[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6PP8H2D1BSD4BSSRVRDPF64":3,"skill-same:01M6PP8H2D1BSD4BSSRVRDPF64":97},{"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":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":56,"commitSha":57,"skillDirectory":58,"forksCount":14,"repositoryHtmlUrl":59,"repositoryHomepageUrl":6,"ownerHtmlUrl":60,"ownerBlogUrl":61,"ownerDescription":6,"updatedAt":62,"install":63,"resources":76},"01M6PP8H2D1BSD4BSSRVRDPF64","earth2studio-discover",null,"Find Earth2Studio models, data sources, and examples for a weather\u002Fclimate use case. Do NOT use for writing inference code, downloading data, or installation.","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-discover\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"earth2studio discover","discover","models","data sources","for","climate","case","sources","installation","do","writing","use","find","team","discovery","NVIDIA","examples","code","weather","python","2026-11-10 10:12:30",false,"---\nname: earth2studio-discover\nversion: 0.16.0\nlicense: Apache-2.0\nmetadata:\n  author: NVIDIA Earth-2 Team\n  tags:\n    - earth2studio\n    - earth2\n    - python\n    - discovery\n    - models\n    - data-sources\ndescription: >\n  Find Earth2Studio models, data sources, and examples for a weather\u002Fclimate use\n  case. Do NOT use for writing inference code, downloading data, or installation.\n---\n\n# Earth2Studio Discoverability Skill\n\n## Purpose\n\nHelp users identify the right Earth2Studio models, data sources, and examples for\ntheir weather\u002Fclimate task. Use when: comparing models by GPU\u002FVRAM requirements,\nchoosing forecast class (nowcast, medium-range, seasonal), finding compatible\ndata sources via lexicons, or locating gallery examples for downscaling,\nensemble generation, or data assimilation.\n\n## Prerequisites\n\n- Internet access to fetch live documentation pages from nvidia.github.io\n- Familiarity with Earth2Studio badge system (Class, Region, VRAM, Release)\n\nYou are helping a user find the right Earth2Studio components for their use case. Your job is to understand what they want to do, then point them at the models, data sources, and examples that fit — verified against live documentation.\n\n## Core principle: discover from live docs, don't memorize\n\nEarth2Studio adds models, data sources, and examples every release. Model classes get new badges, new data sources appear, examples get reorganized. Any static list in this skill will rot.\n\n**Rules:**\n1. Always fetch the relevant live doc pages before recommending components.\n2. Use badge metadata (Region, Class, VRAM, Release) from the docs to filter candidates.\n3. Verify data-source ↔ model compatibility using the lexicon system (see Step 4).\n4. Cite doc URLs so the user can explore further.\n\n## Live doc references\n\nFetch these pages as needed (not all at once — only what the user's question requires):\n\n| Category | URL |\n|----------|-----|\n| Prognostic models | https:\u002F\u002Fnvidia.github.io\u002Fearth2studio\u002Fmodules\u002Fmodels_px.html |\n| Diagnostic models | https:\u002F\u002Fnvidia.github.io\u002Fearth2studio\u002Fmodules\u002Fmodels_dx.html |\n| Data assimilation | https:\u002F\u002Fnvidia.github.io\u002Fearth2studio\u002Fmodules\u002Fmodels_da.html |\n| Data sources (analysis) | https:\u002F\u002Fnvidia.github.io\u002Fearth2studio\u002Fmodules\u002Fdatasources_analysis.html |\n| Data sources (forecast) | https:\u002F\u002Fnvidia.github.io\u002Fearth2studio\u002Fmodules\u002Fdatasources_forecast.html |\n| Data sources (dataframe) | https:\u002F\u002Fnvidia.github.io\u002Fearth2studio\u002Fmodules\u002Fdatasources_dataframe.html |\n| Examples gallery | https:\u002F\u002Fnvidia.github.io\u002Fearth2studio\u002Fexamples\u002Findex.html |\n| Lexicon source | https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fearth2studio\u002Ftree\u002Fmain\u002Fearth2studio\u002Flexicon |\n\n## Interaction protocol\n\n### Step 1. Understand the user's problem\n\nExtract from what the user has said (ask follow-ups if needed, cap at 3 questions):\n\n- **Task type** — medium-range forecasting, nowcasting, downscaling\u002Fsuper-resolution, seasonal\u002Fsubseasonal, data assimilation, climate projection, ensemble generation, derived diagnostics\n- **Region** — global, North America, Europe, Asia, specific country\u002Farea\n- **Temporal scale** — hours ahead (nowcast), days ahead (medium-range), weeks\u002Fmonths (seasonal), climate\n- **Variables of interest** — temperature, precipitation, wind, pressure, radiation, specific levels, etc.\n- **Hardware constraints** — GPU type, available VRAM (40GB, 48GB, 80GB, 96GB)\n- **Deterministic vs. ensemble** — single forecast or probabilistic\n\nGood follow-up phrasing: *\"Are you looking for a single best-estimate forecast or an ensemble with uncertainty?\"* — not *\"what's your use case?\"*\n\n### Step 2. Fetch relevant model docs\n\nBased on the user's task type, fetch the appropriate model page(s):\n\n- Forecasting → prognostic models (px)\n- Post-processing \u002F downscaling \u002F derived variables → diagnostic models (dx)\n- Observation integration → data assimilation (da)\n- Often a workflow chains px → dx, so check both\n\nFrom the doc pages, extract for each candidate model:\n- **Class badge** — NWC, DS, MR, S2S, DA, CM\n- **Region badge** — Global, NA, EU, AS, etc.\n- **Rec VRAM badge** — minimum GPU memory\n- **Release year** — newer models generally supersede older ones in the same class\n\nFilter to models matching the user's task type, region, and hardware. Present a short-list (not the full catalog) with badge metadata.\n\n### Step 3. Fetch relevant data source docs\n\nBased on the user's data needs, fetch the appropriate data source page:\n\n- Historical reanalysis → analysis data sources\n- Real-time or operational → forecast data sources\n- Observations \u002F station data → dataframe data sources\n\nNote which data sources cover the user's region and variables.\n\n### Step 4. Verify compatibility via lexicon\n\nThis is the key technical step. Earth2Studio models declare their required input variables via `input_coords()`. Data sources expose available variables through their lexicon VOCAB. If a data source's lexicon VOCAB keys contain all variables in a model's `input_coords` (the \"variable\" dimension), they are compatible.\n\nTo verify:\n1. Check the model's doc page or source for its `input_coords` — specifically the variable list\n2. Check the data source's lexicon file at `earth2studio\u002Flexicon\u002F\u003Csource>.py` for its VOCAB keys\n3. Confirm the data source VOCAB covers all variables the model needs\n\nIf checking source code directly (e.g. user has a local clone), the lexicon files are at:\n```\nearth2studio\u002Flexicon\u002Fgfs.py\nearth2studio\u002Flexicon\u002Fhrrr.py\nearth2studio\u002Flexicon\u002Fcds.py\nearth2studio\u002Flexicon\u002Farco.py\nearth2studio\u002Flexicon\u002Fwb2.py\n... (one per data source)\n```\n\nEach defines a `VOCAB: dict[str, str | tuple]` mapping Earth2Studio variable names to source-specific identifiers.\n\nSurface compatibility results clearly: *\"GraphCastOperational needs [list of variables] — GFS and ERA5 (via ARCO\u002FCDS) both provide these, but HRRR does not cover pressure levels above X.\"*\n\n### Step 5. Suggest examples\n\nFetch the examples gallery and identify examples that demonstrate the user's workflow pattern. Examples are organized by category:\n\n- `01_getting_started` — basic deterministic, diagnostic, ensemble pipelines\n- `02_medium_range` — ensemble extension, perturbation, cyclone tracking\n- `03_downscaling` — CorrDiff, CBottle, ensemble downscaling\n- `04_nowcasting` — StormCast, StormScope\n- `05_data_assimilation` — StormCast SDA, HealDA\n- `06_seasonal` — DLESyM, statistical methods\n- `07_misc` — distributed inference, IO, custom data, generation\n- `08_extend` — building custom models, diagnostics, data sources\n\nPoint the user at the most relevant 1–3 examples as starting points. Explain what each demonstrates and how it relates to their problem.\n\n### Step 6. Return recommendations\n\nOutput structure (omit empty sections):\n\n```\n## Your use case\n[1-2 sentence restatement of what the user wants to do]\n\n## Recommended models\n| Model | Class | Region | VRAM | Why |\n|-------|-------|--------|------|-----|\n[Short-list with rationale per row]\n\n## Compatible data sources\n| Data Source | Coverage | Compatible with |\n|-------------|----------|-----------------|\n[Verified via lexicon]\n\n## Relevant examples\n- [Example name](link) — what it demonstrates\n\n## Next steps\n[What to install, what to read next]\n```\n\nKeep recommendations to 2–4 models maximum. If multiple options exist, explain the tradeoff (accuracy vs. speed, deterministic vs. ensemble, VRAM, etc.) rather than listing everything.\n\n## Limitations\n\n- Recommendations are only as current as the live docs; unreleased models are not discoverable.\n- Badge metadata may be incomplete for newly added models.\n- Lexicon compatibility checks require source code access for full accuracy; doc-only checks are approximate.\n\n## Troubleshooting\n\n| Error | Cause | Solution |\n|-------|-------|----------|\n| Model page returns 404 | URL changed after a release | Check https:\u002F\u002Fnvidia.github.io\u002Fearth2studio\u002F for updated navigation |\n| Lexicon file not found | Data source is new or renamed | Search `earth2studio\u002Flexicon\u002F` directory for current filenames |\n| Badge missing from model | Model docs not yet updated | Fall back to the model's source code `__init__` or README for specs |\n\n## Ownership and out-of-scope\n\n**Owns:** component discovery, model\u002Fdata-source compatibility checking, badge-based filtering, example recommendation, hardware-fit assessment.\n\n**Does not own:** installation (use earth2studio-install skill), writing inference code, model training, custom model development, runtime debugging, PhysicsNeMo model discovery.\n","# Earth2Studio Discoverability Skill\n\n## Purpose\n\nHelp users identify the right Earth2Studio models, data sources, and examples for\ntheir weather\u002Fclimate task. Use when: comparing models by GPU\u002FVRAM requirements,\nchoosing forecast class (nowcast, medium-range, seasonal), finding compatible\ndata sources via lexicons, or locating gallery examples for downscaling,\nensemble generation, or data assimilation.\n\n## Prerequisites\n\n- Internet access to fetch live documentation pages from nvidia.github.io\n- Familiarity with Earth2Studio badge system (Class, Region, VRAM, Release)\n\nYou are helping a user find the right Earth2Studio components for their use case. Your job is to understand what they want to do, then point them at the models, data sources, and examples that fit — verified against live documentation.\n\n## Core principle: discover from live docs, don't memorize\n\nEarth2Studio adds models, data sources, and examples every release. Model classes get new badges, new data sources appear, examples get reorganized. Any static list in this skill will rot.\n\n**Rules:**\n1. Always fetch the relevant live doc pages before recommending components.\n2. Use badge metadata (Region, Class, VRAM, Release) from the docs to filter candidates.\n3. Verify data-source ↔ model compatibility using the lexicon system (see Step 4).\n4. Cite doc URLs so the user can explore further.\n\n## Live doc references\n\nFetch these pages as needed (not all at once — only what the user's question requires):\n\n| Category | URL |\n|----------|-----|\n| Prognostic models | https:\u002F\u002Fnvidia.github.io\u002Fearth2studio\u002Fmodules\u002Fmodels_px.html |\n| Diagnostic models | https:\u002F\u002Fnvidia.github.io\u002Fearth2studio\u002Fmodules\u002Fmodels_dx.html |\n| Data assimilation | https:\u002F\u002Fnvidia.github.io\u002Fearth2studio\u002Fmodules\u002Fmodels_da.html |\n| Data sources (analysis) | https:\u002F\u002Fnvidia.github.io\u002Fearth2studio\u002Fmodules\u002Fdatasources_analysis.html |\n| Data sources (forecast) | https:\u002F\u002Fnvidia.github.io\u002Fearth2studio\u002Fmodules\u002Fdatasources_forecast.html |\n| Data sources (dataframe) | https:\u002F\u002Fnvidia.github.io\u002Fearth2studio\u002Fmodules\u002Fdatasources_dataframe.html |\n| Examples gallery | https:\u002F\u002Fnvidia.github.io\u002Fearth2studio\u002Fexamples\u002Findex.html |\n| Lexicon source | https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fearth2studio\u002Ftree\u002Fmain\u002Fearth2studio\u002Flexicon |\n\n## Interaction protocol\n\n### Step 1. Understand the user's problem\n\nExtract from what the user has said (ask follow-ups if needed, cap at 3 questions):\n\n- **Task type** — medium-range forecasting, nowcasting, downscaling\u002Fsuper-resolution, seasonal\u002Fsubseasonal, data assimilation, climate projection, ensemble generation, derived diagnostics\n- **Region** — global, North America, Europe, Asia, specific country\u002Farea\n- **Temporal scale** — hours ahead (nowcast), days ahead (medium-range), weeks\u002Fmonths (seasonal), climate\n- **Variables of interest** — temperature, precipitation, wind, pressure, radiation, specific levels, etc.\n- **Hardware constraints** — GPU type, available VRAM (40GB, 48GB, 80GB, 96GB)\n- **Deterministic vs. ensemble** — single forecast or probabilistic\n\nGood follow-up phrasing: *\"Are you looking for a single best-estimate forecast or an ensemble with uncertainty?\"* — not *\"what's your use case?\"*\n\n### Step 2. Fetch relevant model docs\n\nBased on the user's task type, fetch the appropriate model page(s):\n\n- Forecasting → prognostic models (px)\n- Post-processing \u002F downscaling \u002F derived variables → diagnostic models (dx)\n- Observation integration → data assimilation (da)\n- Often a workflow chains px → dx, so check both\n\nFrom the doc pages, extract for each candidate model:\n- **Class badge** — NWC, DS, MR, S2S, DA, CM\n- **Region badge** — Global, NA, EU, AS, etc.\n- **Rec VRAM badge** — minimum GPU memory\n- **Release year** — newer models generally supersede older ones in the same class\n\nFilter to models matching the user's task type, region, and hardware. Present a short-list (not the full catalog) with badge metadata.\n\n### Step 3. Fetch relevant data source docs\n\nBased on the user's data needs, fetch the appropriate data source page:\n\n- Historical reanalysis → analysis data sources\n- Real-time or operational → forecast data sources\n- Observations \u002F station data → dataframe data sources\n\nNote which data sources cover the user's region and variables.\n\n### Step 4. Verify compatibility via lexicon\n\nThis is the key technical step. Earth2Studio models declare their required input variables via `input_coords()`. Data sources expose available variables through their lexicon VOCAB. If a data source's lexicon VOCAB keys contain all variables in a model's `input_coords` (the \"variable\" dimension), they are compatible.\n\nTo verify:\n1. Check the model's doc page or source for its `input_coords` — specifically the variable list\n2. Check the data source's lexicon file at `earth2studio\u002Flexicon\u002F\u003Csource>.py` for its VOCAB keys\n3. Confirm the data source VOCAB covers all variables the model needs\n\nIf checking source code directly (e.g. user has a local clone), the lexicon files are at:\n```\nearth2studio\u002Flexicon\u002Fgfs.py\nearth2studio\u002Flexicon\u002Fhrrr.py\nearth2studio\u002Flexicon\u002Fcds.py\nearth2studio\u002Flexicon\u002Farco.py\nearth2studio\u002Flexicon\u002Fwb2.py\n... (one per data source)\n```\n\nEach defines a `VOCAB: dict[str, str | tuple]` mapping Earth2Studio variable names to source-specific identifiers.\n\nSurface compatibility results clearly: *\"GraphCastOperational needs [list of variables] — GFS and ERA5 (via ARCO\u002FCDS) both provide these, but HRRR does not cover pressure levels above X.\"*\n\n### Step 5. Suggest examples\n\nFetch the examples gallery and identify examples that demonstrate the user's workflow pattern. Examples are organized by category:\n\n- `01_getting_started` — basic deterministic, diagnostic, ensemble pipelines\n- `02_medium_range` — ensemble extension, perturbation, cyclone tracking\n- `03_downscaling` — CorrDiff, CBottle, ensemble downscaling\n- `04_nowcasting` — StormCast, StormScope\n- `05_data_assimilation` — StormCast SDA, HealDA\n- `06_seasonal` — DLESyM, statistical methods\n- `07_misc` — distributed inference, IO, custom data, generation\n- `08_extend` — building custom models, diagnostics, data sources\n\nPoint the user at the most relevant 1–3 examples as starting points. Explain what each demonstrates and how it relates to their problem.\n\n### Step 6. Return recommendations\n\nOutput structure (omit empty sections):\n\n```\n## Your use case\n[1-2 sentence restatement of what the user wants to do]\n\n## Recommended models\n| Model | Class | Region | VRAM | Why |\n|-------|-------|--------|------|-----|\n[Short-list with rationale per row]\n\n## Compatible data sources\n| Data Source | Coverage | Compatible with |\n|-------------|----------|-----------------|\n[Verified via lexicon]\n\n## Relevant examples\n- [Example name](link) — what it demonstrates\n\n## Next steps\n[What to install, what to read next]\n```\n\nKeep recommendations to 2–4 models maximum. If multiple options exist, explain the tradeoff (accuracy vs. speed, deterministic vs. ensemble, VRAM, etc.) rather than listing everything.\n\n## Limitations\n\n- Recommendations are only as current as the live docs; unreleased models are not discoverable.\n- Badge metadata may be incomplete for newly added models.\n- Lexicon compatibility checks require source code access for full accuracy; doc-only checks are approximate.\n\n## Troubleshooting\n\n| Error | Cause | Solution |\n|-------|-------|----------|\n| Model page returns 404 | URL changed after a release | Check https:\u002F\u002Fnvidia.github.io\u002Fearth2studio\u002F for updated navigation |\n| Lexicon file not found | Data source is new or renamed | Search `earth2studio\u002Flexicon\u002F` directory for current filenames |\n| Badge missing from model | Model docs not yet updated | Fall back to the model's source code `__init__` or README for specs |\n\n## Ownership and out-of-scope\n\n**Owns:** component discovery, model\u002Fdata-source compatibility checking, badge-based filtering, example recommendation, hardware-fit assessment.\n\n**Does not own:** installation (use earth2studio-install skill), writing inference code, model training, custom model development, runtime debugging, PhysicsNeMo model discovery.\n",{"name":5,"license":15,"version":49,"metadata":50,"description":7},"0.16.0",{"tags":51,"author":55},[52,53,43,38,26,54],"earth2studio","earth2","data-sources","NVIDIA Earth-2 Team","main","7a940b8e5af16c95e5c190a524421d32fc7445a9",".claude\u002Fskills\u002Fearth2studio-discover","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui","https:\u002F\u002Fneuroads.com.br","2026-08-12 00:42:44",{"source":8,"skillName":5,"commands":64,"prompt":74,"zipAvailable":75},[65,68,71],{"tool":66,"command":67},"npx","npx skills add headmktlucca-ui\u002Fneuroads-lp --skill earth2studio-discover",{"tool":69,"command":70},"bunx","bunx skills add headmktlucca-ui\u002Fneuroads-lp --skill earth2studio-discover",{"tool":72,"command":73},"pnpm","pnpm dlx skills add headmktlucca-ui\u002Fneuroads-lp --skill earth2studio-discover","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fearth2studio-discover\u002FSKILL.md",true,[77,83,87,91],{"path":78,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":81,"sourceHtmlUrl":82},"BENCHMARK.md","REFERENCE","参考文档",3988,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fearth2studio-discover\u002FBENCHMARK.md",{"path":84,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":85,"sourceHtmlUrl":86},"evals\u002Fevals.json",4918,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fearth2studio-discover\u002Fevals\u002Fevals.json",{"path":88,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":89,"sourceHtmlUrl":90},"skill-card.md",4093,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fearth2studio-discover\u002Fskill-card.md",{"path":92,"resourceType":93,"resourceTypeDesc":94,"sizeBytes":95,"sourceHtmlUrl":96},"skill.oms.sig","OTHER","其他",4597,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fearth2studio-discover\u002Fskill.oms.sig",[98,124,148,174,192,224],{"skillNo":99,"name":100,"nameZh":6,"description":101,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":102,"licenseFileUrl":6,"sourceHtmlUrl":103,"categories":104,"keywords":113,"publishedAt":44},"01M6PTDCZTXTGSTX4RFK901YSZ","xunit-skill","Generates xUnit.net tests in C#. Covers Fact\u002FTheory, constructor injection, IClassFixture, and FluentAssertions. Use when user mentions \"xUnit\", \"[Fact]\", \"[Theory]\", \"Assert.Equal\", \"C# xUnit\". Triggers on: \"xUnit\", \"[Fact]\", \"[Theory]\", \"Assert.Equal C#\", \"xUnit.net\".","MIT","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fxunit-skill\u002FSKILL.md",[105,110],{"categoryNo":106,"categoryName":107,"taxonomyType":108,"taxonomyTypeDesc":109},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",{"categoryNo":111,"categoryName":112,"taxonomyType":108,"taxonomyTypeDesc":109},"01JBSEEDCAT000000000000023","Skill与智能体开发",[114,115,116,117,118,35,119,120,121,122,123],"xunit","xunit skill","testing","injection","user","tests","triggers","unit","unit-testing","C#",{"skillNo":125,"name":126,"nameZh":6,"description":127,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":102,"licenseFileUrl":6,"sourceHtmlUrl":128,"categories":129,"keywords":132,"publishedAt":44},"01M6PTCB59ZTG5FTMWGPYEMMBP","xcuitest-skill","Generates XCUITest UI tests for iOS\u002FiPadOS apps in Swift. Apple's native testing framework for reliable, fast UI automation. Supports local simulators and TestMu AI cloud real devices. Use when user mentions \"XCUITest\", \"XCTest\", \"iOS UI test\", \"Swift test\", \"XCUIApplication\". Triggers on: \"XCUITest\", \"XCTest UI\", \"iOS UI test\", \"Swift UI test\", \"XCUIApplication\", \"TestMu\".","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fxcuitest-skill\u002FSKILL.md",[130,131],{"categoryNo":106,"categoryName":107,"taxonomyType":108,"taxonomyTypeDesc":109},{"categoryNo":111,"categoryName":112,"taxonomyType":108,"taxonomyTypeDesc":109},[133,134,116,135,136,137,138,139,35,140,141,142,143,144,119,120,145,146,28,147],"xcuitest","xcuitest skill","automation","test","testing framework","ios","devices","mobile","mobile-testing","ui test","local","apple","apps","native","objective",{"skillNo":149,"name":150,"nameZh":6,"description":151,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":102,"licenseFileUrl":6,"sourceHtmlUrl":152,"categories":153,"keywords":158,"publishedAt":44},"01M6PTATMNTY9NF4J7KSR76YK6","world-builder","Create and manage generative game zones for Dorothy's Pokemon-style world. 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",[154,157],{"categoryNo":155,"categoryName":156,"taxonomyType":108,"taxonomyTypeDesc":109},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":111,"categoryName":112,"taxonomyType":108,"taxonomyTypeDesc":109},[159,160,161,162,163,164,165,166,35,167,168,169,170,171,172,28,173],"builder","world","world builder","MCP","create","mcp tools","Tools","game","pokemon","manage","style","via","theme","zone","generative",{"skillNo":175,"name":176,"nameZh":6,"description":177,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":102,"licenseFileUrl":6,"sourceHtmlUrl":178,"categories":179,"keywords":182,"publishedAt":44},"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",[180,181],{"categoryNo":106,"categoryName":107,"taxonomyType":108,"taxonomyTypeDesc":109},{"categoryNo":111,"categoryName":112,"taxonomyType":108,"taxonomyTypeDesc":109},[183,116,184,135,185,186,187,188,118,35,189,143,119,120,190,191],"webdriverio skill","typescript","javascript","browser","cloud","e2e","e2e-testing","URL","conf",{"skillNo":193,"name":194,"nameZh":6,"description":195,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":102,"licenseFileUrl":6,"sourceHtmlUrl":196,"categories":197,"keywords":205,"publishedAt":44},"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",[198,201,202],{"categoryNo":199,"categoryName":200,"taxonomyType":108,"taxonomyTypeDesc":109},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":155,"categoryName":156,"taxonomyType":108,"taxonomyTypeDesc":109},{"categoryNo":203,"categoryName":204,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000032","设计师",[206,207,208,209,210,211,212,213,214,215,41,216,217,218,219,220,221,222,120,223],"design","web","guidelines","web design guidelines","web design","css","performance","components","web components","tasks","web performance","ui","platform design","html","responsive design","responsive","platform","implementing",{"skillNo":225,"name":226,"nameZh":6,"description":227,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":102,"licenseFileUrl":6,"sourceHtmlUrl":228,"categories":229,"keywords":232,"publishedAt":44},"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",[230,231],{"categoryNo":155,"categoryName":156,"taxonomyType":108,"taxonomyTypeDesc":109},{"categoryNo":203,"categoryName":204,"taxonomyType":21,"taxonomyTypeDesc":22},[206,208,233,215,217,35,234,235,144,222,236,120,145,237,28,238,239],"watchos design guidelines","workout","human","watch","interface","platform-design","features"]