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    "summary_en": "Query the CZ CELLxGENE Census programmatically for versioned public single-cell and spatial transcriptomics data. Use when you need population-scale cell metadata, gene expression slices, Census summary counts, source H5AD URIs/downloads, embeddings, spatial Census data, or reference atlas comparisons across organisms, tissues, diseases, assays, and cell types. For analyzing your own local single-cell data use scanpy, anndata, or scvi-tools.",
    "summary_zh": "以编程方式查询 CZ CELLxGENE Census，获取版本化的公共单细胞和空间转录组数据。当您需要跨物种、组织、疾病、检测方法和细胞类型的人群规模细胞元数据、基因表达切片、Census 汇总计数、源 H5AD URI/下载、嵌入、空间 Census 数据或参考图谱比较时使用。如需分析您自己的本地单细胞数据，请使用 scanpy、anndata 或 scvi-tools。",
    "description_en": "This Agent Skill provides procedural knowledge for querying the CZ CELLxGENE Census programmatically. It enables access to versioned public single-cell and spatial transcriptomics data, including population-scale cell metadata, gene expression slices, Census summary counts, source H5AD URIs/downloads, embeddings, spatial Census data, and reference atlas comparisons across organisms, tissues, diseases, assays, and cell types. The skill includes installation instructions, core workflow patterns, best practices, and troubleshooting guidance. It is designed for use with Python >=3.10,<3.13 and targets cellxgene-census 1.17.x and the 2025-11-08 stable LTS Census. No authentication is required for public Census data.",
    "description_zh": "该 Agent Skill 提供了以编程方式查询 CZ CELLxGENE Census 的程序性知识。它支持访问版本化的公共单细胞和空间转录组数据，包括跨物种、组织、疾病、检测方法和细胞类型的人群规模细胞元数据、基因表达切片、Census 汇总计数、源 H5AD URI/下载、嵌入、空间 Census 数据以及参考图谱比较。该技能包含安装说明、核心工作流模式、最佳实践和故障排除指南。它适用于 Python >=3.10,<3.13，并针对 cellxgene-census 1.17.x 和 2025-11-08 稳定 LTS Census。公共 Census 数据无需身份验证。",
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          "idempotency": "idempotent",
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          "input_contract": "natural_language",
          "output_contract": "natural_language",
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        "dependencies": [
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          "TileDB-SOMA >=1.15.5 (for spatial workflows)"
        ]
      },
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        "signal": "none",
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      "fit": {
        "good_for": [
          "Querying single-cell expression data by cell type, tissue, or disease",
          "Exploring available single-cell datasets and metadata",
          "Training machine learning models on single-cell data",
          "Performing large-scale cross-dataset analyses",
          "Integrating Census data with scanpy or other analysis frameworks",
          "Computing statistics across millions of cells",
          "Accessing pre-calculated embeddings or model predictions"
        ],
        "not_for": [
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      "unknowns": [
        "Actual runtime behavior and connectivity to the Census service have not been tested.",
        "Real-world usage and adoption metrics are not observed.",
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