[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71G9MJSZMFV7FE93JWWV6GX":3,"skill-same:01M71G9MJSZMFV7FE93JWWV6GX":78},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"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},"01M71G9MJSZMFV7FE93JWWV6GX","matchms",null,"Mass spectrometry analysis. Process mzML\u002FMGF\u002FMSP, spectral similarity (cosine, modified cosine), metadata harmonization, compound ID, for metabolomics and MS data processing.","JSGforever\u002Fskillguard","JSGforever","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F85541948?v=4","01M71FQD3E132VMR5F4TAYX3YS","01M71FQD4DYW0XJ4XQ4YQH75Y9",2,"GPL-3.0","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__Microck--claude-scientific-skills\u002Fscientific-skills\u002Fmatchms\u002FSKILL.md",[],[5,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],"id","metadata","cosine","data","process","compound","data processing","similarity","metabolomics","harmonization","analysis","spectral","for","processing","ms","mass spectrometry","2026-11-10 10:12:30",false,"---\nname: matchms\ndescription: \"Mass spectrometry analysis. Process mzML\u002FMGF\u002FMSP, spectral similarity (cosine, modified cosine), metadata harmonization, compound ID, for metabolomics and MS data processing.\"\n---\n\n# Matchms\n\n## Overview\n\nMatchms is an open-source Python library for mass spectrometry data processing and analysis. Import spectra from various formats, standardize metadata, filter peaks, calculate spectral similarities, and build reproducible analytical workflows.\n\n## Core Capabilities\n\n### 1. Importing and Exporting Mass Spectrometry Data\n\nLoad spectra from multiple file formats and export processed data:\n\n```python\nfrom matchms.importing import load_from_mgf, load_from_mzml, load_from_msp, load_from_json\nfrom matchms.exporting import save_as_mgf, save_as_msp, save_as_json\n\n# Import spectra\nspectra = list(load_from_mgf(\"spectra.mgf\"))\nspectra = list(load_from_mzml(\"data.mzML\"))\nspectra = list(load_from_msp(\"library.msp\"))\n\n# Export processed spectra\nsave_as_mgf(spectra, \"output.mgf\")\nsave_as_json(spectra, \"output.json\")\n```\n\n**Supported formats:**\n- mzML and mzXML (raw mass spectrometry formats)\n- MGF (Mascot Generic Format)\n- MSP (spectral library format)\n- JSON (GNPS-compatible)\n- metabolomics-USI references\n- Pickle (Python serialization)\n\nFor detailed importing\u002Fexporting documentation, consult `references\u002Fimporting_exporting.md`.\n\n### 2. Spectrum Filtering and Processing\n\nApply comprehensive filters to standardize metadata and refine peak data:\n\n```python\nfrom matchms.filtering import default_filters, normalize_intensities\nfrom matchms.filtering import select_by_relative_intensity, require_minimum_number_of_peaks\n\n# Apply default metadata harmonization filters\nspectrum = default_filters(spectrum)\n\n# Normalize peak intensities\nspectrum = normalize_intensities(spectrum)\n\n# Filter peaks by relative intensity\nspectrum = select_by_relative_intensity(spectrum, intensity_from=0.01, intensity_to=1.0)\n\n# Require minimum peaks\nspectrum = require_minimum_number_of_peaks(spectrum, n_required=5)\n```\n\n**Filter categories:**\n- **Metadata processing**: Harmonize compound names, derive chemical structures, standardize adducts, correct charges\n- **Peak filtering**: Normalize intensities, select by m\u002Fz or intensity, remove precursor peaks\n- **Quality control**: Require minimum peaks, validate precursor m\u002Fz, ensure metadata completeness\n- **Chemical annotation**: Add fingerprints, derive InChI\u002FSMILES, repair structural mismatches\n\nMatchms provides 40+ filters. For the complete filter reference, consult `references\u002Ffiltering.md`.\n\n### 3. Calculating Spectral Similarities\n\nCompare spectra using various similarity metrics:\n\n```python\nfrom matchms import calculate_scores\nfrom matchms.similarity import CosineGreedy, ModifiedCosine, CosineHungarian\n\n# Calculate cosine similarity (fast, greedy algorithm)\nscores = calculate_scores(references=library_spectra,\n                         queries=query_spectra,\n                         similarity_function=CosineGreedy())\n\n# Calculate modified cosine (accounts for precursor m\u002Fz differences)\nscores = calculate_scores(references=library_spectra,\n                         queries=query_spectra,\n                         similarity_function=ModifiedCosine(tolerance=0.1))\n\n# Get best matches\nbest_matches = scores.scores_by_query(query_spectra[0], sort=True)[:10]\n```\n\n**Available similarity functions:**\n- **CosineGreedy\u002FCosineHungarian**: Peak-based cosine similarity with different matching algorithms\n- **ModifiedCosine**: Cosine similarity accounting for precursor mass differences\n- **NeutralLossesCosine**: Similarity based on neutral loss patterns\n- **FingerprintSimilarity**: Molecular structure similarity using fingerprints\n- **MetadataMatch**: Compare user-defined metadata fields\n- **PrecursorMzMatch\u002FParentMassMatch**: Simple mass-based filtering\n\nFor detailed similarity function documentation, consult `references\u002Fsimilarity.md`.\n\n### 4. Building Processing Pipelines\n\nCreate reproducible, multi-step analysis workflows:\n\n```python\nfrom matchms import SpectrumProcessor\nfrom matchms.filtering import default_filters, normalize_intensities\nfrom matchms.filtering import select_by_relative_intensity, remove_peaks_around_precursor_mz\n\n# Define a processing pipeline\nprocessor = SpectrumProcessor([\n    default_filters,\n    normalize_intensities,\n    lambda s: select_by_relative_intensity(s, intensity_from=0.01),\n    lambda s: remove_peaks_around_precursor_mz(s, mz_tolerance=17)\n])\n\n# Apply to all spectra\nprocessed_spectra = [processor(s) for s in spectra]\n```\n\n### 5. Working with Spectrum Objects\n\nThe core `Spectrum` class contains mass spectral data:\n\n```python\nfrom matchms import Spectrum\nimport numpy as np\n\n# Create a spectrum\nmz = np.array([100.0, 150.0, 200.0, 250.0])\nintensities = np.array([0.1, 0.5, 0.9, 0.3])\nmetadata = {\"precursor_mz\": 250.5, \"ionmode\": \"positive\"}\n\nspectrum = Spectrum(mz=mz, intensities=intensities, metadata=metadata)\n\n# Access spectrum properties\nprint(spectrum.peaks.mz)           # m\u002Fz values\nprint(spectrum.peaks.intensities)  # Intensity values\nprint(spectrum.get(\"precursor_mz\")) # Metadata field\n\n# Visualize spectra\nspectrum.plot()\nspectrum.plot_against(reference_spectrum)\n```\n\n### 6. Metadata Management\n\nStandardize and harmonize spectrum metadata:\n\n```python\n# Metadata is automatically harmonized\nspectrum.set(\"Precursor_mz\", 250.5)  # Gets harmonized to lowercase key\nprint(spectrum.get(\"precursor_mz\"))   # Returns 250.5\n\n# Derive chemical information\nfrom matchms.filtering import derive_inchi_from_smiles, derive_inchikey_from_inchi\nfrom matchms.filtering import add_fingerprint\n\nspectrum = derive_inchi_from_smiles(spectrum)\nspectrum = derive_inchikey_from_inchi(spectrum)\nspectrum = add_fingerprint(spectrum, fingerprint_type=\"morgan\", nbits=2048)\n```\n\n## Common Workflows\n\nFor typical mass spectrometry analysis workflows, including:\n- Loading and preprocessing spectral libraries\n- Matching unknown spectra against reference libraries\n- Quality filtering and data cleaning\n- Large-scale similarity comparisons\n- Network-based spectral clustering\n\nConsult `references\u002Fworkflows.md` for detailed examples.\n\n## Installation\n\n```bash\nuv pip install matchms\n```\n\nFor molecular structure processing (SMILES, InChI):\n```bash\nuv pip install matchms[chemistry]\n```\n\n## Reference Documentation\n\nDetailed reference documentation is available in the `references\u002F` directory:\n- `filtering.md` - Complete filter function reference with descriptions\n- `similarity.md` - All similarity metrics and when to use them\n- `importing_exporting.md` - File format details and I\u002FO operations\n- `workflows.md` - Common analysis patterns and examples\n\nLoad these references as needed for detailed information about specific matchms capabilities.\n","# Matchms\n\n## Overview\n\nMatchms is an open-source Python library for mass spectrometry data processing and analysis. Import spectra from various formats, standardize metadata, filter peaks, calculate spectral similarities, and build reproducible analytical workflows.\n\n## Core Capabilities\n\n### 1. Importing and Exporting Mass Spectrometry Data\n\nLoad spectra from multiple file formats and export processed data:\n\n```python\nfrom matchms.importing import load_from_mgf, load_from_mzml, load_from_msp, load_from_json\nfrom matchms.exporting import save_as_mgf, save_as_msp, save_as_json\n\n# Import spectra\nspectra = list(load_from_mgf(\"spectra.mgf\"))\nspectra = list(load_from_mzml(\"data.mzML\"))\nspectra = list(load_from_msp(\"library.msp\"))\n\n# Export processed spectra\nsave_as_mgf(spectra, \"output.mgf\")\nsave_as_json(spectra, \"output.json\")\n```\n\n**Supported formats:**\n- mzML and mzXML (raw mass spectrometry formats)\n- MGF (Mascot Generic Format)\n- MSP (spectral library format)\n- JSON (GNPS-compatible)\n- metabolomics-USI references\n- Pickle (Python serialization)\n\nFor detailed importing\u002Fexporting documentation, consult `references\u002Fimporting_exporting.md`.\n\n### 2. Spectrum Filtering and Processing\n\nApply comprehensive filters to standardize metadata and refine peak data:\n\n```python\nfrom matchms.filtering import default_filters, normalize_intensities\nfrom matchms.filtering import select_by_relative_intensity, require_minimum_number_of_peaks\n\n# Apply default metadata harmonization filters\nspectrum = default_filters(spectrum)\n\n# Normalize peak intensities\nspectrum = normalize_intensities(spectrum)\n\n# Filter peaks by relative intensity\nspectrum = select_by_relative_intensity(spectrum, intensity_from=0.01, intensity_to=1.0)\n\n# Require minimum peaks\nspectrum = require_minimum_number_of_peaks(spectrum, n_required=5)\n```\n\n**Filter categories:**\n- **Metadata processing**: Harmonize compound names, derive chemical structures, standardize adducts, correct charges\n- **Peak filtering**: Normalize intensities, select by m\u002Fz or intensity, remove precursor peaks\n- **Quality control**: Require minimum peaks, validate precursor m\u002Fz, ensure metadata completeness\n- **Chemical annotation**: Add fingerprints, derive InChI\u002FSMILES, repair structural mismatches\n\nMatchms provides 40+ filters. For the complete filter reference, consult `references\u002Ffiltering.md`.\n\n### 3. Calculating Spectral Similarities\n\nCompare spectra using various similarity metrics:\n\n```python\nfrom matchms import calculate_scores\nfrom matchms.similarity import CosineGreedy, ModifiedCosine, CosineHungarian\n\n# Calculate cosine similarity (fast, greedy algorithm)\nscores = calculate_scores(references=library_spectra,\n                         queries=query_spectra,\n                         similarity_function=CosineGreedy())\n\n# Calculate modified cosine (accounts for precursor m\u002Fz differences)\nscores = calculate_scores(references=library_spectra,\n                         queries=query_spectra,\n                         similarity_function=ModifiedCosine(tolerance=0.1))\n\n# Get best matches\nbest_matches = scores.scores_by_query(query_spectra[0], sort=True)[:10]\n```\n\n**Available similarity functions:**\n- **CosineGreedy\u002FCosineHungarian**: Peak-based cosine similarity with different matching algorithms\n- **ModifiedCosine**: Cosine similarity accounting for precursor mass differences\n- **NeutralLossesCosine**: Similarity based on neutral loss patterns\n- **FingerprintSimilarity**: Molecular structure similarity using fingerprints\n- **MetadataMatch**: Compare user-defined metadata fields\n- **PrecursorMzMatch\u002FParentMassMatch**: Simple mass-based filtering\n\nFor detailed similarity function documentation, consult `references\u002Fsimilarity.md`.\n\n### 4. Building Processing Pipelines\n\nCreate reproducible, multi-step analysis workflows:\n\n```python\nfrom matchms import SpectrumProcessor\nfrom matchms.filtering import default_filters, normalize_intensities\nfrom matchms.filtering import select_by_relative_intensity, remove_peaks_around_precursor_mz\n\n# Define a processing pipeline\nprocessor = SpectrumProcessor([\n    default_filters,\n    normalize_intensities,\n    lambda s: select_by_relative_intensity(s, intensity_from=0.01),\n    lambda s: remove_peaks_around_precursor_mz(s, mz_tolerance=17)\n])\n\n# Apply to all spectra\nprocessed_spectra = [processor(s) for s in spectra]\n```\n\n### 5. Working with Spectrum Objects\n\nThe core `Spectrum` class contains mass spectral data:\n\n```python\nfrom matchms import Spectrum\nimport numpy as np\n\n# Create a spectrum\nmz = np.array([100.0, 150.0, 200.0, 250.0])\nintensities = np.array([0.1, 0.5, 0.9, 0.3])\nmetadata = {\"precursor_mz\": 250.5, \"ionmode\": \"positive\"}\n\nspectrum = Spectrum(mz=mz, intensities=intensities, metadata=metadata)\n\n# Access spectrum properties\nprint(spectrum.peaks.mz)           # m\u002Fz values\nprint(spectrum.peaks.intensities)  # Intensity values\nprint(spectrum.get(\"precursor_mz\")) # Metadata field\n\n# Visualize spectra\nspectrum.plot()\nspectrum.plot_against(reference_spectrum)\n```\n\n### 6. Metadata Management\n\nStandardize and harmonize spectrum metadata:\n\n```python\n# Metadata is automatically harmonized\nspectrum.set(\"Precursor_mz\", 250.5)  # Gets harmonized to lowercase key\nprint(spectrum.get(\"precursor_mz\"))   # Returns 250.5\n\n# Derive chemical information\nfrom matchms.filtering import derive_inchi_from_smiles, derive_inchikey_from_inchi\nfrom matchms.filtering import add_fingerprint\n\nspectrum = derive_inchi_from_smiles(spectrum)\nspectrum = derive_inchikey_from_inchi(spectrum)\nspectrum = add_fingerprint(spectrum, fingerprint_type=\"morgan\", nbits=2048)\n```\n\n## Common Workflows\n\nFor typical mass spectrometry analysis workflows, including:\n- Loading and preprocessing spectral libraries\n- Matching unknown spectra against reference libraries\n- Quality filtering and data cleaning\n- Large-scale similarity comparisons\n- Network-based spectral clustering\n\nConsult `references\u002Fworkflows.md` for detailed examples.\n\n## Installation\n\n```bash\nuv pip install matchms\n```\n\nFor molecular structure processing (SMILES, InChI):\n```bash\nuv pip install matchms[chemistry]\n```\n\n## Reference Documentation\n\nDetailed reference documentation is available in the `references\u002F` directory:\n- `filtering.md` - Complete filter function reference with descriptions\n- `similarity.md` - All similarity metrics and when to use them\n- `importing_exporting.md` - File format details and I\u002FO operations\n- `workflows.md` - Common analysis patterns and examples\n\nLoad these references as needed for detailed information about specific matchms capabilities.\n",{"name":5,"description":7},"main","f2ec4b3b9133d34eeaf9b38e4b0a97e8e5705a7f","benchmarks\u002Ffinal\u002Fskills\u002Fasset1__Microck--claude-scientific-skills\u002Fscientific-skills\u002Fmatchms",0,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard","https:\u002F\u002Fgithub.com\u002FJSGforever","2026-08-29 18:27:56",{"source":8,"skillName":5,"commands":47,"prompt":57,"zipAvailable":58},[48,51,54],{"tool":49,"command":50},"npx","npx skills add JSGforever\u002Fskillguard --skill matchms",{"tool":52,"command":53},"bunx","bunx skills add JSGforever\u002Fskillguard --skill matchms",{"tool":55,"command":56},"pnpm","pnpm dlx skills add JSGforever\u002Fskillguard --skill matchms","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__Microck--claude-scientific-skills\u002Fscientific-skills\u002Fmatchms\u002FSKILL.md",true,[60,66,70,74],{"path":61,"resourceType":62,"resourceTypeDesc":63,"sizeBytes":64,"sourceHtmlUrl":65},"references\u002Ffiltering.md","REFERENCE","参考文档",10038,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__Microck--claude-scientific-skills\u002Fscientific-skills\u002Fmatchms\u002Freferences\u002Ffiltering.md",{"path":67,"resourceType":62,"resourceTypeDesc":63,"sizeBytes":68,"sourceHtmlUrl":69},"references\u002Fimporting_exporting.md",10894,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__Microck--claude-scientific-skills\u002Fscientific-skills\u002Fmatchms\u002Freferences\u002Fimporting_exporting.md",{"path":71,"resourceType":62,"resourceTypeDesc":63,"sizeBytes":72,"sourceHtmlUrl":73},"references\u002Fsimilarity.md",12974,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__Microck--claude-scientific-skills\u002Fscientific-skills\u002Fmatchms\u002Freferences\u002Fsimilarity.md",{"path":75,"resourceType":62,"resourceTypeDesc":63,"sizeBytes":76,"sourceHtmlUrl":77},"references\u002Fworkflows.md",22230,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__Microck--claude-scientific-skills\u002Fscientific-skills\u002Fmatchms\u002Freferences\u002Fworkflows.md",[79,111,126,153,175,195],{"skillNo":80,"name":81,"nameZh":6,"description":82,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":83,"categories":84,"keywords":90,"publishedAt":34},"01M71HY2SNXBA9Q00WZDTZQ31Z","skillguard-codex-scanner","Hybrid deterministic+LLM agent-skill security scanner. 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NOT for simple chat, request-response APIs, or single-user apps.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--real-time-collaboration-engine\u002FSKILL.md",[],[133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152],"time","real time collaboration engine","real time collaboration","collaboration","engine","real time","Video","reliability","video editing","performance","conflict resolution","presence","access","apps","updates","Bash","activate","optimizer","react","streaming",{"skillNo":154,"name":155,"nameZh":6,"description":156,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":157,"categories":158,"keywords":162,"publishedAt":34},"01M71HXP6CTY4KFN0QVMMN0V7J","react-performance-optimizer","Optimize React apps for 60fps performance. 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NOT for backend optimization, non-React frameworks, or premature optimization.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--react-performance-optimizer\u002FSKILL.md",[159],{"categoryNo":160,"categoryName":161,"taxonomyType":88,"taxonomyTypeDesc":89},"01JBSEEDCAT000000000000009","前端开发",[142,163,150,151,164,165,166,167,168,169,133,170,171,146,148,172,149,152,173,174],"react performance optimizer","react performance","performance optimizer","app","nextjs","expert","re","render","2025","code","router","strategies",{"skillNo":176,"name":177,"nameZh":6,"description":178,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":179,"categories":180,"keywords":182,"publishedAt":34},"01M71HXH7QXSQB4QTDZ7SP7YXQ","llm-streaming-response-handler","Build production LLM streaming UIs with Server-Sent Events, real-time token display, cancellation, error recovery. 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NOT for batch processing, non-streaming APIs, or WebSocket bidirectional chat.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--llm-streaming-response-handler\u002FSKILL.md",[181],{"categoryNo":160,"categoryName":161,"taxonomyType":88,"taxonomyTypeDesc":89},[183,152,184,185,31,186,142,133,187,188,189,148,149,150,190,151,191,192,193,194],"llm streaming response handler","handler","error","retry","chat ui","error recovery","critical","batch","display","text","server","read",{"skillNo":196,"name":197,"nameZh":6,"description":198,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":199,"categories":200,"keywords":208,"publishedAt":34},"01M71HXCB0E1YRKYW1Y8T0Y106","github-actions-pipeline-builder","Build production CI\u002FCD pipelines with GitHub Actions. 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NOT for Jenkins\u002FCircleCI, manual deployments, or non-GitHub repositories.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--github-actions-pipeline-builder\u002FSKILL.md",[201,204,207],{"categoryNo":202,"categoryName":203,"taxonomyType":88,"taxonomyTypeDesc":89},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":205,"categoryName":206,"taxonomyType":88,"taxonomyTypeDesc":89},"01JBSEEDCAT000000000000011","运维与部署",{"categoryNo":86,"categoryName":87,"taxonomyType":88,"taxonomyTypeDesc":89},[209,210,211,212,213,214,215,140,168,216,217,218,149,174,219,194,220,221,222,223],"github actions pipeline builder","actions","github actions","pipeline builder","github","builder","pipeline","testing","ci","workflows","run","release","write","edit","git"]