[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7D3CW1STMSQ2Y5GPRWPR1VF":3,"skill-same:01M7D3CW1STMSQ2Y5GPRWPR1VF":79},{"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":20,"linkOnly":21,"markdownContent":22,"markdownBody":23,"frontMatter":24,"refName":30,"commitSha":31,"skillDirectory":32,"forksCount":14,"repositoryHtmlUrl":33,"repositoryHomepageUrl":6,"ownerHtmlUrl":34,"ownerBlogUrl":35,"ownerDescription":6,"updatedAt":36,"install":37,"resources":50},"01M7D3CW1STMSQ2Y5GPRWPR1VF","pacsomatic",null,"Operator toolkit for nf-core\u002Fpacsomatic matched tumor-normal workflows from BAM inputs. Use this skill when the user needs to validate run inputs, generate pacsomatic-compliant samplesheets, prepare reproducible Nextflow launch artifacts, run locally or submit to schedulers (LSF\u002FSlurm\u002FPBS\u002FSGE), and triage execution failures. Triggers on requests to run pacsomatic, prepare launch commands\u002Fscripts, perform dry-run checks, or troubleshoot pipeline startup and scheduler submission errors.","manuelcorpas\u002F26-SKILL-PORTABILITY","manuelcorpas","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F383303?v=4","01M7D36MFH14D3HT61MF6ZXYES","Manuel Corpas","01M7D36MG6A3BYTSB1YG7XH1TA",0,"MIT","https:\u002F\u002Fgithub.com\u002Fmanuelcorpas\u002F26-SKILL-PORTABILITY\u002Fblob\u002Fmain\u002F08-SECOND-ECOSYSTEM\u002FK-DENSE-SCIENTIFIC-AGENT-SKILLS\u002FSKILLS\u002Fpacsomatic\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fmanuelcorpas\u002F26-SKILL-PORTABILITY\u002Fblob\u002Fmain\u002F08-SECOND-ECOSYSTEM\u002FK-DENSE-SCIENTIFIC-AGENT-SKILLS\u002FSKILLS\u002Fpacsomatic\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: pacsomatic\ndescription: Operator toolkit for nf-core\u002Fpacsomatic matched tumor-normal workflows from BAM inputs. Use this skill when the user needs to validate run inputs, generate pacsomatic-compliant samplesheets, prepare reproducible Nextflow launch artifacts, run locally or submit to schedulers (LSF\u002FSlurm\u002FPBS\u002FSGE), and triage execution failures. Triggers on requests to run pacsomatic, prepare launch commands\u002Fscripts, perform dry-run checks, or troubleshoot pipeline startup and scheduler submission errors.\nlicense: MIT\nmetadata:\n  version: \"1.2\"\n  skill-author: Beifang Niu\n  contributors: Haidong, Wenchao\n  upstream-pipeline: https:\u002F\u002Fgithub.com\u002Fnf-core\u002Fpacsomatic\n---\n\n# pacsomatic\n\n## Overview\n\nThis skill provides a reproducible execution workflow for nf-core\u002Fpacsomatic, centered on a single helper entrypoint that handles validation, artifact generation, and optional execution.\n\nPrimary entrypoint:\n- `scripts\u002Frun_pacsomatic.py`\n\nThe helper script:\n- validates required identifiers, files, reference mode, and runtime prerequisites\n- writes a pacsomatic-compatible samplesheet (`patient,sample,status,bam,pbi`)\n- generates a params YAML and launch script for reproducible reruns\n- supports dry-run validation and run\u002Fsubmit execution paths\n\nUse this skill as the default path for pacsomatic operations. Do not bypass it with manually assembled `nextflow run nf-core\u002Fpacsomatic` commands unless the user explicitly asks for manual command construction.\n\n## When to Use This Skill\n\nInvoke this skill when the user asks to:\n- run matched tumor-normal analysis from BAM files\n- generate or fix pacsomatic samplesheet and launch artifacts\n- execute locally or submit to schedulers (LSF\u002FSlurm\u002FPBS\u002FSGE)\n- perform dry-run validation before execution\n- troubleshoot launch failures or summarize run outputs\n\nDo not use this skill for:\n- deep biological interpretation beyond run-level sanity checks\n- editing pipeline internals unless explicitly requested\n\nTypical trigger phrases:\n- \"run nf-core\u002Fpacsomatic for this tumor-normal pair\"\n- \"prepare pacsomatic samplesheet and launch script\"\n- \"do a dry run first and tell me what is missing\"\n- \"submit pacsomatic to slurm\u002Flsf and return the job id\"\n- \"why did pacsomatic submission fail\"\n\n## Routing and Execution Rules\n\n1. Always collect required run inputs first.\n2. Always route through `scripts\u002Frun_pacsomatic.py` for validation and artifact generation.\n3. Default to `--dry-run` when the user asks for checks\u002Fvalidation only.\n4. Use `--run` only when the user asks to execute\u002Fsubmit.\n5. For scheduler modes, include executor-specific resource arguments and return detected job ID when available.\n6. If execution fails, report first failure point and next triage target (`.nextflow.log`, `pipeline_info`, failing task logs).\n\n## Inputs Required\n\nRequired:\n- tumor BAM path\n- normal BAM path\n- patient ID\n- tumor sample ID\n- normal sample ID\n- output directory\n- exactly one reference mode: `--fasta` or `--genome`\n\nOptional:\n- profile, resources, scheduler account\u002Fqueue\n- pipeline version (`-r`)\n- params file, resume\u002Freport\u002Fdag flags\n- `--dry-run` and\u002For `--run`\n\n## Workflow\n\n1. Validate identity and input constraints.\n2. Validate required local paths (BAM, optional PBI, optional FASTA).\n3. Resolve runtime and dependency checks.\n4. Build samplesheet and generated params YAML.\n5. Generate launch script for selected executor.\n6. If `--dry-run` and not `--run`, stop after artifact generation.\n7. If `--run`, execute locally or submit to scheduler.\n8. Return command\u002Fscript path, validation status, and job ID (if detected).\n\n## Agent Response Contract\n\nEvery response after invocation should include:\n- exact command used or generated script path\n- confirmation that validation checks ran\n- run type (`dry-run` vs `run`)\n- scheduler job ID when available\n- one concrete next step for validation\u002Ftriage\n\n## Quick Start\n\nDry run:\n\n```bash\npython scripts\u002Frun_pacsomatic.py \\\n  --tumor-bam \u002Fpath\u002Fto\u002Ftumor.bam \\\n  --normal-bam \u002Fpath\u002Fto\u002Fnormal.bam \\\n  --patient-id P001 \\\n  --tumor-sample-id P001_T \\\n  --normal-sample-id P001_N \\\n  --outdir \u002Fpath\u002Fto\u002Foutput \\\n  --genome GRCh38 \\\n  --profile singularity,sanger \\\n  --dry-run\n```\n\nScheduler execution example (Slurm):\n\n```bash\npython scripts\u002Frun_pacsomatic.py \\\n  --tumor-bam \u002Fpath\u002Fto\u002Ftumor.bam \\\n  --normal-bam \u002Fpath\u002Fto\u002Fnormal.bam \\\n  --patient-id P001 \\\n  --tumor-sample-id P001_T \\\n  --normal-sample-id P001_N \\\n  --outdir \u002Fpath\u002Fto\u002Foutput \\\n  --genome GRCh38 \\\n  --profile singularity,sanger \\\n  --executor slurm \\\n  --queue compute \\\n  --project my_account \\\n  --cpus 16 \\\n  --memory-gb 64 \\\n  --walltime 48:00 \\\n  --run\n```\n\n## Configuration\n\nUse `config.yaml` as the baseline for profile\u002Fexecutor\u002Fruntime defaults. Override at invocation time when user requirements differ.\n\n## Testing\n\nRun unit tests from skill root:\n\n```bash\npython -m unittest discover -s tests\u002Fpacsomatic -v\n```\n\n## References\n\n- `references\u002Fagent-playbook.md`\n- `references\u002Fconfig-and-output.md`\n- `references\u002Fpacsomatic_guide.md`\n- `scripts\u002Frun_pacsomatic.py`\n","# pacsomatic\n\n## Overview\n\nThis skill provides a reproducible execution workflow for nf-core\u002Fpacsomatic, centered on a single helper entrypoint that handles validation, artifact generation, and optional execution.\n\nPrimary entrypoint:\n- `scripts\u002Frun_pacsomatic.py`\n\nThe helper script:\n- validates required identifiers, files, reference mode, and runtime prerequisites\n- writes a pacsomatic-compatible samplesheet (`patient,sample,status,bam,pbi`)\n- generates a params YAML and launch script for reproducible reruns\n- supports dry-run validation and run\u002Fsubmit execution paths\n\nUse this skill as the default path for pacsomatic operations. Do not bypass it with manually assembled `nextflow run nf-core\u002Fpacsomatic` commands unless the user explicitly asks for manual command construction.\n\n## When to Use This Skill\n\nInvoke this skill when the user asks to:\n- run matched tumor-normal analysis from BAM files\n- generate or fix pacsomatic samplesheet and launch artifacts\n- execute locally or submit to schedulers (LSF\u002FSlurm\u002FPBS\u002FSGE)\n- perform dry-run validation before execution\n- troubleshoot launch failures or summarize run outputs\n\nDo not use this skill for:\n- deep biological interpretation beyond run-level sanity checks\n- editing pipeline internals unless explicitly requested\n\nTypical trigger phrases:\n- \"run nf-core\u002Fpacsomatic for this tumor-normal pair\"\n- \"prepare pacsomatic samplesheet and launch script\"\n- \"do a dry run first and tell me what is missing\"\n- \"submit pacsomatic to slurm\u002Flsf and return the job id\"\n- \"why did pacsomatic submission fail\"\n\n## Routing and Execution Rules\n\n1. Always collect required run inputs first.\n2. Always route through `scripts\u002Frun_pacsomatic.py` for validation and artifact generation.\n3. Default to `--dry-run` when the user asks for checks\u002Fvalidation only.\n4. Use `--run` only when the user asks to execute\u002Fsubmit.\n5. For scheduler modes, include executor-specific resource arguments and return detected job ID when available.\n6. If execution fails, report first failure point and next triage target (`.nextflow.log`, `pipeline_info`, failing task logs).\n\n## Inputs Required\n\nRequired:\n- tumor BAM path\n- normal BAM path\n- patient ID\n- tumor sample ID\n- normal sample ID\n- output directory\n- exactly one reference mode: `--fasta` or `--genome`\n\nOptional:\n- profile, resources, scheduler account\u002Fqueue\n- pipeline version (`-r`)\n- params file, resume\u002Freport\u002Fdag flags\n- `--dry-run` and\u002For `--run`\n\n## Workflow\n\n1. Validate identity and input constraints.\n2. Validate required local paths (BAM, optional PBI, optional FASTA).\n3. Resolve runtime and dependency checks.\n4. Build samplesheet and generated params YAML.\n5. Generate launch script for selected executor.\n6. If `--dry-run` and not `--run`, stop after artifact generation.\n7. If `--run`, execute locally or submit to scheduler.\n8. Return command\u002Fscript path, validation status, and job ID (if detected).\n\n## Agent Response Contract\n\nEvery response after invocation should include:\n- exact command used or generated script path\n- confirmation that validation checks ran\n- run type (`dry-run` vs `run`)\n- scheduler job ID when available\n- one concrete next step for validation\u002Ftriage\n\n## Quick Start\n\nDry run:\n\n```bash\npython scripts\u002Frun_pacsomatic.py \\\n  --tumor-bam \u002Fpath\u002Fto\u002Ftumor.bam \\\n  --normal-bam \u002Fpath\u002Fto\u002Fnormal.bam \\\n  --patient-id P001 \\\n  --tumor-sample-id P001_T \\\n  --normal-sample-id P001_N \\\n  --outdir \u002Fpath\u002Fto\u002Foutput \\\n  --genome GRCh38 \\\n  --profile singularity,sanger \\\n  --dry-run\n```\n\nScheduler execution example (Slurm):\n\n```bash\npython scripts\u002Frun_pacsomatic.py \\\n  --tumor-bam \u002Fpath\u002Fto\u002Ftumor.bam \\\n  --normal-bam \u002Fpath\u002Fto\u002Fnormal.bam \\\n  --patient-id P001 \\\n  --tumor-sample-id P001_T \\\n  --normal-sample-id P001_N \\\n  --outdir \u002Fpath\u002Fto\u002Foutput \\\n  --genome GRCh38 \\\n  --profile singularity,sanger \\\n  --executor slurm \\\n  --queue compute \\\n  --project my_account \\\n  --cpus 16 \\\n  --memory-gb 64 \\\n  --walltime 48:00 \\\n  --run\n```\n\n## Configuration\n\nUse `config.yaml` as the baseline for profile\u002Fexecutor\u002Fruntime defaults. Override at invocation time when user requirements differ.\n\n## Testing\n\nRun unit tests from skill root:\n\n```bash\npython -m unittest discover -s tests\u002Fpacsomatic -v\n```\n\n## References\n\n- `references\u002Fagent-playbook.md`\n- `references\u002Fconfig-and-output.md`\n- `references\u002Fpacsomatic_guide.md`\n- `scripts\u002Frun_pacsomatic.py`\n",{"name":5,"license":15,"metadata":25,"description":7},{"version":26,"contributors":27,"skill-author":28,"upstream-pipeline":29},"1.2","Haidong, Wenchao","Beifang Niu","https:\u002F\u002Fgithub.com\u002Fnf-core\u002Fpacsomatic","main","3a8447fd0c3cec120c60fe3eb7ffbc3d67568371","08-SECOND-ECOSYSTEM\u002FK-DENSE-SCIENTIFIC-AGENT-SKILLS\u002FSKILLS\u002Fpacsomatic","https:\u002F\u002Fgithub.com\u002Fmanuelcorpas\u002F26-SKILL-PORTABILITY","https:\u002F\u002Fgithub.com\u002Fmanuelcorpas","https:\u002F\u002Fmanuelcorpas.com\u002F","2026-09-20 04:11:00",{"source":8,"skillName":5,"commands":38,"prompt":48,"zipAvailable":49},[39,42,45],{"tool":40,"command":41},"npx","npx skills add manuelcorpas\u002F26-SKILL-PORTABILITY --skill pacsomatic",{"tool":43,"command":44},"bunx","bunx skills add manuelcorpas\u002F26-SKILL-PORTABILITY --skill pacsomatic",{"tool":46,"command":47},"pnpm","pnpm dlx skills add manuelcorpas\u002F26-SKILL-PORTABILITY --skill pacsomatic","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmanuelcorpas\u002F26-SKILL-PORTABILITY\u002Fblob\u002Fmain\u002F08-SECOND-ECOSYSTEM\u002FK-DENSE-SCIENTIFIC-AGENT-SKILLS\u002FSKILLS\u002Fpacsomatic\u002FSKILL.md",true,[51,57,61,65,69,73],{"path":52,"resourceType":53,"resourceTypeDesc":54,"sizeBytes":55,"sourceHtmlUrl":56},"config.yaml","REFERENCE","参考文档",1058,"https:\u002F\u002Fgithub.com\u002Fmanuelcorpas\u002F26-SKILL-PORTABILITY\u002Fblob\u002Fmain\u002F08-SECOND-ECOSYSTEM\u002FK-DENSE-SCIENTIFIC-AGENT-SKILLS\u002FSKILLS\u002Fpacsomatic\u002Fconfig.yaml",{"path":58,"resourceType":58,"resourceTypeDesc":59,"sizeBytes":60,"sourceHtmlUrl":16},"LICENSE","许可证",1068,{"path":62,"resourceType":53,"resourceTypeDesc":54,"sizeBytes":63,"sourceHtmlUrl":64},"references\u002Fagent-playbook.md",2052,"https:\u002F\u002Fgithub.com\u002Fmanuelcorpas\u002F26-SKILL-PORTABILITY\u002Fblob\u002Fmain\u002F08-SECOND-ECOSYSTEM\u002FK-DENSE-SCIENTIFIC-AGENT-SKILLS\u002FSKILLS\u002Fpacsomatic\u002Freferences\u002Fagent-playbook.md",{"path":66,"resourceType":53,"resourceTypeDesc":54,"sizeBytes":67,"sourceHtmlUrl":68},"references\u002Fconfig-and-output.md",2156,"https:\u002F\u002Fgithub.com\u002Fmanuelcorpas\u002F26-SKILL-PORTABILITY\u002Fblob\u002Fmain\u002F08-SECOND-ECOSYSTEM\u002FK-DENSE-SCIENTIFIC-AGENT-SKILLS\u002FSKILLS\u002Fpacsomatic\u002Freferences\u002Fconfig-and-output.md",{"path":70,"resourceType":53,"resourceTypeDesc":54,"sizeBytes":71,"sourceHtmlUrl":72},"references\u002Fpacsomatic_guide.md",7363,"https:\u002F\u002Fgithub.com\u002Fmanuelcorpas\u002F26-SKILL-PORTABILITY\u002Fblob\u002Fmain\u002F08-SECOND-ECOSYSTEM\u002FK-DENSE-SCIENTIFIC-AGENT-SKILLS\u002FSKILLS\u002Fpacsomatic\u002Freferences\u002Fpacsomatic_guide.md",{"path":74,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":77,"sourceHtmlUrl":78},"scripts\u002Frun_pacsomatic.py","SCRIPT","脚本",29233,"https:\u002F\u002Fgithub.com\u002Fmanuelcorpas\u002F26-SKILL-PORTABILITY\u002Fblob\u002Fmain\u002F08-SECOND-ECOSYSTEM\u002FK-DENSE-SCIENTIFIC-AGENT-SKILLS\u002FSKILLS\u002Fpacsomatic\u002Fscripts\u002Frun_pacsomatic.py",[80,87,94,101,108,115],{"skillNo":81,"name":82,"nameZh":6,"description":83,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":84,"categories":85,"keywords":86,"publishedAt":20},"01M7D3F0ECBGHC7AG1VNBBSKWX","uncertainty-and-units","Track physical units and propagate measurement uncertainty in scientific calculations using pint and uncertainties. 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Trigger on \"is this number physically reasonable\", \"sanity check these units\", \"what regime is this flow in\", or a result that looks off by orders of magnitude.","https:\u002F\u002Fgithub.com\u002Fmanuelcorpas\u002F26-SKILL-PORTABILITY\u002Fblob\u002Fmain\u002F08-SECOND-ECOSYSTEM\u002FK-DENSE-SCIENTIFIC-AGENT-SKILLS\u002FSKILLS\u002Funcertainty-and-units\u002FSKILL.md",[],[],{"skillNo":88,"name":89,"nameZh":6,"description":90,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":91,"categories":92,"keywords":93,"publishedAt":20},"01M7D3EVMST1S5GH8E9ECNQWKJ","scikit-survival","Build, evaluate, and audit right-censored or competing-risk survival workflows with scikit-survival, including leakage-safe preprocessing, model selection, probability prediction, and censoring-aware metrics.","https:\u002F\u002Fgithub.com\u002Fmanuelcorpas\u002F26-SKILL-PORTABILITY\u002Fblob\u002Fmain\u002F08-SECOND-ECOSYSTEM\u002FK-DENSE-SCIENTIFIC-AGENT-SKILLS\u002FSKILLS\u002Fscikit-survival\u002FSKILL.md",[],[],{"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":15,"licenseFileUrl":6,"sourceHtmlUrl":98,"categories":99,"keywords":100,"publishedAt":20},"01M7D3EP613EF8GVGJQQK4H2Q7","pytdc","Use Therapeutics Data Commons through the PyTDC Python package for registry discovery, approved dataset access, task-aware splits, evaluator metrics, benchmark groups, and bounded molecular-oracle workflows.","https:\u002F\u002Fgithub.com\u002Fmanuelcorpas\u002F26-SKILL-PORTABILITY\u002Fblob\u002Fmain\u002F08-SECOND-ECOSYSTEM\u002FK-DENSE-SCIENTIFIC-AGENT-SKILLS\u002FSKILLS\u002Fpytdc\u002FSKILL.md",[],[],{"skillNo":102,"name":103,"nameZh":6,"description":104,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":105,"categories":106,"keywords":107,"publishedAt":20},"01M7D3EHNQ4JA4FEX8XR7VXXEP","pysam","Python\u002FHTSlib workflows for genomic files. Use when reading, querying, filtering, or writing SAM\u002FBAM\u002FCRAM, VCF\u002FBCF, FASTA\u002FFASTQ, or tabix data with pysam, including pileup, coverage, indexing, and CRAM references.","https:\u002F\u002Fgithub.com\u002Fmanuelcorpas\u002F26-SKILL-PORTABILITY\u002Fblob\u002Fmain\u002F08-SECOND-ECOSYSTEM\u002FK-DENSE-SCIENTIFIC-AGENT-SKILLS\u002FSKILLS\u002Fpysam\u002FSKILL.md",[],[],{"skillNo":109,"name":110,"nameZh":6,"description":111,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":112,"categories":113,"keywords":114,"publishedAt":20},"01M7D3EBFVTP5S3JQ9Z0RZN8NX","pymatgen","Analyze, validate, convert, and transform materials structures and computed materials data with current pymatgen APIs, including local phase diagrams, symmetry sensitivity, electronic-structure I\u002FO, and explicitly bounded Materials Project queries.","https:\u002F\u002Fgithub.com\u002Fmanuelcorpas\u002F26-SKILL-PORTABILITY\u002Fblob\u002Fmain\u002F08-SECOND-ECOSYSTEM\u002FK-DENSE-SCIENTIFIC-AGENT-SKILLS\u002FSKILLS\u002Fpymatgen\u002FSKILL.md",[],[],{"skillNo":116,"name":117,"nameZh":6,"description":118,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":119,"categories":120,"keywords":121,"publishedAt":20},"01M7D3E6H6XPJJ1KN4HX8PFN36","pydicom","Use pydicom to read, inspect, write, transform, and safely preflight local DICOM datasets and pixel data. Applies to DICOM metadata, transfer syntaxes, compression plugins, frames, private elements, JSON, and bounded de-identification review.","https:\u002F\u002Fgithub.com\u002Fmanuelcorpas\u002F26-SKILL-PORTABILITY\u002Fblob\u002Fmain\u002F08-SECOND-ECOSYSTEM\u002FK-DENSE-SCIENTIFIC-AGENT-SKILLS\u002FSKILLS\u002Fpydicom\u002FSKILL.md",[],[]]