[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71B835XBAXKCCAJTNHX43F3":3,"skill-same:01M71B835XBAXKCCAJTNHX43F3":81},{"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":54,"commitSha":55,"skillDirectory":56,"forksCount":57,"repositoryHtmlUrl":58,"repositoryHomepageUrl":6,"ownerHtmlUrl":59,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":60,"install":61,"resources":74},"01M71B835XBAXKCCAJTNHX43F3","azure-ai-vision-imageanalysis-py",null,"Azure AI Vision Image Analysis SDK for captions, tags, objects, OCR, people detection, and smart cropping. Use for computer vision and image understanding tasks.\nTriggers: \"image analysis\", \"computer vision\", \"OCR\", \"object detection\", \"ImageAnalysisClient\", \"image caption\".","strmt7\u002Fai_skills_collection_benchmarked","strmt7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F28989754?v=4","01M71929P914SWKQ7QJV24NBBY","Stratos","01M71929Q9VGKQ7PWYR95VSQQQ",1,"MIT","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Fcloud-azure-microsoft-sdks\u002Fofficial-vendor-reference\u002Fazure-ai-vision-imageanalysis-py\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体","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],"azure ai","azure ai vision","vision","azure","azure ai vision imageanalysis py","objects","for","object","image understanding","image","people","tags","image analysis","triggers","use","Captions","analysis","ocr","Computer Vision","tasks","2026-11-10 10:12:30",false,"---\nname: azure-ai-vision-imageanalysis-py\ndescription: |\n  Azure AI Vision Image Analysis SDK for captions, tags, objects, OCR, people detection, and smart cropping. Use for computer vision and image understanding tasks.\n  Triggers: \"image analysis\", \"computer vision\", \"OCR\", \"object detection\", \"ImageAnalysisClient\", \"image caption\".\nlicense: MIT\nmetadata:\n  author: Microsoft\n  version: \"1.0.0\"\n  package: azure-ai-vision-imageanalysis\n---\n\n# Azure AI Vision Image Analysis SDK for Python\n\nClient library for Azure AI Vision 4.0 image analysis including captions, tags, objects, OCR, and more.\n\n## Installation\n\n```bash\npip install azure-ai-vision-imageanalysis\n```\n\n## Environment Variables\n\n```bash\nVISION_ENDPOINT=https:\u002F\u002F\u003Cresource>.cognitiveservices.azure.com\nVISION_KEY=\u003Cyour-api-key>  # If using API key\n```\n\n## Authentication\n\n### API Key\n\n```python\nimport os\nfrom azure.ai.vision.imageanalysis import ImageAnalysisClient\nfrom azure.core.credentials import AzureKeyCredential\n\nendpoint = os.environ[\"VISION_ENDPOINT\"]\nkey = os.environ[\"VISION_KEY\"]\n\nclient = ImageAnalysisClient(\n    endpoint=endpoint,\n    credential=AzureKeyCredential(key)\n)\n```\n\n### Entra ID (Recommended)\n\n```python\nfrom azure.ai.vision.imageanalysis import ImageAnalysisClient\nfrom azure.identity import DefaultAzureCredential\n\nclient = ImageAnalysisClient(\n    endpoint=os.environ[\"VISION_ENDPOINT\"],\n    credential=DefaultAzureCredential()\n)\n```\n\n## Analyze Image from URL\n\n```python\nfrom azure.ai.vision.imageanalysis.models import VisualFeatures\n\nimage_url = \"https:\u002F\u002Fexample.com\u002Fimage.jpg\"\n\nresult = client.analyze_from_url(\n    image_url=image_url,\n    visual_features=[\n        VisualFeatures.CAPTION,\n        VisualFeatures.TAGS,\n        VisualFeatures.OBJECTS,\n        VisualFeatures.READ,\n        VisualFeatures.PEOPLE,\n        VisualFeatures.SMART_CROPS,\n        VisualFeatures.DENSE_CAPTIONS\n    ],\n    gender_neutral_caption=True,\n    language=\"en\"\n)\n```\n\n## Analyze Image from File\n\n```python\nwith open(\"image.jpg\", \"rb\") as f:\n    image_data = f.read()\n\nresult = client.analyze(\n    image_data=image_data,\n    visual_features=[VisualFeatures.CAPTION, VisualFeatures.TAGS]\n)\n```\n\n## Image Caption\n\n```python\nresult = client.analyze_from_url(\n    image_url=image_url,\n    visual_features=[VisualFeatures.CAPTION],\n    gender_neutral_caption=True\n)\n\nif result.caption:\n    print(f\"Caption: {result.caption.text}\")\n    print(f\"Confidence: {result.caption.confidence:.2f}\")\n```\n\n## Dense Captions (Multiple Regions)\n\n```python\nresult = client.analyze_from_url(\n    image_url=image_url,\n    visual_features=[VisualFeatures.DENSE_CAPTIONS]\n)\n\nif result.dense_captions:\n    for caption in result.dense_captions.list:\n        print(f\"Caption: {caption.text}\")\n        print(f\"  Confidence: {caption.confidence:.2f}\")\n        print(f\"  Bounding box: {caption.bounding_box}\")\n```\n\n## Tags\n\n```python\nresult = client.analyze_from_url(\n    image_url=image_url,\n    visual_features=[VisualFeatures.TAGS]\n)\n\nif result.tags:\n    for tag in result.tags.list:\n        print(f\"Tag: {tag.name} (confidence: {tag.confidence:.2f})\")\n```\n\n## Object Detection\n\n```python\nresult = client.analyze_from_url(\n    image_url=image_url,\n    visual_features=[VisualFeatures.OBJECTS]\n)\n\nif result.objects:\n    for obj in result.objects.list:\n        print(f\"Object: {obj.tags[0].name}\")\n        print(f\"  Confidence: {obj.tags[0].confidence:.2f}\")\n        box = obj.bounding_box\n        print(f\"  Bounding box: x={box.x}, y={box.y}, w={box.width}, h={box.height}\")\n```\n\n## OCR (Text Extraction)\n\n```python\nresult = client.analyze_from_url(\n    image_url=image_url,\n    visual_features=[VisualFeatures.READ]\n)\n\nif result.read:\n    for block in result.read.blocks:\n        for line in block.lines:\n            print(f\"Line: {line.text}\")\n            print(f\"  Bounding polygon: {line.bounding_polygon}\")\n\n            # Word-level details\n            for word in line.words:\n                print(f\"  Word: {word.text} (confidence: {word.confidence:.2f})\")\n```\n\n## People Detection\n\n```python\nresult = client.analyze_from_url(\n    image_url=image_url,\n    visual_features=[VisualFeatures.PEOPLE]\n)\n\nif result.people:\n    for person in result.people.list:\n        print(f\"Person detected:\")\n        print(f\"  Confidence: {person.confidence:.2f}\")\n        box = person.bounding_box\n        print(f\"  Bounding box: x={box.x}, y={box.y}, w={box.width}, h={box.height}\")\n```\n\n## Smart Cropping\n\n```python\nresult = client.analyze_from_url(\n    image_url=image_url,\n    visual_features=[VisualFeatures.SMART_CROPS],\n    smart_crops_aspect_ratios=[0.9, 1.33, 1.78]  # Portrait, 4:3, 16:9\n)\n\nif result.smart_crops:\n    for crop in result.smart_crops.list:\n        print(f\"Aspect ratio: {crop.aspect_ratio}\")\n        box = crop.bounding_box\n        print(f\"  Crop region: x={box.x}, y={box.y}, w={box.width}, h={box.height}\")\n```\n\n## Async Client\n\n```python\nfrom azure.ai.vision.imageanalysis.aio import ImageAnalysisClient\nfrom azure.identity.aio import DefaultAzureCredential\n\nasync def analyze_image():\n    async with ImageAnalysisClient(\n        endpoint=endpoint,\n        credential=DefaultAzureCredential()\n    ) as client:\n        result = await client.analyze_from_url(\n            image_url=image_url,\n            visual_features=[VisualFeatures.CAPTION]\n        )\n        print(result.caption.text)\n```\n\n## Visual Features\n\n| Feature | Description |\n|---------|-------------|\n| `CAPTION` | Single sentence describing the image |\n| `DENSE_CAPTIONS` | Captions for multiple regions |\n| `TAGS` | Content tags (objects, scenes, actions) |\n| `OBJECTS` | Object detection with bounding boxes |\n| `READ` | OCR text extraction |\n| `PEOPLE` | People detection with bounding boxes |\n| `SMART_CROPS` | Suggested crop regions for thumbnails |\n\n## Error Handling\n\n```python\nfrom azure.core.exceptions import HttpResponseError\n\ntry:\n    result = client.analyze_from_url(\n        image_url=image_url,\n        visual_features=[VisualFeatures.CAPTION]\n    )\nexcept HttpResponseError as e:\n    print(f\"Status code: {e.status_code}\")\n    print(f\"Reason: {e.reason}\")\n    print(f\"Message: {e.error.message}\")\n```\n\n## Image Requirements\n\n- Formats: JPEG, PNG, GIF, BMP, WEBP, ICO, TIFF, MPO\n- Max size: 20 MB\n- Dimensions: 50x50 to 16000x16000 pixels\n\n## Best Practices\n\n1. **Select only needed features** to optimize latency and cost\n2. **Use async client** for high-throughput scenarios\n3. **Handle HttpResponseError** for invalid images or auth issues\n4. **Enable gender_neutral_caption** for inclusive descriptions\n5. **Specify language** for localized captions\n6. **Use smart_crops_aspect_ratios** matching your thumbnail requirements\n7. **Cache results** when analyzing the same image multiple times\n","# Azure AI Vision Image Analysis SDK for Python\n\nClient library for Azure AI Vision 4.0 image analysis including captions, tags, objects, OCR, and more.\n\n## Installation\n\n```bash\npip install azure-ai-vision-imageanalysis\n```\n\n## Environment Variables\n\n```bash\nVISION_ENDPOINT=https:\u002F\u002F\u003Cresource>.cognitiveservices.azure.com\nVISION_KEY=\u003Cyour-api-key>  # If using API key\n```\n\n## Authentication\n\n### API Key\n\n```python\nimport os\nfrom azure.ai.vision.imageanalysis import ImageAnalysisClient\nfrom azure.core.credentials import AzureKeyCredential\n\nendpoint = os.environ[\"VISION_ENDPOINT\"]\nkey = os.environ[\"VISION_KEY\"]\n\nclient = ImageAnalysisClient(\n    endpoint=endpoint,\n    credential=AzureKeyCredential(key)\n)\n```\n\n### Entra ID (Recommended)\n\n```python\nfrom azure.ai.vision.imageanalysis import ImageAnalysisClient\nfrom azure.identity import DefaultAzureCredential\n\nclient = ImageAnalysisClient(\n    endpoint=os.environ[\"VISION_ENDPOINT\"],\n    credential=DefaultAzureCredential()\n)\n```\n\n## Analyze Image from URL\n\n```python\nfrom azure.ai.vision.imageanalysis.models import VisualFeatures\n\nimage_url = \"https:\u002F\u002Fexample.com\u002Fimage.jpg\"\n\nresult = client.analyze_from_url(\n    image_url=image_url,\n    visual_features=[\n        VisualFeatures.CAPTION,\n        VisualFeatures.TAGS,\n        VisualFeatures.OBJECTS,\n        VisualFeatures.READ,\n        VisualFeatures.PEOPLE,\n        VisualFeatures.SMART_CROPS,\n        VisualFeatures.DENSE_CAPTIONS\n    ],\n    gender_neutral_caption=True,\n    language=\"en\"\n)\n```\n\n## Analyze Image from File\n\n```python\nwith open(\"image.jpg\", \"rb\") as f:\n    image_data = f.read()\n\nresult = client.analyze(\n    image_data=image_data,\n    visual_features=[VisualFeatures.CAPTION, VisualFeatures.TAGS]\n)\n```\n\n## Image Caption\n\n```python\nresult = client.analyze_from_url(\n    image_url=image_url,\n    visual_features=[VisualFeatures.CAPTION],\n    gender_neutral_caption=True\n)\n\nif result.caption:\n    print(f\"Caption: {result.caption.text}\")\n    print(f\"Confidence: {result.caption.confidence:.2f}\")\n```\n\n## Dense Captions (Multiple Regions)\n\n```python\nresult = client.analyze_from_url(\n    image_url=image_url,\n    visual_features=[VisualFeatures.DENSE_CAPTIONS]\n)\n\nif result.dense_captions:\n    for caption in result.dense_captions.list:\n        print(f\"Caption: {caption.text}\")\n        print(f\"  Confidence: {caption.confidence:.2f}\")\n        print(f\"  Bounding box: {caption.bounding_box}\")\n```\n\n## Tags\n\n```python\nresult = client.analyze_from_url(\n    image_url=image_url,\n    visual_features=[VisualFeatures.TAGS]\n)\n\nif result.tags:\n    for tag in result.tags.list:\n        print(f\"Tag: {tag.name} (confidence: {tag.confidence:.2f})\")\n```\n\n## Object Detection\n\n```python\nresult = client.analyze_from_url(\n    image_url=image_url,\n    visual_features=[VisualFeatures.OBJECTS]\n)\n\nif result.objects:\n    for obj in result.objects.list:\n        print(f\"Object: {obj.tags[0].name}\")\n        print(f\"  Confidence: {obj.tags[0].confidence:.2f}\")\n        box = obj.bounding_box\n        print(f\"  Bounding box: x={box.x}, y={box.y}, w={box.width}, h={box.height}\")\n```\n\n## OCR (Text Extraction)\n\n```python\nresult = client.analyze_from_url(\n    image_url=image_url,\n    visual_features=[VisualFeatures.READ]\n)\n\nif result.read:\n    for block in result.read.blocks:\n        for line in block.lines:\n            print(f\"Line: {line.text}\")\n            print(f\"  Bounding polygon: {line.bounding_polygon}\")\n\n            # Word-level details\n            for word in line.words:\n                print(f\"  Word: {word.text} (confidence: {word.confidence:.2f})\")\n```\n\n## People Detection\n\n```python\nresult = client.analyze_from_url(\n    image_url=image_url,\n    visual_features=[VisualFeatures.PEOPLE]\n)\n\nif result.people:\n    for person in result.people.list:\n        print(f\"Person detected:\")\n        print(f\"  Confidence: {person.confidence:.2f}\")\n        box = person.bounding_box\n        print(f\"  Bounding box: x={box.x}, y={box.y}, w={box.width}, h={box.height}\")\n```\n\n## Smart Cropping\n\n```python\nresult = client.analyze_from_url(\n    image_url=image_url,\n    visual_features=[VisualFeatures.SMART_CROPS],\n    smart_crops_aspect_ratios=[0.9, 1.33, 1.78]  # Portrait, 4:3, 16:9\n)\n\nif result.smart_crops:\n    for crop in result.smart_crops.list:\n        print(f\"Aspect ratio: {crop.aspect_ratio}\")\n        box = crop.bounding_box\n        print(f\"  Crop region: x={box.x}, y={box.y}, w={box.width}, h={box.height}\")\n```\n\n## Async Client\n\n```python\nfrom azure.ai.vision.imageanalysis.aio import ImageAnalysisClient\nfrom azure.identity.aio import DefaultAzureCredential\n\nasync def analyze_image():\n    async with ImageAnalysisClient(\n        endpoint=endpoint,\n        credential=DefaultAzureCredential()\n    ) as client:\n        result = await client.analyze_from_url(\n            image_url=image_url,\n            visual_features=[VisualFeatures.CAPTION]\n        )\n        print(result.caption.text)\n```\n\n## Visual Features\n\n| Feature | Description |\n|---------|-------------|\n| `CAPTION` | Single sentence describing the image |\n| `DENSE_CAPTIONS` | Captions for multiple regions |\n| `TAGS` | Content tags (objects, scenes, actions) |\n| `OBJECTS` | Object detection with bounding boxes |\n| `READ` | OCR text extraction |\n| `PEOPLE` | People detection with bounding boxes |\n| `SMART_CROPS` | Suggested crop regions for thumbnails |\n\n## Error Handling\n\n```python\nfrom azure.core.exceptions import HttpResponseError\n\ntry:\n    result = client.analyze_from_url(\n        image_url=image_url,\n        visual_features=[VisualFeatures.CAPTION]\n    )\nexcept HttpResponseError as e:\n    print(f\"Status code: {e.status_code}\")\n    print(f\"Reason: {e.reason}\")\n    print(f\"Message: {e.error.message}\")\n```\n\n## Image Requirements\n\n- Formats: JPEG, PNG, GIF, BMP, WEBP, ICO, TIFF, MPO\n- Max size: 20 MB\n- Dimensions: 50x50 to 16000x16000 pixels\n\n## Best Practices\n\n1. **Select only needed features** to optimize latency and cost\n2. **Use async client** for high-throughput scenarios\n3. **Handle HttpResponseError** for invalid images or auth issues\n4. **Enable gender_neutral_caption** for inclusive descriptions\n5. **Specify language** for localized captions\n6. **Use smart_crops_aspect_ratios** matching your thumbnail requirements\n7. **Cache results** when analyzing the same image multiple times\n",{"name":5,"license":15,"metadata":49,"description":53},{"author":50,"package":51,"version":52},"Microsoft","azure-ai-vision-imageanalysis","1.0.0","Azure AI Vision Image Analysis SDK for captions, tags, objects, OCR, people detection, and smart cropping. 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Auto-detects dev servers, writes reusable test scripts, and supports screenshots, responsive checks, UX validation, login flows, link checks, and arbitrary browser automation. Use when the user wants to test a website, automate browser interactions, validate web functionality, or perform browser-based testing.","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Ftesting-qa-benchmarking\u002Flatest-release-browser-automation\u002Fplaywright-skill\u002FSKILL.md",[148,149],{"categoryNo":91,"categoryName":92,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":121,"categoryName":122,"taxonomyType":21,"taxonomyTypeDesc":22},[108,151,152,153,98,154,155,130,156,106,157,158,159,160,161,162,163,164,165,166],"playwright skill","browser automation","flows","auto","access","Bash","website","ux","login","scripts","read","write","node","install","network","responsive",{"skillNo":168,"name":169,"nameZh":6,"description":170,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":86,"licenseFileUrl":171,"sourceHtmlUrl":172,"categories":173,"keywords":175,"publishedAt":44},"01M71CY25GM939M6RES91QSG3T","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.","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Fsearch-retrieval-web-automation\u002Fofficial-reference\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Fsearch-retrieval-web-automation\u002Fofficial-reference\u002Fweb-artifacts-builder\u002FSKILL.md",[174],{"categoryNo":94,"categoryName":95,"taxonomyType":21,"taxonomyTypeDesc":22},[176,177,178,109,179,180,181,30,182,183,102,184,38,185,186,187,188,189,190,191],"builder","artifacts builder","artifacts","web artifacts builder","single-file","css","Tailwind","ui components","single","file","components","react","multi","state","routing","state management",{"skillNo":193,"name":194,"nameZh":6,"description":195,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":86,"licenseFileUrl":196,"sourceHtmlUrl":197,"categories":198,"keywords":208,"publishedAt":44},"01M71CXZB60QJJ1H4S3NN94WMZ","frontend-design","Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML\u002FCSS layouts, or when styling\u002Fbeautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Fsearch-retrieval-web-automation\u002Fofficial-reference\u002Ffrontend-design\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Fsearch-retrieval-web-automation\u002Fofficial-reference\u002Ffrontend-design\u002FSKILL.md",[199,200,203],{"categoryNo":94,"categoryName":95,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":201,"categoryName":202,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":204,"categoryName":205,"taxonomyType":206,"taxonomyTypeDesc":207},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[102,209,210,181,211,212,213,100,214,215,38,130,178,216,186,217,187,218,219,107],"design","frontend design","ui design","generic","aesthetics","landing","pages","code","web ui","react components","examples",{"skillNo":221,"name":222,"nameZh":6,"description":223,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":224,"categories":225,"keywords":226,"publishedAt":44},"01M71CVZ54WFBTVA7R16PFY1ZE","zarr-python","Chunked N-D arrays for cloud storage. Compressed arrays, parallel I\u002FO, S3\u002FGCS integration, NumPy\u002FDask\u002FXarray compatible, for large-scale scientific computing pipelines.","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Fscience-research-data-analysis\u002Flatest-release-community\u002Fzarr-python\u002FSKILL.md",[],[227,228,229,230,231,232,233,234,235,30,236,237,238,239,240,241,242,243],"zarr python","python","zarr","arrays","Cloud Storage","scientific","storage","parallel","dask","numpy","gcs","s3","integration","scientific computing","cloud","scale","pipelines"]