[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AFH4E6J4MWX86HTN6ZM2JP":3,"skill-same:01M6AFH4E6J4MWX86HTN6ZM2JP":108},{"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":45,"linkOnly":46,"markdownContent":47,"markdownBody":48,"frontMatter":49,"refName":50,"commitSha":51,"skillDirectory":52,"forksCount":53,"repositoryHtmlUrl":54,"repositoryHomepageUrl":55,"ownerHtmlUrl":56,"ownerBlogUrl":57,"ownerDescription":58,"updatedAt":59,"install":60,"resources":73},"01M6AFH4E6J4MWX86HTN6ZM2JP","omero-integration",null,"Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs\u002Fannotations, batch processing, for high-content screening and microscopy workflows.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33481,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fomero-integration\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44],"integration","omero integration","annotations","for","high","screening","processing","data management","via","platform","manage","datasets","images","python","analyze","access","data","data management platform","content","batch","workflows","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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Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[115],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[117,118,119,120,121,27,122,123,124,125,126,127,128,129,130,131,132,133,134,135],"scoring","pool","wealthville pool scoring","liquidity","which","exit","now","use","when","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":137,"name":138,"nameZh":6,"description":139,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":140,"categories":141,"keywords":143,"publishedAt":45},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[142],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[144,145,146,147,148,149,150,151,152,153,154,125,155,156,157,158,159,160,161,162],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":164,"name":165,"nameZh":6,"description":166,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":167,"categories":168,"keywords":178,"publishedAt":45},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[169,172,173],{"categoryNo":170,"categoryName":171,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":174,"categoryName":175,"taxonomyType":176,"taxonomyTypeDesc":177},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[179,180,181,182,183,184,185,27,186,187,188,189,190,191,192,124,39,193,125,194],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","database","query",{"skillNo":196,"name":197,"nameZh":6,"description":198,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":199,"licenseFileUrl":200,"sourceHtmlUrl":201,"categories":202,"keywords":206,"publishedAt":45},"01M6AFY6F7CC2BBZP1QD6VTNVC","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\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[203],{"categoryNo":204,"categoryName":205,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,27,229],"artifacts builder","web","web artifacts builder","artifacts","builder","state","routing","state management","component","css","frontend","ui","Tools","file","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":231,"name":232,"nameZh":6,"description":233,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":199,"licenseFileUrl":234,"sourceHtmlUrl":235,"categories":236,"keywords":241,"publishedAt":45},"01M6AFWF33SFV9Z3SF4PEEE4BF","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\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[237,240],{"categoryNo":238,"categoryName":239,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":204,"categoryName":205,"taxonomyType":21,"taxonomyTypeDesc":22},[242,243,244,245,246,208,247,217,218,248,249,250,251,27,252,127,253],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":255,"name":256,"nameZh":6,"description":257,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":199,"licenseFileUrl":258,"sourceHtmlUrl":259,"categories":260,"keywords":268,"publishedAt":45},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[261,264,265],{"categoryNo":262,"categoryName":263,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":266,"categoryName":267,"taxonomyType":176,"taxonomyTypeDesc":177},"01JBSEEDCAT000000000000036","内容创作者",[269,270,271,272,273,274,40,275,210,276,220,155,42,277,278,279,280,191,281,125,282,283,284,285,286,133,287],"discernment nudge","nudge","interpretation","help","run","reasoning","writing","lookup","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]