[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6R3GY2ZMXV7P5RHT69KM2YJ":3,"skill-same:01M6R3GY2ZMXV7P5RHT69KM2YJ":96},{"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":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":49,"commitSha":50,"skillDirectory":51,"forksCount":52,"repositoryHtmlUrl":53,"repositoryHomepageUrl":6,"ownerHtmlUrl":54,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":55,"install":56,"resources":69},"01M6R3GY2ZMXV7P5RHT69KM2YJ","pyopenms",null,"Python interface to OpenMS for mass spectrometry data analysis. Use for LC-MS\u002FMS proteomics and metabolomics workflows including file handling (mzML, mzXML, mzTab, FASTA, pepXML, protXML, mzIdentML), signal processing, feature detection, peptide identification, and quantitative analysis. Apply when working with mass spectrometry data, analyzing proteomics experiments, or processing metabolomics datasets.","Mishit18\u002Fclaude-code-templates","Mishit18","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F244127857?v=4","01M6QXBYKWM76HSKYDNWSKKE41","Mishit Sharma","01M6QXBYRJ0CAVM51S1RTEKTZZ",1,"MIT","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpyopenms\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化","SCENARIO","使用场景",[5,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"mass spectrometry","FASTA","when","signal","for","interface","processing","identification","metabolomics","feature","file handling","data analysis","quantitative analysis","ms","analysis","data","analyzing","experiments","workflows","python","2026-11-10 10:12:30",false,"---\nname: pyopenms\ndescription: Python interface to OpenMS for mass spectrometry data analysis. Use for LC-MS\u002FMS proteomics and metabolomics workflows including file handling (mzML, mzXML, mzTab, FASTA, pepXML, protXML, mzIdentML), signal processing, feature detection, peptide identification, and quantitative analysis. Apply when working with mass spectrometry data, analyzing proteomics experiments, or processing metabolomics datasets.\n---\n\n# PyOpenMS\n\n## Overview\n\nPyOpenMS provides Python bindings to the OpenMS library for computational mass spectrometry, enabling analysis of proteomics and metabolomics data. Use for handling mass spectrometry file formats, processing spectral data, detecting features, identifying peptides\u002Fproteins, and performing quantitative analysis.\n\n## Installation\n\nInstall using uv:\n\n```bash\nuv uv pip install pyopenms\n```\n\nVerify installation:\n\n```python\nimport pyopenms\nprint(pyopenms.__version__)\n```\n\n## Core Capabilities\n\nPyOpenMS organizes functionality into these domains:\n\n### 1. File I\u002FO and Data Formats\n\nHandle mass spectrometry file formats and convert between representations.\n\n**Supported formats**: mzML, mzXML, TraML, mzTab, FASTA, pepXML, protXML, mzIdentML, featureXML, consensusXML, idXML\n\nBasic file reading:\n\n```python\nimport pyopenms as ms\n\n# Read mzML file\nexp = ms.MSExperiment()\nms.MzMLFile().load(\"data.mzML\", exp)\n\n# Access spectra\nfor spectrum in exp:\n    mz, intensity = spectrum.get_peaks()\n    print(f\"Spectrum: {len(mz)} peaks\")\n```\n\n**For detailed file handling**: See `references\u002Ffile_io.md`\n\n### 2. Signal Processing\n\nProcess raw spectral data with smoothing, filtering, centroiding, and normalization.\n\nBasic spectrum processing:\n\n```python\n# Smooth spectrum with Gaussian filter\ngaussian = ms.GaussFilter()\nparams = gaussian.getParameters()\nparams.setValue(\"gaussian_width\", 0.1)\ngaussian.setParameters(params)\ngaussian.filterExperiment(exp)\n```\n\n**For algorithm details**: See `references\u002Fsignal_processing.md`\n\n### 3. Feature Detection\n\nDetect and link features across spectra and samples for quantitative analysis.\n\n```python\n# Detect features\nff = ms.FeatureFinder()\nff.run(\"centroided\", exp, features, params, ms.FeatureMap())\n```\n\n**For complete workflows**: See `references\u002Ffeature_detection.md`\n\n### 4. Peptide and Protein Identification\n\nIntegrate with search engines and process identification results.\n\n**Supported engines**: Comet, Mascot, MSGFPlus, XTandem, OMSSA, Myrimatch\n\nBasic identification workflow:\n\n```python\n# Load identification data\nprotein_ids = []\npeptide_ids = []\nms.IdXMLFile().load(\"identifications.idXML\", protein_ids, peptide_ids)\n\n# Apply FDR filtering\nfdr = ms.FalseDiscoveryRate()\nfdr.apply(peptide_ids)\n```\n\n**For detailed workflows**: See `references\u002Fidentification.md`\n\n### 5. Metabolomics Analysis\n\nPerform untargeted metabolomics preprocessing and analysis.\n\nTypical workflow:\n1. Load and process raw data\n2. Detect features\n3. Align retention times across samples\n4. Link features to consensus map\n5. Annotate with compound databases\n\n**For complete metabolomics workflows**: See `references\u002Fmetabolomics.md`\n\n## Data Structures\n\nPyOpenMS uses these primary objects:\n\n- **MSExperiment**: Collection of spectra and chromatograms\n- **MSSpectrum**: Single mass spectrum with m\u002Fz and intensity pairs\n- **MSChromatogram**: Chromatographic trace\n- **Feature**: Detected chromatographic peak with quality metrics\n- **FeatureMap**: Collection of features\n- **PeptideIdentification**: Search results for peptides\n- **ProteinIdentification**: Search results for proteins\n\n**For detailed documentation**: See `references\u002Fdata_structures.md`\n\n## Common Workflows\n\n### Quick Start: Load and Explore Data\n\n```python\nimport pyopenms as ms\n\n# Load mzML file\nexp = ms.MSExperiment()\nms.MzMLFile().load(\"sample.mzML\", exp)\n\n# Get basic statistics\nprint(f\"Number of spectra: {exp.getNrSpectra()}\")\nprint(f\"Number of chromatograms: {exp.getNrChromatograms()}\")\n\n# Examine first spectrum\nspec = exp.getSpectrum(0)\nprint(f\"MS level: {spec.getMSLevel()}\")\nprint(f\"Retention time: {spec.getRT()}\")\nmz, intensity = spec.get_peaks()\nprint(f\"Peaks: {len(mz)}\")\n```\n\n### Parameter Management\n\nMost algorithms use a parameter system:\n\n```python\n# Get algorithm parameters\nalgo = ms.GaussFilter()\nparams = algo.getParameters()\n\n# View available parameters\nfor param in params.keys():\n    print(f\"{param}: {params.getValue(param)}\")\n\n# Modify parameters\nparams.setValue(\"gaussian_width\", 0.2)\nalgo.setParameters(params)\n```\n\n### Export to Pandas\n\nConvert data to pandas DataFrames for analysis:\n\n```python\nimport pyopenms as ms\nimport pandas as pd\n\n# Load feature map\nfm = ms.FeatureMap()\nms.FeatureXMLFile().load(\"features.featureXML\", fm)\n\n# Convert to DataFrame\ndf = fm.get_df()\nprint(df.head())\n```\n\n## Integration with Other Tools\n\nPyOpenMS integrates with:\n- **Pandas**: Export data to DataFrames\n- **NumPy**: Work with peak arrays\n- **Scikit-learn**: Machine learning on MS data\n- **Matplotlib\u002FSeaborn**: Visualization\n- **R**: Via rpy2 bridge\n\n## Resources\n\n- **Official documentation**: https:\u002F\u002Fpyopenms.readthedocs.io\n- **OpenMS documentation**: https:\u002F\u002Fwww.openms.org\n- **GitHub**: https:\u002F\u002Fgithub.com\u002FOpenMS\u002FOpenMS\n\n## References\n\n- `references\u002Ffile_io.md` - Comprehensive file format handling\n- `references\u002Fsignal_processing.md` - Signal processing algorithms\n- `references\u002Ffeature_detection.md` - Feature detection and linking\n- `references\u002Fidentification.md` - Peptide and protein identification\n- `references\u002Fmetabolomics.md` - Metabolomics-specific workflows\n- `references\u002Fdata_structures.md` - Core objects and data structures\n\n","# PyOpenMS\n\n## Overview\n\nPyOpenMS provides Python bindings to the OpenMS library for computational mass spectrometry, enabling analysis of proteomics and metabolomics data. Use for handling mass spectrometry file formats, processing spectral data, detecting features, identifying peptides\u002Fproteins, and performing quantitative analysis.\n\n## Installation\n\nInstall using uv:\n\n```bash\nuv uv pip install pyopenms\n```\n\nVerify installation:\n\n```python\nimport pyopenms\nprint(pyopenms.__version__)\n```\n\n## Core Capabilities\n\nPyOpenMS organizes functionality into these domains:\n\n### 1. File I\u002FO and Data Formats\n\nHandle mass spectrometry file formats and convert between representations.\n\n**Supported formats**: mzML, mzXML, TraML, mzTab, FASTA, pepXML, protXML, mzIdentML, featureXML, consensusXML, idXML\n\nBasic file reading:\n\n```python\nimport pyopenms as ms\n\n# Read mzML file\nexp = ms.MSExperiment()\nms.MzMLFile().load(\"data.mzML\", exp)\n\n# Access spectra\nfor spectrum in exp:\n    mz, intensity = spectrum.get_peaks()\n    print(f\"Spectrum: {len(mz)} peaks\")\n```\n\n**For detailed file handling**: See `references\u002Ffile_io.md`\n\n### 2. Signal Processing\n\nProcess raw spectral data with smoothing, filtering, centroiding, and normalization.\n\nBasic spectrum processing:\n\n```python\n# Smooth spectrum with Gaussian filter\ngaussian = ms.GaussFilter()\nparams = gaussian.getParameters()\nparams.setValue(\"gaussian_width\", 0.1)\ngaussian.setParameters(params)\ngaussian.filterExperiment(exp)\n```\n\n**For algorithm details**: See `references\u002Fsignal_processing.md`\n\n### 3. Feature Detection\n\nDetect and link features across spectra and samples for quantitative analysis.\n\n```python\n# Detect features\nff = ms.FeatureFinder()\nff.run(\"centroided\", exp, features, params, ms.FeatureMap())\n```\n\n**For complete workflows**: See `references\u002Ffeature_detection.md`\n\n### 4. Peptide and Protein Identification\n\nIntegrate with search engines and process identification results.\n\n**Supported engines**: Comet, Mascot, MSGFPlus, XTandem, OMSSA, Myrimatch\n\nBasic identification workflow:\n\n```python\n# Load identification data\nprotein_ids = []\npeptide_ids = []\nms.IdXMLFile().load(\"identifications.idXML\", protein_ids, peptide_ids)\n\n# Apply FDR filtering\nfdr = ms.FalseDiscoveryRate()\nfdr.apply(peptide_ids)\n```\n\n**For detailed workflows**: See `references\u002Fidentification.md`\n\n### 5. Metabolomics Analysis\n\nPerform untargeted metabolomics preprocessing and analysis.\n\nTypical workflow:\n1. Load and process raw data\n2. Detect features\n3. Align retention times across samples\n4. Link features to consensus map\n5. Annotate with compound databases\n\n**For complete metabolomics workflows**: See `references\u002Fmetabolomics.md`\n\n## Data Structures\n\nPyOpenMS uses these primary objects:\n\n- **MSExperiment**: Collection of spectra and chromatograms\n- **MSSpectrum**: Single mass spectrum with m\u002Fz and intensity pairs\n- **MSChromatogram**: Chromatographic trace\n- **Feature**: Detected chromatographic peak with quality metrics\n- **FeatureMap**: Collection of features\n- **PeptideIdentification**: Search results for peptides\n- **ProteinIdentification**: Search results for proteins\n\n**For detailed documentation**: See `references\u002Fdata_structures.md`\n\n## Common Workflows\n\n### Quick Start: Load and Explore Data\n\n```python\nimport pyopenms as ms\n\n# Load mzML file\nexp = ms.MSExperiment()\nms.MzMLFile().load(\"sample.mzML\", exp)\n\n# Get basic statistics\nprint(f\"Number of spectra: {exp.getNrSpectra()}\")\nprint(f\"Number of chromatograms: {exp.getNrChromatograms()}\")\n\n# Examine first spectrum\nspec = exp.getSpectrum(0)\nprint(f\"MS level: {spec.getMSLevel()}\")\nprint(f\"Retention time: {spec.getRT()}\")\nmz, intensity = spec.get_peaks()\nprint(f\"Peaks: {len(mz)}\")\n```\n\n### Parameter Management\n\nMost algorithms use a parameter system:\n\n```python\n# Get algorithm parameters\nalgo = ms.GaussFilter()\nparams = algo.getParameters()\n\n# View available parameters\nfor param in params.keys():\n    print(f\"{param}: {params.getValue(param)}\")\n\n# Modify parameters\nparams.setValue(\"gaussian_width\", 0.2)\nalgo.setParameters(params)\n```\n\n### Export to Pandas\n\nConvert data to pandas DataFrames for analysis:\n\n```python\nimport pyopenms as ms\nimport pandas as pd\n\n# Load feature map\nfm = ms.FeatureMap()\nms.FeatureXMLFile().load(\"features.featureXML\", fm)\n\n# Convert to DataFrame\ndf = fm.get_df()\nprint(df.head())\n```\n\n## Integration with Other Tools\n\nPyOpenMS integrates with:\n- **Pandas**: Export data to DataFrames\n- **NumPy**: Work with peak arrays\n- **Scikit-learn**: Machine learning on MS data\n- **Matplotlib\u002FSeaborn**: Visualization\n- **R**: Via rpy2 bridge\n\n## Resources\n\n- **Official documentation**: https:\u002F\u002Fpyopenms.readthedocs.io\n- **OpenMS documentation**: https:\u002F\u002Fwww.openms.org\n- **GitHub**: https:\u002F\u002Fgithub.com\u002FOpenMS\u002FOpenMS\n\n## References\n\n- `references\u002Ffile_io.md` - Comprehensive file format handling\n- `references\u002Fsignal_processing.md` - Signal processing algorithms\n- `references\u002Ffeature_detection.md` - Feature detection and linking\n- `references\u002Fidentification.md` - Peptide and protein identification\n- `references\u002Fmetabolomics.md` - Metabolomics-specific workflows\n- `references\u002Fdata_structures.md` - Core objects and data structures\n\n",{"name":5,"description":7},"main","015422534a7457348a313b6381f0255d3364eb8c","cli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpyopenms",0,"https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates","https:\u002F\u002Fgithub.com\u002FMishit18","2026-11-13 18:15:57",{"source":8,"skillName":5,"commands":57,"prompt":67,"zipAvailable":68},[58,61,64],{"tool":59,"command":60},"npx","npx skills add Mishit18\u002Fclaude-code-templates --skill pyopenms",{"tool":62,"command":63},"bunx","bunx skills add Mishit18\u002Fclaude-code-templates --skill pyopenms",{"tool":65,"command":66},"pnpm","pnpm dlx skills add Mishit18\u002Fclaude-code-templates --skill pyopenms","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpyopenms\u002FSKILL.md",true,[70,76,80,84,88,92],{"path":71,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":74,"sourceHtmlUrl":75},"references\u002Fdata_structures.md","REFERENCE","参考文档",12391,"https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpyopenms\u002Freferences\u002Fdata_structures.md",{"path":77,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":78,"sourceHtmlUrl":79},"references\u002Ffeature_detection.md",10540,"https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpyopenms\u002Freferences\u002Ffeature_detection.md",{"path":81,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":82,"sourceHtmlUrl":83},"references\u002Ffile_io.md",7717,"https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpyopenms\u002Freferences\u002Ffile_io.md",{"path":85,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":86,"sourceHtmlUrl":87},"references\u002Fidentification.md",11330,"https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpyopenms\u002Freferences\u002Fidentification.md",{"path":89,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":90,"sourceHtmlUrl":91},"references\u002Fmetabolomics.md",13350,"https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpyopenms\u002Freferences\u002Fmetabolomics.md",{"path":93,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":94,"sourceHtmlUrl":95},"references\u002Fsignal_processing.md",9336,"https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpyopenms\u002Freferences\u002Fsignal_processing.md",[97,128,154,180,206,231],{"skillNo":98,"name":99,"nameZh":6,"description":100,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":101,"licenseFileUrl":102,"sourceHtmlUrl":103,"categories":104,"keywords":108,"publishedAt":44},"01M6R4ARN4W2VZRDERDTMN1KP0","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.","Apache-2.0","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[105],{"categoryNo":106,"categoryName":107,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,28,127],"web artifacts builder","web","artifacts","artifacts builder","builder","frontend","css","components","react","routing","state management","state","file","Tailwind","use","single-file","ui components","multi","single",{"skillNo":129,"name":130,"nameZh":6,"description":131,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":101,"licenseFileUrl":132,"sourceHtmlUrl":133,"categories":134,"keywords":139,"publishedAt":44},"01M6R45GXREM5DP8T08MAC788H","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.","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[135,138],{"categoryNo":136,"categoryName":137,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":106,"categoryName":107,"taxonomyType":21,"taxonomyTypeDesc":22},[140,141,142,114,143,144,110,145,146,147,148,149,150,151,152,153,28],"testing","webapp testing","webapp","screenshots","playwright","debugging","toolkit","browser","ui","logs","applications","behavior","local","with",{"skillNo":155,"name":156,"nameZh":6,"description":157,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":158,"sourceHtmlUrl":159,"categories":160,"keywords":161,"publishedAt":44},"01M6R2GYJ470X8EB7R58V0B68K","think-tank","Run a Virtual Think Tank — a structured multi-persona debate — before planning or making architectural\u002Fdesign\u002Fstrategic decisions. Use this skill whenever the user is about to plan a system, make a technology choice, evaluate trade-offs, decide on an approach, or faces any decision where multiple perspectives would sharpen the outcome. Also trigger when the user says \"think tank\", \"debate this\", \"perspectives on\", \"trade-offs\", \"should I use X or Y\", \"help me decide\", \"before we plan\", or asks for pros\u002Fcons of competing approaches. This skill should run BEFORE any implementation planning begins — it produces a structured analysis that feeds into better plans.","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fthink-tank\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fthink-tank\u002FSKILL.md",[],[162,163,164,165,166,38,167,168,169,170,171,172,173,174,175,176,177,178,179,26],"think tank","think","design","run","feeds","persona","plan","sharpen","virtual","decision","choice","decide","perspectives","outcome","me","debate","make","trade-offs",{"skillNo":181,"name":182,"nameZh":6,"description":183,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":101,"licenseFileUrl":184,"sourceHtmlUrl":185,"categories":186,"keywords":190,"publishedAt":44},"01M6R2FSR55AVRFA9YZ8SC2GZG","skill-creator","Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fskill-creator\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fskill-creator\u002FSKILL.md",[187],{"categoryNo":188,"categoryName":189,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[191,192,42,193,194,195,196,197,198,199,200,201,202,203,153,28,204,26,205],"skill creator","creator","create","update","capabilities","knowledge","tool","integrations","specialized","guide","new skill","existing skill","users","Existing","new",{"skillNo":207,"name":208,"nameZh":6,"description":209,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":210,"categories":211,"keywords":212,"publishedAt":44},"01M6R2FK42ZZ4HJTJVVK9WJBBP","ship-learn-next","Transform learning content (like YouTube transcripts, articles, tutorials) into actionable implementation plans using the Ship-Learn-Next framework. Use when user wants to turn advice, lessons, or educational content into concrete action steps, reps, or a learning quest.","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fship-learn-next\u002FSKILL.md",[],[213,214,215,216,217,218,219,220,123,221,222,223,224,225,226,227,228,229,230,26],"learn","ship learn next","ship","next","framework","learning","read","write","advice","implementation","youtube","plans","quest","transform","actionable","action","lessons","youtube transcripts",{"skillNo":232,"name":233,"nameZh":6,"description":234,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":101,"licenseFileUrl":235,"sourceHtmlUrl":236,"categories":237,"keywords":244,"publishedAt":44},"01M6R2088G1RHDA5MDX16HASRB","slack-gif-creator","Knowledge and utilities for creating animated GIFs optimized for Slack. Provides constraints, validation tools, and animation concepts. Use when users request animated GIFs for Slack like \"make me a GIF of X doing Y for Slack.\"","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fenterprise-communication\u002Fslack-gif-creator\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fenterprise-communication\u002Fslack-gif-creator\u002FSKILL.md",[238,241],{"categoryNo":239,"categoryName":240,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000005","邮件与沟通",{"categoryNo":242,"categoryName":243,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",[245,246,247,192,248,249,250,251,196,123,176,203,178,252,253,28,254,26],"slack gif creator","slack","gif","validation","Tools","constraints","Animation","Utilities","concepts","request"]