[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M69AS2WAAD5CZ9S9CBSEZ226":3,"skill-same:01M69AS2WAAD5CZ9S9CBSEZ226":65},{"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":37,"linkOnly":38,"markdownContent":39,"markdownBody":40,"frontMatter":41,"refName":42,"commitSha":43,"skillDirectory":44,"forksCount":45,"repositoryHtmlUrl":46,"repositoryHomepageUrl":6,"ownerHtmlUrl":47,"ownerBlogUrl":48,"ownerDescription":49,"updatedAt":50,"install":51,"resources":64},"01M69AS2WAAD5CZ9S9CBSEZ226","synthetic-data",null,"Generate synthetic training data using NVIDIA Cosmos world foundation models for SDG pipelines","microsoft\u002Fphysical-ai-toolchain","microsoft","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6154722?v=4","01M698VYDXZ6JW5WH3CRV650R8","Microsoft","01M69ARCAV4WR03MSZSQPHZ2S8",137,"MIT","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fphysical-ai-toolchain\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fsynthetic-data\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],"synthetic data","data","synthetic","training","generate","NVIDIA","pipelines","models","foundation","world","cosmos","for","Training Data","2026-11-10 10:12:30",false,"---\nname: synthetic-data\ndescription: 'Generate synthetic training data using NVIDIA Cosmos world foundation models for SDG pipelines'\n---\n\n# Synthetic Data Skill\n\nGenerate photorealistic training data using NVIDIA Cosmos world foundation models — Cosmos Transfer, Cosmos Predict, and Cosmos Reason.\n\n## Overview\n\nThe Synthetic Data domain provides SDG pipelines that transform simulation-rendered frames into photorealistic training data, predict future environment states, and curate output for training quality.\n\n## Pipeline Stages\n\n| Stage    | Model               | Purpose                                            |\n|----------|---------------------|----------------------------------------------------|\n| Transfer | Cosmos Transfer 2.5 | Convert Isaac Sim renders to photorealistic images |\n| Predict  | Cosmos Predict 2.5  | Generate future frame sequences from observations  |\n| Reason   | Cosmos Reason 2     | Assess data quality and filter training samples    |\n\n## Workflow Submission\n\nSDG workflows can be submitted via OSMO or AzureML:\n\n- **OSMO workflows**: `synthetic-data\u002Fworkflows\u002Fosmo\u002F`\n- **AzureML jobs**: `synthetic-data\u002Fworkflows\u002Fazureml\u002F`\n\n## Key Files\n\n| File                                                                | Purpose                                 |\n|---------------------------------------------------------------------|-----------------------------------------|\n| `synthetic-data\u002FREADME.md`                                          | Domain overview and directory structure |\n| `synthetic-data\u002Fworkflows\u002Fosmo\u002Fsdg-pipeline.yaml`                   | End-to-end OSMO SDG pipeline            |\n| `synthetic-data\u002Fcosmos\u002Fconfigs\u002FREADME.md`                           | Model configuration reference           |\n| `synthetic-data\u002Fspecifications\u002Fsynthetic-data.specification.md`     | SDG pipeline specification              |\n| `synthetic-data\u002Fspecifications\u002Fcosmos-integration.specification.md` | Cosmos model integration specification  |\n\n## Environment Requirements\n\nAll NVIDIA Cosmos containers require:\n\n| Variable                     | Value |\n|------------------------------|-------|\n| `ACCEPT_EULA`                | `Y`   |\n| `PRIVACY_CONSENT`            | `Y`   |\n| `NVIDIA_DRIVER_CAPABILITIES` | `all` |\n\n## GPU Requirements\n\nEach Cosmos model stage requires a minimum of 1x A100 (40 GB) GPU. H100 (80 GB) recommended for production workloads.\n","# Synthetic Data Skill\n\nGenerate photorealistic training data using NVIDIA Cosmos world foundation models — Cosmos Transfer, Cosmos Predict, and Cosmos Reason.\n\n## Overview\n\nThe Synthetic Data domain provides SDG pipelines that transform simulation-rendered frames into photorealistic training data, predict future environment states, and curate output for training quality.\n\n## Pipeline Stages\n\n| Stage    | Model               | Purpose                                            |\n|----------|---------------------|----------------------------------------------------|\n| Transfer | Cosmos Transfer 2.5 | Convert Isaac Sim renders to photorealistic images |\n| Predict  | Cosmos Predict 2.5  | Generate future frame sequences from observations  |\n| Reason   | Cosmos Reason 2     | Assess data quality and filter training samples    |\n\n## Workflow Submission\n\nSDG workflows can be submitted via OSMO or AzureML:\n\n- **OSMO workflows**: `synthetic-data\u002Fworkflows\u002Fosmo\u002F`\n- **AzureML jobs**: `synthetic-data\u002Fworkflows\u002Fazureml\u002F`\n\n## Key Files\n\n| File                                                                | Purpose                                 |\n|---------------------------------------------------------------------|-----------------------------------------|\n| `synthetic-data\u002FREADME.md`                                          | Domain overview and directory structure |\n| `synthetic-data\u002Fworkflows\u002Fosmo\u002Fsdg-pipeline.yaml`                   | End-to-end OSMO SDG pipeline            |\n| `synthetic-data\u002Fcosmos\u002Fconfigs\u002FREADME.md`                           | Model configuration reference           |\n| `synthetic-data\u002Fspecifications\u002Fsynthetic-data.specification.md`     | SDG pipeline specification              |\n| `synthetic-data\u002Fspecifications\u002Fcosmos-integration.specification.md` | Cosmos model integration specification  |\n\n## Environment Requirements\n\nAll NVIDIA Cosmos containers require:\n\n| Variable                     | Value |\n|------------------------------|-------|\n| `ACCEPT_EULA`                | `Y`   |\n| `PRIVACY_CONSENT`            | `Y`   |\n| `NVIDIA_DRIVER_CAPABILITIES` | `all` |\n\n## GPU Requirements\n\nEach Cosmos model stage requires a minimum of 1x A100 (40 GB) GPU. H100 (80 GB) recommended for production workloads.\n",{"name":5,"description":7},"main","39b9db6f0c0cee5c51d548ee422fe41cd1e7ff03",".github\u002Fskills\u002Fsynthetic-data",71,"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fphysical-ai-toolchain","https:\u002F\u002Fgithub.com\u002Fmicrosoft","https:\u002F\u002Fopensource.microsoft.com","Open source projects and samples from Microsoft","2026-11-12 14:41:59",{"source":8,"skillName":5,"commands":52,"prompt":62,"zipAvailable":63},[53,56,59],{"tool":54,"command":55},"npx","npx skills add microsoft\u002Fphysical-ai-toolchain --skill synthetic-data",{"tool":57,"command":58},"bunx","bunx skills add microsoft\u002Fphysical-ai-toolchain --skill synthetic-data",{"tool":60,"command":61},"pnpm","pnpm dlx skills add microsoft\u002Fphysical-ai-toolchain --skill synthetic-data","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fphysical-ai-toolchain\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fsynthetic-data\u002FSKILL.md",true,[],[66,91,114,139,162,184],{"skillNo":67,"name":68,"nameZh":6,"description":69,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":70,"categories":71,"keywords":72,"publishedAt":37},"01M69AS0T2FF3R3YHFQZAF4X3F","osmo-lerobot-training","Submit, monitor, analyze, and evaluate LeRobot imitation learning training jobs on OSMO with Azure ML MLflow integration and inference evaluation - 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Use when: discovering deployed environment details; creating OSMO image manifests or platform values; preparing a HiL host; uploading or downloading deployment files through Azure Key Vault; or deploying with generated environment configuration.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fphysical-ai-toolchain\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fenvironment-deployment\u002FSKILL.md",[168,169],{"categoryNo":98,"categoryName":99,"taxonomyType":100,"taxonomyTypeDesc":101},{"categoryNo":147,"categoryName":148,"taxonomyType":100,"taxonomyTypeDesc":101},[171,154,172,74,75,156,173,174,175,176,177,178,179,180,86,181,28,182,183,113],"environment deployment","environment","platform","azure key vault","files","host","generated","use","manifests","when","vault","values","aks",{"skillNo":185,"name":186,"nameZh":6,"description":187,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":188,"categories":189,"keywords":194,"publishedAt":37},"01M69ARPMK4GHCD723YBTWYYJS","e2e-tests","Autonomously launch, monitor, retry, and report all Physical AI cloud E2E tests across independent Azure ML and OSMO GPU infrastructure.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fphysical-ai-toolchain\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fe2e-tests\u002FSKILL.md",[190,193],{"categoryNo":191,"categoryName":192,"taxonomyType":100,"taxonomyTypeDesc":101},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":98,"categoryName":99,"taxonomyType":100,"taxonomyTypeDesc":101},[195,196,197,74,198,75,199,200,201,85,202,203,204,86,108,205,206,89,207,208],"e2e tests","tests","e2e","cli","launch","linux","retry","azure cli","Bash","across","report","jq","kubectl","cloud"]