[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71B304M3FCS15G8EXCM2P94":3,"skill-same:01M71B304M3FCS15G8EXCM2P94":78},{"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":16,"sourceHtmlUrl":17,"categories":18,"keywords":19,"publishedAt":34,"linkOnly":35,"markdownContent":36,"markdownBody":37,"frontMatter":38,"refName":39,"commitSha":40,"skillDirectory":41,"forksCount":42,"repositoryHtmlUrl":43,"repositoryHomepageUrl":6,"ownerHtmlUrl":44,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":45,"install":46,"resources":59},"01M71B304M3FCS15G8EXCM2P94","anndata-io",null,"Read 10x data with Scanpy and persist selected AnnData metadata changes when working with backed H5AD files.","strmt7\u002Fai_skills_collection_benchmarked","strmt7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F28989754?v=4","01M71929P914SWKQ7QJV24NBBY","Stratos","01M71929Q9VGKQ7PWYR95VSQQQ",1,"MIT","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Frepaired\u002Fskills\u002Fby-category\u002Fscience-research-data-analysis\u002Flatest-release-community\u002Fanndata-io\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Frepaired\u002Fskills\u002Fby-category\u002Fscience-research-data-analysis\u002Flatest-release-community\u002Fanndata-io\u002FSKILL.md",[],[20,21,22,23,24,25,26,27,28,29,30,31,32,33],"anndata","io","anndata io","metadata","data","scanpy","10x","read","files","h5ad","with","persist","changes","when","2026-11-10 10:12:30",false,"---\nname: anndata-io\ndescription: Read 10x data with Scanpy and persist selected AnnData metadata changes when working with backed H5AD files.\nlicense: MIT\n---\n\n# AnnData I\u002FO: 10x readers and backed metadata\n\nFor every task, apply Caveman. Before broad repository navigation, use\nCocoIndex Code; for web-page research, use Crawl4AI. Read and follow the\nrepository-root `docs\u002Fagent-tools.md` and its linked skills, and retain the\nrequired execution evidence.\n\nThis maintained, narrowly scoped overlay corrects selected I\u002FO examples in the\npinned AnnData skill. It does not replace or validate the rest of that skill.\nThe original entrypoint and I\u002FO reference are retained in `provenance\u002F`.\n\n## Read 10x data\n\nInstall Scanpy in the user's chosen environment when these readers are needed.\nFor a 10x HDF5 file:\n\n```python\nimport scanpy as sc\n\ncounts_h5 = sc.read_10x_h5(h5_path)\n```\n\nFor a 10x Matrix Market directory:\n\n```python\nimport scanpy as sc\n\ncounts_mtx = sc.read_10x_mtx(mtx_dir)\n```\n\nInspect observation and feature\nnames, feature types and matrix dimensions before downstream analysis. The\nreaders' defaults filter to gene expression; pass the documented arguments if\nother feature types are needed. These are Scanpy readers, not AnnData readers.\n\n## Save changes to observation metadata\n\nAnnData backed mode does not automatically persist changes to `obs`. Select a\nsubset that fits in memory, materialize it, close the backed file, and write to a\nnew output path. The selection and label below are supplied by the caller:\n\n```python\nimport anndata as ad\n\nbacked = ad.io.read_h5ad(input_path, backed=\"r\")\ntry:\n    subset = backed[obs_selection, :].to_memory()\nfinally:\n    backed.file.close()\nsubset.obs[\"sample\"] = sample_label\nsubset.write_h5ad(output_path)\n```\n\nChoose a distinct output file and verify the result after reopening it. The\nselection determines which observations are written; this example does not\nwrite the complete original dataset unless the selection includes all rows.\nMaterializing the selection requires enough memory for its data and annotations.\nFor larger-than-memory changes, choose and validate an appropriate workflow for\nthe specific data rather than assuming this example will fit.\n\nSee the [AnnData backed reader documentation](https:\u002F\u002Fanndata.readthedocs.io\u002Fen\u002Fstable\u002Fgenerated\u002Fanndata.io.read_h5ad.html),\n[Scanpy HDF5 reader](https:\u002F\u002Fscanpy.readthedocs.io\u002Fen\u002Fstable\u002Fgenerated\u002Fscanpy.read_10x_h5.html)\nand [Scanpy Matrix Market reader](https:\u002F\u002Fscanpy.readthedocs.io\u002Fen\u002Fstable\u002Fgenerated\u002Fscanpy.read_10x_mtx.html).\nThe recorded component control uses synthetic data; it is not an agent benchmark.\n","# AnnData I\u002FO: 10x readers and backed metadata\n\nFor every task, apply Caveman. Before broad repository navigation, use\nCocoIndex Code; for web-page research, use Crawl4AI. Read and follow the\nrepository-root `docs\u002Fagent-tools.md` and its linked skills, and retain the\nrequired execution evidence.\n\nThis maintained, narrowly scoped overlay corrects selected I\u002FO examples in the\npinned AnnData skill. It does not replace or validate the rest of that skill.\nThe original entrypoint and I\u002FO reference are retained in `provenance\u002F`.\n\n## Read 10x data\n\nInstall Scanpy in the user's chosen environment when these readers are needed.\nFor a 10x HDF5 file:\n\n```python\nimport scanpy as sc\n\ncounts_h5 = sc.read_10x_h5(h5_path)\n```\n\nFor a 10x Matrix Market directory:\n\n```python\nimport scanpy as sc\n\ncounts_mtx = sc.read_10x_mtx(mtx_dir)\n```\n\nInspect observation and feature\nnames, feature types and matrix dimensions before downstream analysis. The\nreaders' defaults filter to gene expression; pass the documented arguments if\nother feature types are needed. These are Scanpy readers, not AnnData readers.\n\n## Save changes to observation metadata\n\nAnnData backed mode does not automatically persist changes to `obs`. Select a\nsubset that fits in memory, materialize it, close the backed file, and write to a\nnew output path. The selection and label below are supplied by the caller:\n\n```python\nimport anndata as ad\n\nbacked = ad.io.read_h5ad(input_path, backed=\"r\")\ntry:\n    subset = backed[obs_selection, :].to_memory()\nfinally:\n    backed.file.close()\nsubset.obs[\"sample\"] = sample_label\nsubset.write_h5ad(output_path)\n```\n\nChoose a distinct output file and verify the result after reopening it. The\nselection determines which observations are written; this example does not\nwrite the complete original dataset unless the selection includes all rows.\nMaterializing the selection requires enough memory for its data and annotations.\nFor larger-than-memory changes, choose and validate an appropriate workflow for\nthe specific data rather than assuming this example will fit.\n\nSee the [AnnData backed reader documentation](https:\u002F\u002Fanndata.readthedocs.io\u002Fen\u002Fstable\u002Fgenerated\u002Fanndata.io.read_h5ad.html),\n[Scanpy HDF5 reader](https:\u002F\u002Fscanpy.readthedocs.io\u002Fen\u002Fstable\u002Fgenerated\u002Fscanpy.read_10x_h5.html)\nand [Scanpy Matrix Market reader](https:\u002F\u002Fscanpy.readthedocs.io\u002Fen\u002Fstable\u002Fgenerated\u002Fscanpy.read_10x_mtx.html).\nThe recorded component control uses synthetic data; it is not an agent benchmark.\n",{"name":5,"license":15,"description":7},"main","5c95bcac4646c5aed06bd9aed5235cfc7985aafc","included\u002Frepaired\u002Fskills\u002Fby-category\u002Fscience-research-data-analysis\u002Flatest-release-community\u002Fanndata-io",0,"https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked","https:\u002F\u002Fgithub.com\u002Fstrmt7","2026-11-11 04:18:12",{"source":8,"skillName":5,"commands":47,"prompt":57,"zipAvailable":58},[48,51,54],{"tool":49,"command":50},"npx","npx skills add strmt7\u002Fai_skills_collection_benchmarked --skill anndata-io",{"tool":52,"command":53},"bunx","bunx skills add strmt7\u002Fai_skills_collection_benchmarked --skill anndata-io",{"tool":55,"command":56},"pnpm","pnpm dlx skills add strmt7\u002Fai_skills_collection_benchmarked --skill anndata-io","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Frepaired\u002Fskills\u002Fby-category\u002Fscience-research-data-analysis\u002Flatest-release-community\u002Fanndata-io\u002FSKILL.md",true,[60,64,70,74],{"path":61,"resourceType":61,"resourceTypeDesc":62,"sizeBytes":63,"sourceHtmlUrl":16},"LICENSE","许可证",1068,{"path":65,"resourceType":66,"resourceTypeDesc":67,"sizeBytes":68,"sourceHtmlUrl":69},"provenance\u002Foriginal.io_operations.md","REFERENCE","参考文档",9456,"https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Frepaired\u002Fskills\u002Fby-category\u002Fscience-research-data-analysis\u002Flatest-release-community\u002Fanndata-io\u002Fprovenance\u002Foriginal.io_operations.md",{"path":71,"resourceType":66,"resourceTypeDesc":67,"sizeBytes":72,"sourceHtmlUrl":73},"provenance\u002Foriginal.SKILL.md",10213,"https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Frepaired\u002Fskills\u002Fby-category\u002Fscience-research-data-analysis\u002Flatest-release-community\u002Fanndata-io\u002Fprovenance\u002Foriginal.SKILL.md",{"path":75,"resourceType":66,"resourceTypeDesc":67,"sizeBytes":76,"sourceHtmlUrl":77},"repair.json",1481,"https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Frepaired\u002Fskills\u002Fby-category\u002Fscience-research-data-analysis\u002Flatest-release-community\u002Fanndata-io\u002Frepair.json",[79,112,141,165,191,219],{"skillNo":80,"name":81,"nameZh":6,"description":82,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":83,"licenseFileUrl":84,"sourceHtmlUrl":85,"categories":86,"keywords":95,"publishedAt":34},"01M71D4H3T6M57DFKN8HENAWRV","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Ftesting-qa-benchmarking\u002Fofficial-reference\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Ftesting-qa-benchmarking\u002Fofficial-reference\u002Fwebapp-testing\u002FSKILL.md",[87,92],{"categoryNo":88,"categoryName":89,"taxonomyType":90,"taxonomyTypeDesc":91},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",{"categoryNo":93,"categoryName":94,"taxonomyType":90,"taxonomyTypeDesc":91},"01JBSEEDCAT000000000000009","前端开发",[96,97,98,99,100,101,102,103,104,105,106,30,107,108,109,110,111],"webapp","testing","webapp testing","for","applications","local","frontend","debugging","toolkit","logs","browser","playwright","web","behavior","ui","screenshots",{"skillNo":113,"name":114,"nameZh":6,"description":115,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":83,"licenseFileUrl":116,"sourceHtmlUrl":117,"categories":118,"keywords":122,"publishedAt":34},"01M71D4CQR2A2TB7GASCR5ZBG3","skill-creator","Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Ftesting-qa-benchmarking\u002Fofficial-reference\u002Fskill-creator-anthropics\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Ftesting-qa-benchmarking\u002Fofficial-reference\u002Fskill-creator-anthropics\u002FSKILL.md",[119],{"categoryNo":120,"categoryName":121,"taxonomyType":90,"taxonomyTypeDesc":91},"01JBSEEDCAT000000000000023","Skill与智能体开发",[123,124,125,126,127,128,33,129,130,131,132,133,134,135,136,137,138,139,99,140],"skill creator","creator","performance","improve","run evals","new","analysis","create","optimize","variance analysis","run","edit","benchmark","a skill","existing skill","evals","modify","accuracy",{"skillNo":142,"name":143,"nameZh":6,"description":144,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":145,"categories":146,"keywords":149,"publishedAt":34},"01M71D488XHEZVXXSXFYP8ENMH","playwright-skill","Complete browser automation with Playwright. Auto-detects dev servers, writes reusable test scripts, and supports screenshots, responsive checks, UX validation, login flows, link checks, and arbitrary browser automation. Use when the user wants to test a website, automate browser interactions, validate web functionality, or perform browser-based testing.","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Ftesting-qa-benchmarking\u002Flatest-release-browser-automation\u002Fplaywright-skill\u002FSKILL.md",[147,148],{"categoryNo":88,"categoryName":89,"taxonomyType":90,"taxonomyTypeDesc":91},{"categoryNo":120,"categoryName":121,"taxonomyType":90,"taxonomyTypeDesc":91},[107,150,151,152,97,153,154,33,155,106,156,157,158,159,27,160,161,162,163,164],"playwright skill","browser automation","flows","auto","access","Bash","website","ux","login","scripts","write","node","install","network","responsive",{"skillNo":166,"name":167,"nameZh":6,"description":168,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":83,"licenseFileUrl":169,"sourceHtmlUrl":170,"categories":171,"keywords":173,"publishedAt":34},"01M71CY25GM939M6RES91QSG3T","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Fsearch-retrieval-web-automation\u002Fofficial-reference\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Fsearch-retrieval-web-automation\u002Fofficial-reference\u002Fweb-artifacts-builder\u002FSKILL.md",[172],{"categoryNo":93,"categoryName":94,"taxonomyType":90,"taxonomyTypeDesc":91},[174,175,176,108,177,178,179,99,180,181,102,182,183,184,185,186,187,188,189,190],"builder","artifacts builder","artifacts","web artifacts builder","single-file","css","Tailwind","ui components","single","use","file","components","react","multi","state","routing","state management",{"skillNo":192,"name":193,"nameZh":6,"description":194,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":83,"licenseFileUrl":195,"sourceHtmlUrl":196,"categories":197,"keywords":207,"publishedAt":34},"01M71CXZB60QJJ1H4S3NN94WMZ","frontend-design","Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML\u002FCSS layouts, or when styling\u002Fbeautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Fsearch-retrieval-web-automation\u002Fofficial-reference\u002Ffrontend-design\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Fsearch-retrieval-web-automation\u002Fofficial-reference\u002Ffrontend-design\u002FSKILL.md",[198,199,202],{"categoryNo":93,"categoryName":94,"taxonomyType":90,"taxonomyTypeDesc":91},{"categoryNo":200,"categoryName":201,"taxonomyType":90,"taxonomyTypeDesc":91},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":203,"categoryName":204,"taxonomyType":205,"taxonomyTypeDesc":206},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[102,208,209,179,210,211,212,100,213,214,183,33,176,215,185,216,186,217,218,30],"design","frontend design","ui design","generic","aesthetics","landing","pages","code","web ui","react components","examples",{"skillNo":220,"name":221,"nameZh":6,"description":222,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":223,"categories":224,"keywords":225,"publishedAt":34},"01M71CVZ54WFBTVA7R16PFY1ZE","zarr-python","Chunked N-D arrays for cloud storage. Compressed arrays, parallel I\u002FO, S3\u002FGCS integration, NumPy\u002FDask\u002FXarray compatible, for large-scale scientific computing pipelines.","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Fscience-research-data-analysis\u002Flatest-release-community\u002Fzarr-python\u002FSKILL.md",[],[226,227,228,229,230,231,232,233,234,99,235,236,237,238,239,240,241,242],"zarr python","python","zarr","arrays","Cloud Storage","scientific","storage","parallel","dask","numpy","gcs","s3","integration","scientific computing","cloud","scale","pipelines"]