[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7A96WBZFS06SPA2C9XV21MS":3,"skill-same:01M7A96WBZFS06SPA2C9XV21MS":93},{"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":18,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":31,"commitSha":32,"skillDirectory":33,"forksCount":34,"repositoryHtmlUrl":35,"repositoryHomepageUrl":36,"ownerHtmlUrl":37,"ownerBlogUrl":38,"ownerDescription":39,"updatedAt":40,"install":41,"resources":54},"01M7A96WBZFS06SPA2C9XV21MS","dicom-series-to-volume",null,"Used for converting one CT DICOM series folder to a HU NIfTI volume with affine evidence. Not for multi-frame DICOM or clinical use.","themusebook\u002Fskills","themusebook","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F349927247?v=4","01M7A5C1RV5S3V5FF6ZACVDNRN","musebook","01M7A5C1SFC3KRZAXNN60XJ7KM",0,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fthemusebook\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fnvidia\u002Fdicom-series-to-volume\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: dicom-series-to-volume\ndescription: Used for converting one CT DICOM series folder to a HU NIfTI volume with affine evidence. Not for multi-frame DICOM or clinical use.\nlicense: Apache-2.0\nallowed-tools: Bash\nmetadata:\n  author: NVIDIA MedTech Team\n  tags:\n    - MedTech\n    - DICOM\n    - NIfTI\n---\n\n# dicom_series_to_volume\n\n## Purpose\n- Used for converting one CT DICOM series folder to a HU NIfTI volume with affine evidence. Not for multi-frame DICOM or clinical use.\n- Use the wrapper exactly as documented; do not replace the upstream entrypoint with a handwritten implementation.\n- Manifest I\u002FO: inputs are `dicom_dir`; outputs are `nifti_volume` and `result_json`.\n\n## Instructions\n- Read `skill_manifest.yaml` before changing arguments, side effects, or validation gates.\n- Run `scripts\u002Fseries_to_volume.py` through the documented command below; keep outputs under a caller-provided run directory.\n- If a host agent exposes `run_script`, use `run_script(\"scripts\u002Fseries_to_volume.py\", args=[...])`; otherwise run the Bash\u002FPython command shown below.\n- Check the emitted JSON and the paired `dicom_volume_quality_v1` verifier before treating the run as evidence.\n\n## Available Scripts\n| Script | Purpose | Arguments |\n|---|---|---|\n| `scripts\u002Fseries_to_volume.py` | Primary entrypoint declared by skill_manifest.yaml. | `PATH_TO_DICOM_DIR [--output OUT.nii.gz]` |\n\n## Prerequisites\n- Runtime requirements: Python packages listed in `runtime.side_effects.pip_packages`.\n- Run commands from the repository root unless an existing section below says otherwise.\n\n## Limitations\n- Single-series only; multi-series input is rejected at preflight.\n- Multi-frame DICOM (NumberOfFrames > 1 per file) not supported.\n- Compressed transfer syntaxes (JPEG \u002F JPEG2000 \u002F RLE) not supported.\n- No voxel reorientation. The affine is derived from DICOM headers and represented in NIfTI\u002FRAS coordinates; a downstream gate (e.g. expected_axcodes) is expected to assert orientation before this volume is fed to a segmentation model.\n- Not for clinical deployment, autonomous diagnosis, regulatory submission, production inference (use a vetted converter such as dcm2niix for that).\n\n## Troubleshooting\n| Error | Cause | Fix |\n|---|---|---|\n| Missing dependency or import error | Runtime package drift from `skill_manifest.yaml`. | Install the packages declared in the manifest or use the documented setup command. |\n| Empty or schema-invalid output | Wrong input path, unsupported modality, or upstream failure. | Re-run with a known fixture and inspect the wrapper JSON plus stderr. |\n| Validation gate failure | Output violated a declared engineering invariant. | Keep the failed evidence pack and use the gate message to repair inputs or wrapper code. |\n\nReads one DICOM series, sorts slices by `ImagePositionPatient`, applies\n`RescaleSlope` and `RescaleIntercept`, builds an affine from orientation and\nspacing tags, and writes a `.nii.gz` plus JSON summary.\n\n```bash\npython scripts\u002Fseries_to_volume.py PATH_TO_DICOM_DIR --output PATH_TO_OUT.nii.gz\n```\n\nFor a trusted run with the paired verifier:\n\n```bash\npython -m eval_engine.run_trusted skills\u002Fdicom-series-to-volume \\\n  --fixture PATH_TO_DICOM_DIR \\\n  --out runs\u002Fdicom_series_to_volume_trusted\n```\n\nKey output fields: `n_slices`, `series_instance_uid`, `output.path`,\n`output.shape`, `output.spacing`, `output.axcodes`, `output.affine`,\n`hu_range`, and `runtime.conversion_seconds`.\n\nScope limits: single-series CT only; no multi-frame DICOM, compressed transfer\nsyntax handling, RT structure sets, auto-reorientation, or clinical use.\n","# dicom_series_to_volume\n\n## Purpose\n- Used for converting one CT DICOM series folder to a HU NIfTI volume with affine evidence. Not for multi-frame DICOM or clinical use.\n- Use the wrapper exactly as documented; do not replace the upstream entrypoint with a handwritten implementation.\n- Manifest I\u002FO: inputs are `dicom_dir`; outputs are `nifti_volume` and `result_json`.\n\n## Instructions\n- Read `skill_manifest.yaml` before changing arguments, side effects, or validation gates.\n- Run `scripts\u002Fseries_to_volume.py` through the documented command below; keep outputs under a caller-provided run directory.\n- If a host agent exposes `run_script`, use `run_script(\"scripts\u002Fseries_to_volume.py\", args=[...])`; otherwise run the Bash\u002FPython command shown below.\n- Check the emitted JSON and the paired `dicom_volume_quality_v1` verifier before treating the run as evidence.\n\n## Available Scripts\n| Script | Purpose | Arguments |\n|---|---|---|\n| `scripts\u002Fseries_to_volume.py` | Primary entrypoint declared by skill_manifest.yaml. | `PATH_TO_DICOM_DIR [--output OUT.nii.gz]` |\n\n## Prerequisites\n- Runtime requirements: Python packages listed in `runtime.side_effects.pip_packages`.\n- Run commands from the repository root unless an existing section below says otherwise.\n\n## Limitations\n- Single-series only; multi-series input is rejected at preflight.\n- Multi-frame DICOM (NumberOfFrames > 1 per file) not supported.\n- Compressed transfer syntaxes (JPEG \u002F JPEG2000 \u002F RLE) not supported.\n- No voxel reorientation. The affine is derived from DICOM headers and represented in NIfTI\u002FRAS coordinates; a downstream gate (e.g. expected_axcodes) is expected to assert orientation before this volume is fed to a segmentation model.\n- Not for clinical deployment, autonomous diagnosis, regulatory submission, production inference (use a vetted converter such as dcm2niix for that).\n\n## Troubleshooting\n| Error | Cause | Fix |\n|---|---|---|\n| Missing dependency or import error | Runtime package drift from `skill_manifest.yaml`. | Install the packages declared in the manifest or use the documented setup command. |\n| Empty or schema-invalid output | Wrong input path, unsupported modality, or upstream failure. | Re-run with a known fixture and inspect the wrapper JSON plus stderr. |\n| Validation gate failure | Output violated a declared engineering invariant. | Keep the failed evidence pack and use the gate message to repair inputs or wrapper code. |\n\nReads one DICOM series, sorts slices by `ImagePositionPatient`, applies\n`RescaleSlope` and `RescaleIntercept`, builds an affine from orientation and\nspacing tags, and writes a `.nii.gz` plus JSON summary.\n\n```bash\npython scripts\u002Fseries_to_volume.py PATH_TO_DICOM_DIR --output PATH_TO_OUT.nii.gz\n```\n\nFor a trusted run with the paired verifier:\n\n```bash\npython -m eval_engine.run_trusted skills\u002Fdicom-series-to-volume \\\n  --fixture PATH_TO_DICOM_DIR \\\n  --out runs\u002Fdicom_series_to_volume_trusted\n```\n\nKey output fields: `n_slices`, `series_instance_uid`, `output.path`,\n`output.shape`, `output.spacing`, `output.axcodes`, `output.affine`,\n`hu_range`, and `runtime.conversion_seconds`.\n\nScope limits: single-series CT only; no multi-frame DICOM, compressed transfer\nsyntax handling, RT structure sets, auto-reorientation, or clinical use.\n",{"name":5,"license":15,"metadata":24,"description":7,"allowed-tools":30},{"tags":25,"author":29},[26,27,28],"MedTech","DICOM","NIfTI","NVIDIA MedTech Team","Bash","main","1c523aba5cdf867a79f078847075492d7778092a","skills\u002Fnvidia\u002Fdicom-series-to-volume",1,"https:\u002F\u002Fgithub.com\u002Fthemusebook\u002Fskills","https:\u002F\u002Fskills.x402.wtf","https:\u002F\u002Fgithub.com\u002Fthemusebook","https:\u002F\u002Fmusebook.trade","The official Github of musebook","2026-11-02 23:42:26",{"source":8,"skillName":5,"commands":42,"prompt":52,"zipAvailable":53},[43,46,49],{"tool":44,"command":45},"npx","npx skills add themusebook\u002Fskills --skill dicom-series-to-volume",{"tool":47,"command":48},"bunx","bunx skills add themusebook\u002Fskills --skill dicom-series-to-volume",{"tool":50,"command":51},"pnpm","pnpm dlx skills add themusebook\u002Fskills --skill dicom-series-to-volume","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fthemusebook\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fnvidia\u002Fdicom-series-to-volume\u002FSKILL.md",true,[55,61,65,71,75,79,83,89],{"path":56,"resourceType":57,"resourceTypeDesc":58,"sizeBytes":59,"sourceHtmlUrl":60},"BENCHMARK.md","REFERENCE","参考文档",4292,"https:\u002F\u002Fgithub.com\u002Fthemusebook\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fnvidia\u002Fdicom-series-to-volume\u002FBENCHMARK.md",{"path":62,"resourceType":57,"resourceTypeDesc":58,"sizeBytes":63,"sourceHtmlUrl":64},"evals\u002Fevals.json",1519,"https:\u002F\u002Fgithub.com\u002Fthemusebook\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fnvidia\u002Fdicom-series-to-volume\u002Fevals\u002Fevals.json",{"path":66,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":69,"sourceHtmlUrl":70},"fixtures\u002Fgenerate_fixtures.py","SCRIPT","脚本",5729,"https:\u002F\u002Fgithub.com\u002Fthemusebook\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fnvidia\u002Fdicom-series-to-volume\u002Ffixtures\u002Fgenerate_fixtures.py",{"path":72,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":73,"sourceHtmlUrl":74},"scripts\u002Fseries_to_volume.py",9274,"https:\u002F\u002Fgithub.com\u002Fthemusebook\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fnvidia\u002Fdicom-series-to-volume\u002Fscripts\u002Fseries_to_volume.py",{"path":76,"resourceType":57,"resourceTypeDesc":58,"sizeBytes":77,"sourceHtmlUrl":78},"skill_manifest.yaml",6188,"https:\u002F\u002Fgithub.com\u002Fthemusebook\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fnvidia\u002Fdicom-series-to-volume\u002Fskill_manifest.yaml",{"path":80,"resourceType":57,"resourceTypeDesc":58,"sizeBytes":81,"sourceHtmlUrl":82},"skill-card.md",3774,"https:\u002F\u002Fgithub.com\u002Fthemusebook\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fnvidia\u002Fdicom-series-to-volume\u002Fskill-card.md",{"path":84,"resourceType":85,"resourceTypeDesc":86,"sizeBytes":87,"sourceHtmlUrl":88},"skill.oms.sig","OTHER","其他",5553,"https:\u002F\u002Fgithub.com\u002Fthemusebook\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fnvidia\u002Fdicom-series-to-volume\u002Fskill.oms.sig",{"path":90,"resourceType":57,"resourceTypeDesc":58,"sizeBytes":91,"sourceHtmlUrl":92},"validators\u002Foutput_schema.json",2105,"https:\u002F\u002Fgithub.com\u002Fthemusebook\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fnvidia\u002Fdicom-series-to-volume\u002Fvalidators\u002Foutput_schema.json",[94,103,110,117,124,131],{"skillNo":95,"name":96,"nameZh":6,"description":97,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":98,"licenseFileUrl":99,"sourceHtmlUrl":100,"categories":101,"keywords":102,"publishedAt":19},"01M7AB34SPKVSDRRG009PM1CZH","youtube-clipper","YouTube 视频智能剪辑工具。下载视频和字幕，AI 分析生成精细章节（几分钟级别）， 用户选择片段后自动剪辑、翻译字幕为中英双语、烧录字幕到视频，并生成总结文案。 使用场景：当用户需要剪辑 YouTube 视频、生成短视频片段、制作双语字幕版本时。 关键词：视频剪辑、YouTube、字幕翻译、双语字幕、视频下载、clip video","MIT","https:\u002F\u002Fgithub.com\u002Fthemusebook\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fyoutube-clipper\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fthemusebook\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fyoutube-clipper\u002FSKILL.md",[],[],{"skillNo":104,"name":105,"nameZh":6,"description":106,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":98,"licenseFileUrl":6,"sourceHtmlUrl":107,"categories":108,"keywords":109,"publishedAt":19},"01M7AB18BH6N7KR7939HRNWVMQ","viem-integration","Integrate EVM blockchains using viem. 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Use when building custom swap logic, async swaps, hook-owned liquidity, custom curves, dynamic fees, MEV protection, limit orders, or oracle hooks.","https:\u002F\u002Fgithub.com\u002Fthemusebook\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fv4-hook-generator\u002FSKILL.md",[],[],{"skillNo":132,"name":133,"nameZh":6,"description":134,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":98,"licenseFileUrl":6,"sourceHtmlUrl":135,"categories":136,"keywords":137,"publishedAt":19},"01M7AAZYH75H4CS03WSW2EZ8DM","testing","For testing with Light Protocol programs and clients on localnet, devnet, and mainnet validation.","https:\u002F\u002Fgithub.com\u002Fthemusebook\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Ftesting\u002FSKILL.md",[],[]]