca-ui\u002Fneuroads-lp","headmktlucca-ui","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F247379838?v=4","01M6PDVAV6M1W96D1SR1PDF8CS","NeuroAds","01M6PDVAZ4P281GNDKD45CYTF5",0,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\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 10:12:30",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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Use this skill when creating, updating, or designing game worlds via MCP tools.","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fworld-builder\u002FSKILL.md",[179,182],{"categoryNo":180,"categoryName":181,"taxonomyType":133,"taxonomyTypeDesc":134},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":136,"categoryName":137,"taxonomyType":133,"taxonomyTypeDesc":134},[184,185,186,187,188,189,190,191,33,192,193,194,195,196,197,25,198],"builder","world","world builder","MCP","create","mcp tools","Tools","game","pokemon","manage","style","via","theme","zone","generative",{"skillNo":200,"name":201,"nameZh":6,"description":202,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":127,"licenseFileUrl":6,"sourceHtmlUrl":203,"categories":204,"keywords":207,"publishedAt":39},"01M6PT9Z92JTSQEHE1QR0NNW5F","webdriverio-skill","Generates WebdriverIO (WDIO) automation tests in JavaScript or TypeScript. Supports local and TestMu AI cloud. Use when user mentions \"WebdriverIO\", \"WDIO\", \"wdio.conf\", \"browser.url\", \"$\", \"$$\". Triggers on: \"WebdriverIO\", \"WDIO\", \"wdio\", \"browser.$\".","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fwebdriverio-skill\u002FSKILL.md",[205,206],{"categoryNo":131,"categoryName":132,"taxonomyType":133,"taxonomyTypeDesc":134},{"categoryNo":136,"categoryName":137,"taxonomyType":133,"taxonomyTypeDesc":134},[208,141,209,160,210,211,212,213,143,33,214,168,144,145,215,216],"webdriverio skill","typescript","javascript","browser","cloud","e2e","e2e-testing","URL","conf",{"skillNo":218,"name":219,"nameZh":6,"description":220,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":127,"licenseFileUrl":6,"sourceHtmlUrl":221,"categories":222,"keywords":232,"publishedAt":39},"01M6PT9NVK36ZWCQ69YXK3FR4K","web-design-guidelines","Web platform design and accessibility guidelines. Use when building web interfaces, auditing accessibility, implementing responsive layouts, or reviewing web UI code. Triggers on tasks involving HTML, CSS, web components, WCAG compliance, responsive design, or web performance.","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fweb\u002FSKILL.md",[223,226,227],{"categoryNo":224,"categoryName":225,"taxonomyType":133,"taxonomyTypeDesc":134},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":180,"categoryName":181,"taxonomyType":133,"taxonomyTypeDesc":134},{"categoryNo":228,"categoryName":229,"taxonomyType":230,"taxonomyTypeDesc":231},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,145,251],"design","web","guidelines","web design guidelines","web design","css","performance","components","web components","tasks","code","web performance","ui","platform design","html","responsive design","responsive","platform","implementing",{"skillNo":253,"name":254,"nameZh":6,"description":255,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":127,"licenseFileUrl":6,"sourceHtmlUrl":256,"categories":257,"keywords":260,"publishedAt":39},"01M6PT9A3JKRE3X34276F2FW5R","watchos-design-guidelines","Apple Human Interface Guidelines for Apple Watch. Use when building watchOS apps, complications, or workout features. Triggers on tasks involving Watch UI, Digital Crown, glanceable interfaces, or wrist-based interactions.","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fwatchos\u002FSKILL.md",[258,259],{"categoryNo":180,"categoryName":181,"taxonomyType":133,"taxonomyTypeDesc":134},{"categoryNo":228,"categoryName":229,"taxonomyType":230,"taxonomyTypeDesc":231},[233,235,261,242,245,33,262,263,169,250,264,145,170,265,25,266,267],"watchos design guidelines","workout","human","watch","interface","platform-design","features"]