[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M69ARK0ESSJ5Y9CPQERC9PAA":3,"skill-same:01M69ARK0ESSJ5Y9CPQERC9PAA":107},{"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":26,"publishedAt":48,"linkOnly":49,"markdownContent":50,"markdownBody":51,"frontMatter":52,"refName":57,"commitSha":58,"skillDirectory":59,"forksCount":60,"repositoryHtmlUrl":61,"repositoryHomepageUrl":62,"ownerHtmlUrl":63,"ownerBlogUrl":64,"ownerDescription":65,"updatedAt":66,"install":67,"resources":80},"01M69ARK0ESSJ5Y9CPQERC9PAA","azure-ai-translation-document-py",null,"Azure AI Document Translation SDK for batch translation of documents with format preservation. Use for translating Word, PDF, Excel, PowerPoint, and other document formats at scale.\nTriggers: \"document translation\", \"batch translation\", \"translate documents\", \"DocumentTranslationClient\".","microsoft\u002Fskills","microsoft","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6154722?v=4","01M698VYDXZ6JW5WH3CRV650R8","Microsoft","01M69AJYYW4E1RMSYCQDJ4FJSH",3195,"MIT","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills\u002Fblob\u002Fmain\u002F.github\u002Fplugins\u002Fazure-sdk-python\u002Fskills\u002Fazure-ai-translation-document-py\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000002","文档处理","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000004","电子表格",[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,9,45,46,47],"azure ai","azure","ai translation","azure ai translation document py","translation","document","Word","for","at","formats","ai-translation","with","triggers","use","PDF","batch","powerpoint","documents","translate","sdk","xlsx","2026-11-10 10:12:30",false,"---\nname: azure-ai-translation-document-py\ndescription: |\n  Azure AI Document Translation SDK for batch translation of documents with format preservation. Use for translating Word, PDF, Excel, PowerPoint, and other document formats at scale.\n  Triggers: \"document translation\", \"batch translation\", \"translate documents\", \"DocumentTranslationClient\".\nlicense: MIT\nmetadata:\n  author: Microsoft\n  version: \"1.0.0\"\n  package: azure-ai-translation-document\n---\n\n# Azure AI Document Translation SDK for Python\n\nClient library for Azure AI Translator document translation service for batch document translation with format preservation.\n\n## Installation\n\n```bash\npip install azure-ai-translation-document\n```\n\n## Environment Variables\n\n```bash\nAZURE_DOCUMENT_TRANSLATION_ENDPOINT=https:\u002F\u002F\u003Cresource>.cognitiveservices.azure.com  # Required for all auth methods\n# Storage for source and target documents\nAZURE_SOURCE_CONTAINER_URL=https:\u002F\u002F\u003Cstorage>.blob.core.windows.net\u002F\u003Ccontainer>?\u003Csas>  # Required for all auth methods\nAZURE_TARGET_CONTAINER_URL=https:\u002F\u002F\u003Cstorage>.blob.core.windows.net\u002F\u003Ccontainer>?\u003Csas>  # Required for all auth methods\nAZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production\nAZURE_DOCUMENT_TRANSLATION_KEY=\u003Cyour-api-key>  # Only required for the legacy API-key auth path below\n```\n\n## Authentication & Lifecycle\n\n> **? Two rules apply to every code sample below:**\n>\n> 1. **Prefer `DefaultAzureCredential`.** It works locally (Azure CLI \u002F VS Code \u002F Developer CLI) and in Azure (managed identity, workload identity) with no code change. Avoid connection strings, account\u002FAPI keys — they bypass Entra audit and rotation.\n>    - Local dev: `DefaultAzureCredential` works as-is.\n>    - Production: set `AZURE_TOKEN_CREDENTIALS=prod` (or `AZURE_TOKEN_CREDENTIALS=\u003Cspecific_credential>`) to constrain the credential chain to production-safe credentials.\n> 2. **Wrap every client in a context manager** so HTTP transports, sockets, and token caches are released deterministically:\n>    - Sync: `with \u003CClient>(...) as client:`\n>    - Async: `async with \u003CClient>(...) as client:` **and** `async with DefaultAzureCredential() as credential:` (from `azure.identity.aio`)\n>\n> Snippets may abbreviate this setup, but production code should always follow both rules.\n\n```python\nimport os\nfrom azure.identity import DefaultAzureCredential, ManagedIdentityCredential\nfrom azure.ai.translation.document import DocumentTranslationClient\n\n# Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=\u003Cspecific_credential>\ncredential = DefaultAzureCredential()\n# Or use a specific credential directly in production:\n# See https:\u002F\u002Flearn.microsoft.com\u002Fpython\u002Fapi\u002Foverview\u002Fazure\u002Fidentity-readme?view=azure-python#credential-classes\n# credential = ManagedIdentityCredential()\n\nwith DocumentTranslationClient(\n    endpoint=os.environ[\"AZURE_DOCUMENT_TRANSLATION_ENDPOINT\"],\n    credential=credential,\n) as client:\n    statuses = list(client.list_translation_statuses())\n```\n\n### Legacy: API Key (existing keyed deployments)\n\nNew code should use `DefaultAzureCredential` above. Use `AzureKeyCredential` only if you have an existing keyed deployment that hasn't been migrated to Entra ID yet — for example, regulated environments still completing their Entra rollout.\n\n```python\nimport os\nfrom azure.core.credentials import AzureKeyCredential\nfrom azure.ai.translation.document import DocumentTranslationClient, SingleDocumentTranslationClient\n\nwith DocumentTranslationClient(\n    endpoint=os.environ[\"AZURE_DOCUMENT_TRANSLATION_ENDPOINT\"],\n    credential=AzureKeyCredential(os.environ[\"AZURE_DOCUMENT_TRANSLATION_KEY\"]),\n) as client:\n    statuses = list(client.list_translation_statuses())\n\n# SingleDocumentTranslationClient accepts the same key-based credential.\n```\n\n## Basic Document Translation\n\n```python\nimport os\nfrom azure.ai.translation.document import DocumentTranslationClient, DocumentTranslationInput, TranslationTarget\nfrom azure.core.exceptions import HttpResponseError\nfrom azure.identity import DefaultAzureCredential\n\ncredential = DefaultAzureCredential()\n\nwith DocumentTranslationClient(\n    endpoint=os.environ[\"AZURE_DOCUMENT_TRANSLATION_ENDPOINT\"],\n    credential=credential,\n) as client:\n    source_url = os.environ[\"AZURE_SOURCE_CONTAINER_URL\"]\n    target_url = os.environ[\"AZURE_TARGET_CONTAINER_URL\"]\n\n    try:\n        # Start translation job\n        poller = client.begin_translation(\n            inputs=[\n                DocumentTranslationInput(\n                    source_url=source_url,\n                    targets=[\n                        TranslationTarget(\n                            target_url=target_url,\n                            language=\"es\"  # Translate to Spanish\n                        )\n                    ]\n                )\n            ]\n        )\n\n        # Wait for completion\n        result = poller.result()\n\n        print(f\"Status: {poller.status()}\")\n        print(f\"Documents translated: {poller.details.documents_succeeded_count}\")\n        print(f\"Documents failed: {poller.details.documents_failed_count}\")\n    except HttpResponseError as e:\n        print(f\"Translation failed: {e.message}\")\n        raise\n```\n\n## Multiple Target Languages\n\n```python\npoller = client.begin_translation(\n    inputs=[\n        DocumentTranslationInput(\n            source_url=source_url,\n            targets=[\n                TranslationTarget(target_url=target_url_es, language=\"es\"),\n                TranslationTarget(target_url=target_url_fr, language=\"fr\"),\n                TranslationTarget(target_url=target_url_de, language=\"de\")\n            ]\n        )\n    ]\n)\n```\n\n## Translate Single Document\n\n```python\nfrom azure.ai.translation.document import SingleDocumentTranslationClient\nfrom azure.identity import DefaultAzureCredential\n\nwith open(\"document.docx\", \"rb\") as f:\n    document_content = f.read()\n\nwith SingleDocumentTranslationClient(endpoint, DefaultAzureCredential()) as single_client:\n    result = single_client.translate(\n        body=document_content,\n        target_language=\"es\",\n        content_type=\"application\u002Fvnd.openxmlformats-officedocument.wordprocessingml.document\"\n    )\n\n# Save translated document\nwith open(\"document_es.docx\", \"wb\") as f:\n    f.write(result)\n```\n\n## Check Translation Status\n\n```python\n# Get all translation operations\noperations = client.list_translation_statuses()\n\nfor op in operations:\n    print(f\"Operation ID: {op.id}\")\n    print(f\"Status: {op.status}\")\n    print(f\"Created: {op.created_on}\")\n    print(f\"Total documents: {op.documents_total_count}\")\n    print(f\"Succeeded: {op.documents_succeeded_count}\")\n    print(f\"Failed: {op.documents_failed_count}\")\n```\n\n## List Document Statuses\n\n```python\n# Get status of individual documents in a job\noperation_id = poller.id\ndocument_statuses = client.list_document_statuses(operation_id)\n\nfor doc in document_statuses:\n    print(f\"Document: {doc.source_document_url}\")\n    print(f\"  Status: {doc.status}\")\n    print(f\"  Translated to: {doc.translated_to}\")\n    if doc.error:\n        print(f\"  Error: {doc.error.message}\")\n```\n\n## Cancel Translation\n\n```python\n# Cancel a running translation\nclient.cancel_translation(operation_id)\n```\n\n## Using Glossary\n\n```python\nfrom azure.ai.translation.document import TranslationGlossary\n\npoller = client.begin_translation(\n    inputs=[\n        DocumentTranslationInput(\n            source_url=source_url,\n            targets=[\n                TranslationTarget(\n                    target_url=target_url,\n                    language=\"es\",\n                    glossaries=[\n                        TranslationGlossary(\n                            glossary_url=\"https:\u002F\u002F\u003Cstorage>.blob.core.windows.net\u002Fglossary\u002Fterms.csv?\u003Csas>\",\n                            file_format=\"csv\"\n                        )\n                    ]\n                )\n            ]\n        )\n    ]\n)\n```\n\n## Supported Document Formats\n\n```python\n# Get supported formats\nformats = client.get_supported_document_formats()\n\nfor fmt in formats:\n    print(f\"Format: {fmt.format}\")\n    print(f\"  Extensions: {fmt.file_extensions}\")\n    print(f\"  Content types: {fmt.content_types}\")\n```\n\n## Supported Languages\n\n```python\n# Get supported languages\nlanguages = client.get_supported_languages()\n\nfor lang in languages:\n    print(f\"Language: {lang.name} ({lang.code})\")\n```\n\n## Async Client\n\n```python\nfrom azure.ai.translation.document.aio import DocumentTranslationClient\nfrom azure.identity.aio import DefaultAzureCredential\n\nasync def translate_documents():\n    async with DefaultAzureCredential() as credential:\n        async with DocumentTranslationClient(\n            endpoint=endpoint,\n            credential=credential,\n        ) as client:\n            poller = await client.begin_translation(inputs=[...])\n            result = await poller.result()\n```\n\n## Supported Formats\n\n| Category | Formats |\n|----------|---------|\n| Documents | DOCX, PDF, PPTX, XLSX, HTML, TXT, RTF |\n| Structured | CSV, TSV, JSON, XML |\n| Localization | XLIFF, XLF, MHTML |\n\n## Storage Requirements\n\n- Source and target containers must be Azure Blob Storage\n- Use SAS tokens with appropriate permissions:\n  - Source: Read, List\n  - Target: Write, List\n\n## Best Practices\n\n1. **Pick sync OR async and stay consistent.** Do not mix `azure.xxx` sync clients with `azure.xxx.aio` async clients in the same call path. Choose one mode per module.\n2. **Always use context managers for clients and async credentials.** Wrap every client in `with Client(...) as client:` (sync) or `async with Client(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.\n3. **Use SAS tokens** with minimal required permissions\n4. **Monitor long-running operations** with `poller.status()`\n5. **Handle document-level errors** by iterating document statuses\n6. **Use glossaries** for domain-specific terminology\n7. **Separate target containers** for each language\n8. **Use async client** for multiple concurrent jobs\n9. **Check supported formats** before submitting documents\n\n## Reference Files\n\n| File | Contents |\n|------|----------|\n| [references\u002Fcapabilities.md](references\u002Fcapabilities.md) | Additional non-hero capabilities, operation-group coverage, and production checklists. |\n| [references\u002Fnon-hero-scenarios.md](references\u002Fnon-hero-scenarios.md) | Dedicated non-hero examples for secondary\u002Fadvanced scenarios. |\n","# Azure AI Document Translation SDK for Python\n\nClient library for Azure AI Translator document translation service for batch document translation with format preservation.\n\n## Installation\n\n```bash\npip install azure-ai-translation-document\n```\n\n## Environment Variables\n\n```bash\nAZURE_DOCUMENT_TRANSLATION_ENDPOINT=https:\u002F\u002F\u003Cresource>.cognitiveservices.azure.com  # Required for all auth methods\n# Storage for source and target documents\nAZURE_SOURCE_CONTAINER_URL=https:\u002F\u002F\u003Cstorage>.blob.core.windows.net\u002F\u003Ccontainer>?\u003Csas>  # Required for all auth methods\nAZURE_TARGET_CONTAINER_URL=https:\u002F\u002F\u003Cstorage>.blob.core.windows.net\u002F\u003Ccontainer>?\u003Csas>  # Required for all auth methods\nAZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production\nAZURE_DOCUMENT_TRANSLATION_KEY=\u003Cyour-api-key>  # Only required for the legacy API-key auth path below\n```\n\n## Authentication & Lifecycle\n\n> **? Two rules apply to every code sample below:**\n>\n> 1. **Prefer `DefaultAzureCredential`.** It works locally (Azure CLI \u002F VS Code \u002F Developer CLI) and in Azure (managed identity, workload identity) with no code change. Avoid connection strings, account\u002FAPI keys — they bypass Entra audit and rotation.\n>    - Local dev: `DefaultAzureCredential` works as-is.\n>    - Production: set `AZURE_TOKEN_CREDENTIALS=prod` (or `AZURE_TOKEN_CREDENTIALS=\u003Cspecific_credential>`) to constrain the credential chain to production-safe credentials.\n> 2. **Wrap every client in a context manager** so HTTP transports, sockets, and token caches are released deterministically:\n>    - Sync: `with \u003CClient>(...) as client:`\n>    - Async: `async with \u003CClient>(...) as client:` **and** `async with DefaultAzureCredential() as credential:` (from `azure.identity.aio`)\n>\n> Snippets may abbreviate this setup, but production code should always follow both rules.\n\n```python\nimport os\nfrom azure.identity import DefaultAzureCredential, ManagedIdentityCredential\nfrom azure.ai.translation.document import DocumentTranslationClient\n\n# Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=\u003Cspecific_credential>\ncredential = DefaultAzureCredential()\n# Or use a specific credential directly in production:\n# See https:\u002F\u002Flearn.microsoft.com\u002Fpython\u002Fapi\u002Foverview\u002Fazure\u002Fidentity-readme?view=azure-python#credential-classes\n# credential = ManagedIdentityCredential()\n\nwith DocumentTranslationClient(\n    endpoint=os.environ[\"AZURE_DOCUMENT_TRANSLATION_ENDPOINT\"],\n    credential=credential,\n) as client:\n    statuses = list(client.list_translation_statuses())\n```\n\n### Legacy: API Key (existing keyed deployments)\n\nNew code should use `DefaultAzureCredential` above. Use `AzureKeyCredential` only if you have an existing keyed deployment that hasn't been migrated to Entra ID yet — for example, regulated environments still completing their Entra rollout.\n\n```python\nimport os\nfrom azure.core.credentials import AzureKeyCredential\nfrom azure.ai.translation.document import DocumentTranslationClient, SingleDocumentTranslationClient\n\nwith DocumentTranslationClient(\n    endpoint=os.environ[\"AZURE_DOCUMENT_TRANSLATION_ENDPOINT\"],\n    credential=AzureKeyCredential(os.environ[\"AZURE_DOCUMENT_TRANSLATION_KEY\"]),\n) as client:\n    statuses = list(client.list_translation_statuses())\n\n# SingleDocumentTranslationClient accepts the same key-based credential.\n```\n\n## Basic Document Translation\n\n```python\nimport os\nfrom azure.ai.translation.document import DocumentTranslationClient, DocumentTranslationInput, TranslationTarget\nfrom azure.core.exceptions import HttpResponseError\nfrom azure.identity import DefaultAzureCredential\n\ncredential = DefaultAzureCredential()\n\nwith DocumentTranslationClient(\n    endpoint=os.environ[\"AZURE_DOCUMENT_TRANSLATION_ENDPOINT\"],\n    credential=credential,\n) as client:\n    source_url = os.environ[\"AZURE_SOURCE_CONTAINER_URL\"]\n    target_url = os.environ[\"AZURE_TARGET_CONTAINER_URL\"]\n\n    try:\n        # Start translation job\n        poller = client.begin_translation(\n            inputs=[\n                DocumentTranslationInput(\n                    source_url=source_url,\n                    targets=[\n                        TranslationTarget(\n                            target_url=target_url,\n                            language=\"es\"  # Translate to Spanish\n                        )\n                    ]\n                )\n            ]\n        )\n\n        # Wait for completion\n        result = poller.result()\n\n        print(f\"Status: {poller.status()}\")\n        print(f\"Documents translated: {poller.details.documents_succeeded_count}\")\n        print(f\"Documents failed: {poller.details.documents_failed_count}\")\n    except HttpResponseError as e:\n        print(f\"Translation failed: {e.message}\")\n        raise\n```\n\n## Multiple Target Languages\n\n```python\npoller = client.begin_translation(\n    inputs=[\n        DocumentTranslationInput(\n            source_url=source_url,\n            targets=[\n                TranslationTarget(target_url=target_url_es, language=\"es\"),\n                TranslationTarget(target_url=target_url_fr, language=\"fr\"),\n                TranslationTarget(target_url=target_url_de, language=\"de\")\n            ]\n        )\n    ]\n)\n```\n\n## Translate Single Document\n\n```python\nfrom azure.ai.translation.document import SingleDocumentTranslationClient\nfrom azure.identity import DefaultAzureCredential\n\nwith open(\"document.docx\", \"rb\") as f:\n    document_content = f.read()\n\nwith SingleDocumentTranslationClient(endpoint, DefaultAzureCredential()) as single_client:\n    result = single_client.translate(\n        body=document_content,\n        target_language=\"es\",\n        content_type=\"application\u002Fvnd.openxmlformats-officedocument.wordprocessingml.document\"\n    )\n\n# Save translated document\nwith open(\"document_es.docx\", \"wb\") as f:\n    f.write(result)\n```\n\n## Check Translation Status\n\n```python\n# Get all translation operations\noperations = client.list_translation_statuses()\n\nfor op in operations:\n    print(f\"Operation ID: {op.id}\")\n    print(f\"Status: {op.status}\")\n    print(f\"Created: {op.created_on}\")\n    print(f\"Total documents: {op.documents_total_count}\")\n    print(f\"Succeeded: {op.documents_succeeded_count}\")\n    print(f\"Failed: {op.documents_failed_count}\")\n```\n\n## List Document Statuses\n\n```python\n# Get status of individual documents in a job\noperation_id = poller.id\ndocument_statuses = client.list_document_statuses(operation_id)\n\nfor doc in document_statuses:\n    print(f\"Document: {doc.source_document_url}\")\n    print(f\"  Status: {doc.status}\")\n    print(f\"  Translated to: {doc.translated_to}\")\n    if doc.error:\n        print(f\"  Error: {doc.error.message}\")\n```\n\n## Cancel Translation\n\n```python\n# Cancel a running translation\nclient.cancel_translation(operation_id)\n```\n\n## Using Glossary\n\n```python\nfrom azure.ai.translation.document import TranslationGlossary\n\npoller = client.begin_translation(\n    inputs=[\n        DocumentTranslationInput(\n            source_url=source_url,\n            targets=[\n                TranslationTarget(\n                    target_url=target_url,\n                    language=\"es\",\n                    glossaries=[\n                        TranslationGlossary(\n                            glossary_url=\"https:\u002F\u002F\u003Cstorage>.blob.core.windows.net\u002Fglossary\u002Fterms.csv?\u003Csas>\",\n                            file_format=\"csv\"\n                        )\n                    ]\n                )\n            ]\n        )\n    ]\n)\n```\n\n## Supported Document Formats\n\n```python\n# Get supported formats\nformats = client.get_supported_document_formats()\n\nfor fmt in formats:\n    print(f\"Format: {fmt.format}\")\n    print(f\"  Extensions: {fmt.file_extensions}\")\n    print(f\"  Content types: {fmt.content_types}\")\n```\n\n## Supported Languages\n\n```python\n# Get supported languages\nlanguages = client.get_supported_languages()\n\nfor lang in languages:\n    print(f\"Language: {lang.name} ({lang.code})\")\n```\n\n## Async Client\n\n```python\nfrom azure.ai.translation.document.aio import DocumentTranslationClient\nfrom azure.identity.aio import DefaultAzureCredential\n\nasync def translate_documents():\n    async with DefaultAzureCredential() as credential:\n        async with DocumentTranslationClient(\n            endpoint=endpoint,\n            credential=credential,\n        ) as client:\n            poller = await client.begin_translation(inputs=[...])\n            result = await poller.result()\n```\n\n## Supported Formats\n\n| Category | Formats |\n|----------|---------|\n| Documents | DOCX, PDF, PPTX, XLSX, HTML, TXT, RTF |\n| Structured | CSV, TSV, JSON, XML |\n| Localization | XLIFF, XLF, MHTML |\n\n## Storage Requirements\n\n- Source and target containers must be Azure Blob Storage\n- Use SAS tokens with appropriate permissions:\n  - Source: Read, List\n  - Target: Write, List\n\n## Best Practices\n\n1. **Pick sync OR async and stay consistent.** Do not mix `azure.xxx` sync clients with `azure.xxx.aio` async clients in the same call path. Choose one mode per module.\n2. **Always use context managers for clients and async credentials.** Wrap every client in `with Client(...) as client:` (sync) or `async with Client(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.\n3. **Use SAS tokens** with minimal required permissions\n4. **Monitor long-running operations** with `poller.status()`\n5. **Handle document-level errors** by iterating document statuses\n6. **Use glossaries** for domain-specific terminology\n7. **Separate target containers** for each language\n8. **Use async client** for multiple concurrent jobs\n9. **Check supported formats** before submitting documents\n\n## Reference Files\n\n| File | Contents |\n|------|----------|\n| [references\u002Fcapabilities.md](references\u002Fcapabilities.md) | Additional non-hero capabilities, operation-group coverage, and production checklists. |\n| [references\u002Fnon-hero-scenarios.md](references\u002Fnon-hero-scenarios.md) | Dedicated non-hero examples for secondary\u002Fadvanced scenarios. |\n",{"name":5,"license":15,"metadata":53,"description":56},{"author":12,"package":54,"version":55},"azure-ai-translation-document","1.0.0","Azure AI Document Translation SDK for batch translation of documents with format preservation. 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Use when creating new skills or updating existing skills.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fskill-creator\u002FSKILL.md",[114],{"categoryNo":115,"categoryName":116,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[118,119,120,9,121,122,123,28,124,125,126,40,127,38,128,34,129,130,131],"skill creator","creator","coding agents","foundry","microsoft foundry","ai coding agents","services","coding","guide","ai coding","sdks","Existing","when","new",{"skillNo":133,"name":134,"nameZh":6,"description":135,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":136,"categories":137,"keywords":148,"publishedAt":48},"01M69B4FD57V1MYKRWAJJECTZ7","podcast-generation","Generate AI-powered podcast-style audio narratives using Azure OpenAI's GPT Realtime Mini model via WebSocket. Use when building text-to-speech features, audio narrative generation, podcast creation from content, or integrating with Azure OpenAI Realtime API for real audio output. Covers full-stack implementation from React frontend to Python FastAPI backend with WebSocket streaming.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fpodcast-generation\u002FSKILL.md",[138,141,144,147],{"categoryNo":139,"categoryName":140,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":142,"categoryName":143,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":145,"categoryName":146,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":115,"categoryName":116,"taxonomyType":21,"taxonomyTypeDesc":22},[149,150,151,152,153,154,155,28,156,157,158,159,160,161,162,163,164,165,166,167,168,130,169],"podcast generation","podcast","generation","frontend","python","react","realtime","azure openai","generate","streaming","api","Audio","stack","text","model","fastapi","creation","python fastapi","narrative","features","fastapi backend",{"skillNo":171,"name":172,"nameZh":6,"description":173,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":174,"categories":175,"keywords":177,"publishedAt":48},"01M69B4CYPM5SESY5ZWEFDXEQZ","microsoft-docs","Understand Microsoft technologies by querying official documentation. Use whenever the user asks how something works, wants tutorials, needs configuration options, limits, quotas, or best practices for any Microsoft technology (Azure, .NET, M365, Windows, Power Platform, etc.)—even if they don't mention \"docs.\" If the question is about understanding a concept rather than writing code, this is the right skill.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fmicrosoft-docs\u002FSKILL.md",[176],{"categoryNo":115,"categoryName":116,"taxonomyType":21,"taxonomyTypeDesc":22},[178,9,179,180,181,182,183,184,185,186,187,28,188,189,190,40,191,70,192,193,194,195,196,34],"docs","microsoft docs","MCP","writing","cli","mcp server","windows","power platform","official","documentation","learn","best practices","question","power","back","technology","platform","learn mcp","concept",{"skillNo":198,"name":199,"nameZh":6,"description":200,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":201,"categories":202,"keywords":204,"publishedAt":48},"01M69B49C3YPD1ZJ3NC0HPCY58","mcp-builder","Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP), Node\u002FTypeScript (MCP SDK), or C#\u002F.NET (Microsoft MCP SDK).","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fmcp-builder\u002FSKILL.md",[203],{"categoryNo":115,"categoryName":116,"taxonomyType":21,"taxonomyTypeDesc":22},[180,205,206,207,153,208,46,209,9,210,211,163,212,213,126,40,214,215,216,38,34,130],"mcp builder","builder","typescript","llms","model context protocol","fastmcp","Tools","node","context","integrate","mcp sdk","building mcp servers",{"skillNo":218,"name":219,"nameZh":6,"description":220,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":221,"categories":222,"keywords":235,"publishedAt":48},"01M69B467A7CDMFQ0T6Q0ZA12Q","kql","KQL language expertise for writing correct, efficient Kusto Query Language queries. Covers syntax gotchas, join patterns, dynamic types, datetime pitfalls, regex patterns, serialization, memory management, result-size discipline, and advanced functions (geo, vector, graph). USE THIS SKILL whenever writing, debugging, or reviewing KQL queries — even simple ones — because the gotchas section prevents the most common errors that waste tool calls and cause expensive retry cascades. Trigger on: KQL, Kusto, ADX, Azure Data Explorer, Fabric Real-Time Intelligence, EventHouse, Log Analytics, log analysis, data exploration, time series, anomaly detection, summarize, where clause, join, extend, project, let statement, parse operator, extract function, any mention of pipe-forward query syntax.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fkql\u002FSKILL.md",[223,226,227,232],{"categoryNo":224,"categoryName":225,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":115,"categoryName":116,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":228,"categoryName":229,"taxonomyType":230,"taxonomyTypeDesc":231},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",{"categoryNo":233,"categoryName":234,"taxonomyType":230,"taxonomyTypeDesc":231},"01JBSEEDCAT000000000000036","内容创作者",[219,181,236,237,238,28,239,240,241,242,243,244,245,246,247,248,249,250,251,34,252,253,254],"summarize","memory","analytics","vector","time series","tool","discipline","log","geo","exploration","pipe","time","project","regex","join","datetime","section","anomaly","parse",{"skillNo":256,"name":257,"nameZh":6,"description":258,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":259,"categories":260,"keywords":261,"publishedAt":48},"01M69B44HJQ7897EZ7NE7DWQX3","github-issue-creator","Convert raw notes, error logs, voice dictation, or screenshots into crisp GitHub-flavored markdown issue reports. Use when the user pastes bug info, error messages, or informal descriptions and wants a structured GitHub issue. Supports images\u002FGIFs for visual evidence.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fgithub-issue-creator\u002FSKILL.md",[],[262,263,264,265,119,266,267,268,269,270,271,272,40,273,274,275,276,277,278,34,130],"github","github issue creator","github issue","issue","issue creator","screenshots","notes","logs","error messages","messages","convert","evidence","reports","images","info","visual","error"]