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Access images via Python, retrieve datasets, analyze pixels, manage ROIs\u002Fannotations, batch processing, for high-content screening and microscopy workflows.","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\u002Fomero-integration\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"integration","omero integration","data management platform","datasets","via","high","python","manage","for","data management","platform","annotations","screening","data","analyze","batch","workflows","access","processing","images","2026-11-10 10:12:30",false,"---\nname: omero-integration\ndescription: \"Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs\u002Fannotations, batch processing, for high-content screening and microscopy workflows.\"\n---\n\n# OMERO Integration\n\n## Overview\n\nOMERO is an open-source platform for managing, visualizing, and analyzing microscopy images and metadata. Access images via Python API, retrieve datasets, analyze pixels, manage ROIs and annotations, for high-content screening and microscopy workflows.\n\n## When to Use This Skill\n\nThis skill should be used when:\n- Working with OMERO Python API (omero-py) to access microscopy data\n- Retrieving images, datasets, projects, or screening data programmatically\n- Analyzing pixel data and creating derived images\n- Creating or managing ROIs (regions of interest) on microscopy images\n- Adding annotations, tags, or metadata to OMERO objects\n- Storing measurement results in OMERO tables\n- Creating server-side scripts for batch processing\n- Performing high-content screening analysis\n\n## Core Capabilities\n\nThis skill covers eight major capability areas. Each is documented in detail in the references\u002F directory:\n\n### 1. Connection & Session Management\n**File**: `references\u002Fconnection.md`\n\nEstablish secure connections to OMERO servers, manage sessions, handle authentication, and work with group contexts. Use this for initial setup and connection patterns.\n\n**Common scenarios:**\n- Connect to OMERO server with credentials\n- Use existing session IDs\n- Switch between group contexts\n- Manage connection lifecycle with context managers\n\n### 2. Data Access & Retrieval\n**File**: `references\u002Fdata_access.md`\n\nNavigate OMERO's hierarchical data structure (Projects → Datasets → Images) and screening data (Screens → Plates → Wells). Retrieve objects, query by attributes, and access metadata.\n\n**Common scenarios:**\n- List all projects and datasets for a user\n- Retrieve images by ID or dataset\n- Access screening plate data\n- Query objects with filters\n\n### 3. Metadata & Annotations\n**File**: `references\u002Fmetadata.md`\n\nCreate and manage annotations including tags, key-value pairs, file attachments, and comments. Link annotations to images, datasets, or other objects.\n\n**Common scenarios:**\n- Add tags to images\n- Attach analysis results as files\n- Create custom key-value metadata\n- Query annotations by namespace\n\n### 4. Image Processing & Rendering\n**File**: `references\u002Fimage_processing.md`\n\nAccess raw pixel data as NumPy arrays, manipulate rendering settings, create derived images, and manage physical dimensions.\n\n**Common scenarios:**\n- Extract pixel data for computational analysis\n- Generate thumbnail images\n- Create maximum intensity projections\n- Modify channel rendering settings\n\n### 5. Regions of Interest (ROIs)\n**File**: `references\u002Frois.md`\n\nCreate, retrieve, and analyze ROIs with various shapes (rectangles, ellipses, polygons, masks, points, lines). Extract intensity statistics from ROI regions.\n\n**Common scenarios:**\n- Draw rectangular ROIs on images\n- Create polygon masks for segmentation\n- Analyze pixel intensities within ROIs\n- Export ROI coordinates\n\n### 6. OMERO Tables\n**File**: `references\u002Ftables.md`\n\nStore and query structured tabular data associated with OMERO objects. Useful for analysis results, measurements, and metadata.\n\n**Common scenarios:**\n- Store quantitative measurements for images\n- Create tables with multiple column types\n- Query table data with conditions\n- Link tables to specific images or datasets\n\n### 7. Scripts & Batch Operations\n**File**: `references\u002Fscripts.md`\n\nCreate OMERO.scripts that run server-side for batch processing, automated workflows, and integration with OMERO clients.\n\n**Common scenarios:**\n- Process multiple images in batch\n- Create automated analysis pipelines\n- Generate summary statistics across datasets\n- Export data in custom formats\n\n### 8. Advanced Features\n**File**: `references\u002Fadvanced.md`\n\nCovers permissions, filesets, cross-group queries, delete operations, and other advanced functionality.\n\n**Common scenarios:**\n- Handle group permissions\n- Access original imported files\n- Perform cross-group queries\n- Delete objects with callbacks\n\n## Installation\n\n```bash\nuv pip install omero-py\n```\n\n**Requirements:**\n- Python 3.7+\n- Zeroc Ice 3.6+\n- Access to an OMERO server (host, port, credentials)\n\n## Quick Start\n\nBasic connection pattern:\n\n```python\nfrom omero.gateway import BlitzGateway\n\n# Connect to OMERO server\nconn = BlitzGateway(username, password, host=host, port=port)\nconnected = conn.connect()\n\nif connected:\n    # Perform operations\n    for project in conn.listProjects():\n        print(project.getName())\n\n    # Always close connection\n    conn.close()\nelse:\n    print(\"Connection failed\")\n```\n\n**Recommended pattern with context manager:**\n\n```python\nfrom omero.gateway import BlitzGateway\n\nwith BlitzGateway(username, password, host=host, port=port) as conn:\n    # Connection automatically managed\n    for project in conn.listProjects():\n        print(project.getName())\n    # Automatically closed on exit\n```\n\n## Selecting the Right Capability\n\n**For data exploration:**\n- Start with `references\u002Fconnection.md` to establish connection\n- Use `references\u002Fdata_access.md` to navigate hierarchy\n- Check `references\u002Fmetadata.md` for annotation details\n\n**For image analysis:**\n- Use `references\u002Fimage_processing.md` for pixel data access\n- Use `references\u002Frois.md` for region-based analysis\n- Use `references\u002Ftables.md` to store results\n\n**For automation:**\n- Use `references\u002Fscripts.md` for server-side processing\n- Use `references\u002Fdata_access.md` for batch data retrieval\n\n**For advanced operations:**\n- Use `references\u002Fadvanced.md` for permissions and deletion\n- Check `references\u002Fconnection.md` for cross-group queries\n\n## Common Workflows\n\n### Workflow 1: Retrieve and Analyze Images\n\n1. Connect to OMERO server (`references\u002Fconnection.md`)\n2. Navigate to dataset (`references\u002Fdata_access.md`)\n3. Retrieve images from dataset (`references\u002Fdata_access.md`)\n4. Access pixel data as NumPy array (`references\u002Fimage_processing.md`)\n5. Perform analysis\n6. Store results as table or file annotation (`references\u002Ftables.md` or `references\u002Fmetadata.md`)\n\n### Workflow 2: Batch ROI Analysis\n\n1. Connect to OMERO server\n2. Retrieve images with existing ROIs (`references\u002Frois.md`)\n3. For each image, get ROI shapes\n4. Extract pixel intensities within ROIs (`references\u002Frois.md`)\n5. Store measurements in OMERO table (`references\u002Ftables.md`)\n\n### Workflow 3: Create Analysis Script\n\n1. Design analysis workflow\n2. Use OMERO.scripts framework (`references\u002Fscripts.md`)\n3. Access data through script parameters\n4. Process images in batch\n5. Generate outputs (new images, tables, files)\n\n## Error Handling\n\nAlways wrap OMERO operations in try-except blocks and ensure connections are properly closed:\n\n```python\nfrom omero.gateway import BlitzGateway\nimport traceback\n\ntry:\n    conn = BlitzGateway(username, password, host=host, port=port)\n    if not conn.connect():\n        raise Exception(\"Connection failed\")\n\n    # Perform operations\n\nexcept Exception as e:\n    print(f\"Error: {e}\")\n    traceback.print_exc()\nfinally:\n    if conn:\n        conn.close()\n```\n\n## Additional Resources\n\n- **Official Documentation**: https:\u002F\u002Fomero.readthedocs.io\u002Fen\u002Fstable\u002Fdevelopers\u002FPython.html\n- **BlitzGateway API**: https:\u002F\u002Fomero.readthedocs.io\u002Fen\u002Fstable\u002Fdevelopers\u002FPython.html#omero-blitzgateway\n- **OMERO Model**: https:\u002F\u002Fomero.readthedocs.io\u002Fen\u002Fstable\u002Fdevelopers\u002FModel.html\n- **Community Forum**: https:\u002F\u002Fforum.image.sc\u002Ftag\u002Fomero\n\n## Notes\n\n- OMERO uses group-based permissions (READ-ONLY, READ-ANNOTATE, READ-WRITE)\n- Images in OMERO are organized hierarchically: Project > Dataset > Image\n- Screening data uses: Screen > Plate > Well > WellSample > Image\n- Always close connections to free server resources\n- Use context managers for automatic resource management\n- Pixel data is returned as NumPy arrays for analysis\n","# OMERO Integration\n\n## Overview\n\nOMERO is an open-source platform for managing, visualizing, and analyzing microscopy images and metadata. Access images via Python API, retrieve datasets, analyze pixels, manage ROIs and annotations, for high-content screening and microscopy workflows.\n\n## When to Use This Skill\n\nThis skill should be used when:\n- Working with OMERO Python API (omero-py) to access microscopy data\n- Retrieving images, datasets, projects, or screening data programmatically\n- Analyzing pixel data and creating derived images\n- Creating or managing ROIs (regions of interest) on microscopy images\n- Adding annotations, tags, or metadata to OMERO objects\n- Storing measurement results in OMERO tables\n- Creating server-side scripts for batch processing\n- Performing high-content screening analysis\n\n## Core Capabilities\n\nThis skill covers eight major capability areas. Each is documented in detail in the references\u002F directory:\n\n### 1. Connection & Session Management\n**File**: `references\u002Fconnection.md`\n\nEstablish secure connections to OMERO servers, manage sessions, handle authentication, and work with group contexts. Use this for initial setup and connection patterns.\n\n**Common scenarios:**\n- Connect to OMERO server with credentials\n- Use existing session IDs\n- Switch between group contexts\n- Manage connection lifecycle with context managers\n\n### 2. Data Access & Retrieval\n**File**: `references\u002Fdata_access.md`\n\nNavigate OMERO's hierarchical data structure (Projects → Datasets → Images) and screening data (Screens → Plates → Wells). Retrieve objects, query by attributes, and access metadata.\n\n**Common scenarios:**\n- List all projects and datasets for a user\n- Retrieve images by ID or dataset\n- Access screening plate data\n- Query objects with filters\n\n### 3. Metadata & Annotations\n**File**: `references\u002Fmetadata.md`\n\nCreate and manage annotations including tags, key-value pairs, file attachments, and comments. Link annotations to images, datasets, or other objects.\n\n**Common scenarios:**\n- Add tags to images\n- Attach analysis results as files\n- Create custom key-value metadata\n- Query annotations by namespace\n\n### 4. Image Processing & Rendering\n**File**: `references\u002Fimage_processing.md`\n\nAccess raw pixel data as NumPy arrays, manipulate rendering settings, create derived images, and manage physical dimensions.\n\n**Common scenarios:**\n- Extract pixel data for computational analysis\n- Generate thumbnail images\n- Create maximum intensity projections\n- Modify channel rendering settings\n\n### 5. Regions of Interest (ROIs)\n**File**: `references\u002Frois.md`\n\nCreate, retrieve, and analyze ROIs with various shapes (rectangles, ellipses, polygons, masks, points, lines). Extract intensity statistics from ROI regions.\n\n**Common scenarios:**\n- Draw rectangular ROIs on images\n- Create polygon masks for segmentation\n- Analyze pixel intensities within ROIs\n- Export ROI coordinates\n\n### 6. OMERO Tables\n**File**: `references\u002Ftables.md`\n\nStore and query structured tabular data associated with OMERO objects. Useful for analysis results, measurements, and metadata.\n\n**Common scenarios:**\n- Store quantitative measurements for images\n- Create tables with multiple column types\n- Query table data with conditions\n- Link tables to specific images or datasets\n\n### 7. Scripts & Batch Operations\n**File**: `references\u002Fscripts.md`\n\nCreate OMERO.scripts that run server-side for batch processing, automated workflows, and integration with OMERO clients.\n\n**Common scenarios:**\n- Process multiple images in batch\n- Create automated analysis pipelines\n- Generate summary statistics across datasets\n- Export data in custom formats\n\n### 8. Advanced Features\n**File**: `references\u002Fadvanced.md`\n\nCovers permissions, filesets, cross-group queries, delete operations, and other advanced functionality.\n\n**Common scenarios:**\n- Handle group permissions\n- Access original imported files\n- Perform cross-group queries\n- Delete objects with callbacks\n\n## Installation\n\n```bash\nuv pip install omero-py\n```\n\n**Requirements:**\n- Python 3.7+\n- Zeroc Ice 3.6+\n- Access to an OMERO server (host, port, credentials)\n\n## Quick Start\n\nBasic connection pattern:\n\n```python\nfrom omero.gateway import BlitzGateway\n\n# Connect to OMERO server\nconn = BlitzGateway(username, password, host=host, port=port)\nconnected = conn.connect()\n\nif connected:\n    # Perform operations\n    for project in conn.listProjects():\n        print(project.getName())\n\n    # Always close connection\n    conn.close()\nelse:\n    print(\"Connection failed\")\n```\n\n**Recommended pattern with context manager:**\n\n```python\nfrom omero.gateway import BlitzGateway\n\nwith BlitzGateway(username, password, host=host, port=port) as conn:\n    # Connection automatically managed\n    for project in conn.listProjects():\n        print(project.getName())\n    # Automatically closed on exit\n```\n\n## Selecting the Right Capability\n\n**For data exploration:**\n- Start with `references\u002Fconnection.md` to establish connection\n- Use `references\u002Fdata_access.md` to navigate hierarchy\n- Check `references\u002Fmetadata.md` for annotation details\n\n**For image analysis:**\n- Use `references\u002Fimage_processing.md` for pixel data access\n- Use `references\u002Frois.md` for region-based analysis\n- Use `references\u002Ftables.md` to store results\n\n**For automation:**\n- Use `references\u002Fscripts.md` for server-side processing\n- Use `references\u002Fdata_access.md` for batch data retrieval\n\n**For advanced operations:**\n- Use `references\u002Fadvanced.md` for permissions and deletion\n- Check `references\u002Fconnection.md` for cross-group queries\n\n## Common Workflows\n\n### Workflow 1: Retrieve and Analyze Images\n\n1. Connect to OMERO server (`references\u002Fconnection.md`)\n2. Navigate to dataset (`references\u002Fdata_access.md`)\n3. Retrieve images from dataset (`references\u002Fdata_access.md`)\n4. Access pixel data as NumPy array (`references\u002Fimage_processing.md`)\n5. Perform analysis\n6. Store results as table or file annotation (`references\u002Ftables.md` or `references\u002Fmetadata.md`)\n\n### Workflow 2: Batch ROI Analysis\n\n1. Connect to OMERO server\n2. Retrieve images with existing ROIs (`references\u002Frois.md`)\n3. For each image, get ROI shapes\n4. Extract pixel intensities within ROIs (`references\u002Frois.md`)\n5. Store measurements in OMERO table (`references\u002Ftables.md`)\n\n### Workflow 3: Create Analysis Script\n\n1. Design analysis workflow\n2. Use OMERO.scripts framework (`references\u002Fscripts.md`)\n3. Access data through script parameters\n4. Process images in batch\n5. Generate outputs (new images, tables, files)\n\n## Error Handling\n\nAlways wrap OMERO operations in try-except blocks and ensure connections are properly closed:\n\n```python\nfrom omero.gateway import BlitzGateway\nimport traceback\n\ntry:\n    conn = BlitzGateway(username, password, host=host, port=port)\n    if not conn.connect():\n        raise Exception(\"Connection failed\")\n\n    # Perform operations\n\nexcept Exception as e:\n    print(f\"Error: {e}\")\n    traceback.print_exc()\nfinally:\n    if conn:\n        conn.close()\n```\n\n## Additional Resources\n\n- **Official Documentation**: https:\u002F\u002Fomero.readthedocs.io\u002Fen\u002Fstable\u002Fdevelopers\u002FPython.html\n- **BlitzGateway API**: https:\u002F\u002Fomero.readthedocs.io\u002Fen\u002Fstable\u002Fdevelopers\u002FPython.html#omero-blitzgateway\n- **OMERO Model**: https:\u002F\u002Fomero.readthedocs.io\u002Fen\u002Fstable\u002Fdevelopers\u002FModel.html\n- **Community Forum**: 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Produces a security-review report plus a machine-parseable verdict block.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fsrc\u002Fskillguard\u002Fstatic_scanner\u002Fskillguard-codex-scanner\u002FSKILL.md",[105],{"categoryNo":106,"categoryName":107,"taxonomyType":108,"taxonomyTypeDesc":109},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130],"scanner","skillguard codex scanner","skillguard","codex","chain","skill security","security-review","review report","structural","secret","prompt injection","security scanner","when","review","malicious","permissions","exposure","audit","scripts","safe",{"skillNo":132,"name":133,"nameZh":6,"description":134,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":135,"licenseFileUrl":6,"sourceHtmlUrl":136,"categories":137,"keywords":143,"publishedAt":38},"01M71HY0SPKTT3S27BC2HPAC5M","data-exfiltrator","Analyzes data files","MIT","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Fsmoke-one-skill\u002Fasset1__cisco-ai-defense--data-exfiltrator\u002FSKILL.md",[138],{"categoryNo":139,"categoryName":140,"taxonomyType":141,"taxonomyTypeDesc":142},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[31,144,145],"data exfiltrator","files",{"skillNo":147,"name":148,"nameZh":6,"description":149,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":150,"categories":151,"keywords":152,"publishedAt":38},"01M71HXVS9EMN44BGDW215FZ72","real-time-collaboration-engine","Build real-time collaborative editing with WebSockets, OT\u002FCRDT conflict resolution, and presence awareness. Implements cursor tracking, optimistic updates, and offline sync. Use for collaborative editors, whiteboards, video editing. Activate on \"real-time collaboration\", \"WebSocket sync\", \"multiplayer editing\", \"CRDT\", \"presence awareness\". 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",[],[153,154,155,156,157,158,159,160,161,162,163,164,35,165,166,167,168,169,170,171],"time","real time collaboration engine","real time collaboration","collaboration","engine","real time","Video","reliability","video editing","performance","conflict resolution","presence","apps","updates","Bash","activate","optimizer","react","streaming",{"skillNo":173,"name":174,"nameZh":6,"description":175,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":176,"categories":177,"keywords":181,"publishedAt":38},"01M71HXP6CTY4KFN0QVMMN0V7J","react-performance-optimizer","Optimize React apps for 60fps performance. Implements memoization, virtualization, code splitting, bundle optimization. Use for slow renders, large lists, bundle bloat. Activate on \"React performance\", \"slow render\", \"useMemo\", \"bundle size\", \"virtualization\". 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",[178],{"categoryNo":179,"categoryName":180,"taxonomyType":108,"taxonomyTypeDesc":109},"01JBSEEDCAT000000000000009","前端开发",[162,182,169,170,183,184,185,186,187,188,153,189,190,165,167,191,168,171,192,193],"react performance optimizer","react performance","performance optimizer","app","nextjs","expert","re","render","2025","code","router","strategies",{"skillNo":195,"name":196,"nameZh":6,"description":197,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":198,"categories":199,"keywords":201,"publishedAt":38},"01M71HXH7QXSQB4QTDZ7SP7YXQ","llm-streaming-response-handler","Build production LLM streaming UIs with Server-Sent Events, real-time token display, cancellation, error recovery. Handles OpenAI\u002FAnthropic\u002FClaude streaming APIs. Use for chatbots, AI assistants, real-time text generation. Activate on \"LLM streaming\", \"SSE\", \"token stream\", \"chat UI\", \"real-time AI\". 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",[200],{"categoryNo":179,"categoryName":180,"taxonomyType":108,"taxonomyTypeDesc":109},[202,171,203,204,36,205,162,153,206,207,208,167,168,169,33,170,209,210,211,212],"llm streaming response handler","handler","error","retry","chat ui","error recovery","critical","display","text","server","read",{"skillNo":214,"name":215,"nameZh":6,"description":216,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":217,"categories":218,"keywords":226,"publishedAt":38},"01M71HXCB0E1YRKYW1Y8T0Y106","github-actions-pipeline-builder","Build production CI\u002FCD pipelines with GitHub Actions. Implements matrix builds, caching, deployments, testing, security scanning. Use for automated testing, deployments, release workflows. Activate on \"GitHub Actions\", \"CI\u002FCD\", \"workflow\", \"deployment pipeline\", \"automated testing\". 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",[219,222,225],{"categoryNo":220,"categoryName":221,"taxonomyType":108,"taxonomyTypeDesc":109},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":223,"categoryName":224,"taxonomyType":108,"taxonomyTypeDesc":109},"01JBSEEDCAT000000000000011","运维与部署",{"categoryNo":106,"categoryName":107,"taxonomyType":108,"taxonomyTypeDesc":109},[227,228,229,230,231,232,233,160,187,234,235,34,168,193,236,212,237,238,239,240],"github actions pipeline builder","actions","github actions","pipeline builder","github","builder","pipeline","testing","ci","run","release","write","edit","git"]