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Use for NoSQL key-value storage, entity CRUD, and batch operations.\nTriggers: \"table storage\", \"TableServiceClient\", \"TableClient\", \"entities\", \"PartitionKey\", \"RowKey\".","microsoft\u002Fskills","microsoft","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6154722?v=4","01M698VYDXZ6JW5WH3CRV650R8","Microsoft","01M69AJYYW4E1RMSYCQDJ4FJSH",3202,"MIT","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills\u002Fblob\u002Fmain\u002F.github\u002Fplugins\u002Fazure-sdk-python\u002Fskills\u002Fazure-data-tables-py\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,9,41,42],"azure","azure data tables py","tables","data","operations","entities","value","for","key-value","cosmos","triggers","db","use","python","crud","nosql","batch","storage","sdk","2026-11-10 10:12:30",false,"---\nname: azure-data-tables-py\ndescription: |\n  Azure Tables SDK for Python (Storage and Cosmos DB). Use for NoSQL key-value storage, entity CRUD, and batch operations.\n  Triggers: \"table storage\", \"TableServiceClient\", \"TableClient\", \"entities\", \"PartitionKey\", \"RowKey\".\nlicense: MIT\nmetadata:\n  author: Microsoft\n  version: \"1.0.0\"\n  package: azure-data-tables\n---\n\n# Azure Tables SDK for Python\n\nNoSQL key-value store for structured data (Azure Storage Tables or Cosmos DB Table API).\n\n## Installation\n\n```bash\npip install azure-data-tables azure-identity\n```\n\n## Environment Variables\n\n```bash\n# Azure Storage Tables\nAZURE_STORAGE_ACCOUNT_URL=https:\u002F\u002F\u003Caccount>.table.core.windows.net  # Required for Azure Storage Tables\n\n# Cosmos DB Table API\nCOSMOS_TABLE_ENDPOINT=https:\u002F\u002F\u003Caccount>.table.cosmos.azure.com  # Required for Cosmos DB Table API\nAZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production\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.data.tables import TableServiceClient, TableClient\n\n# Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=\u003Cspecific_credential>\ncredential = DefaultAzureCredential(require_envvar=True)\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\nendpoint = \"https:\u002F\u002F\u003Caccount>.table.core.windows.net\"\n\n# Service client (manage tables)\nwith TableServiceClient(endpoint=endpoint, credential=credential) as service_client:\n    # Use service_client here (see following sections for operations)\n    ...\n\n# Table client (work with entities)\nwith TableClient(endpoint=endpoint, table_name=\"mytable\", credential=credential) as table_client:\n    # Use table_client here (see following sections for operations)\n    ...\n```\n\n## Client Types\n\n| Client | Purpose |\n|--------|---------|\n| `TableServiceClient` | Create\u002Fdelete tables, list tables |\n| `TableClient` | Entity CRUD, queries |\n\n## Table Operations\n\n```python\n# Create table\nservice_client.create_table(\"mytable\")\n\n# Create if not exists\nservice_client.create_table_if_not_exists(\"mytable\")\n\n# Delete table\nservice_client.delete_table(\"mytable\")\n\n# List tables\nfor table in service_client.list_tables():\n    print(table.name)\n\n# Get table client\ntable_client = service_client.get_table_client(\"mytable\")\n```\n\n## Entity Operations\n\n**Important**: Every entity requires `PartitionKey` and `RowKey` (together form unique ID).\n\n### Create Entity\n\n```python\nentity = {\n    \"PartitionKey\": \"sales\",\n    \"RowKey\": \"order-001\",\n    \"product\": \"Widget\",\n    \"quantity\": 5,\n    \"price\": 9.99,\n    \"shipped\": False\n}\n\n# Create (fails if exists)\ntable_client.create_entity(entity=entity)\n\n# Upsert (create or replace)\ntable_client.upsert_entity(entity=entity)\n```\n\n### Get Entity\n\n```python\n# Get by key (fastest)\nentity = table_client.get_entity(\n    partition_key=\"sales\",\n    row_key=\"order-001\"\n)\nprint(f\"Product: {entity['product']}\")\n```\n\n### Update Entity\n\n```python\n# Replace entire entity\nentity[\"quantity\"] = 10\ntable_client.update_entity(entity=entity, mode=\"replace\")\n\n# Merge (update specific fields only)\nupdate = {\n    \"PartitionKey\": \"sales\",\n    \"RowKey\": \"order-001\",\n    \"shipped\": True\n}\ntable_client.update_entity(entity=update, mode=\"merge\")\n```\n\n### Delete Entity\n\n```python\ntable_client.delete_entity(\n    partition_key=\"sales\",\n    row_key=\"order-001\"\n)\n```\n\n## Query Entities\n\n### Query Within Partition\n\n```python\n# Query by partition (efficient)\nentities = table_client.query_entities(\n    query_filter=\"PartitionKey eq 'sales'\"\n)\nfor entity in entities:\n    print(entity)\n```\n\n### Query with Filters\n\n```python\n# Filter by properties\nentities = table_client.query_entities(\n    query_filter=\"PartitionKey eq 'sales' and quantity gt 3\"\n)\n\n# With parameters (safer)\nentities = table_client.query_entities(\n    query_filter=\"PartitionKey eq @pk and price lt @max_price\",\n    parameters={\"pk\": \"sales\", \"max_price\": 50.0}\n)\n```\n\n### Select Specific Properties\n\n```python\nentities = table_client.query_entities(\n    query_filter=\"PartitionKey eq 'sales'\",\n    select=[\"RowKey\", \"product\", \"price\"]\n)\n```\n\n### List All Entities\n\n```python\n# List all (cross-partition - use sparingly)\nfor entity in table_client.list_entities():\n    print(entity)\n```\n\n## Batch Operations\n\n```python\nfrom azure.data.tables import TableTransactionError\n\n# Batch operations (same partition only!)\noperations = [\n    (\"create\", {\"PartitionKey\": \"batch\", \"RowKey\": \"1\", \"data\": \"first\"}),\n    (\"create\", {\"PartitionKey\": \"batch\", \"RowKey\": \"2\", \"data\": \"second\"}),\n    (\"upsert\", {\"PartitionKey\": \"batch\", \"RowKey\": \"3\", \"data\": \"third\"}),\n]\n\ntry:\n    table_client.submit_transaction(operations)\nexcept TableTransactionError as e:\n    print(f\"Transaction failed: {e}\")\n```\n\n## Async Client\n\n```python\nfrom azure.data.tables.aio import TableServiceClient, TableClient\nfrom azure.identity.aio import DefaultAzureCredential\n\nasync def table_operations():\n    async with DefaultAzureCredential() as credential:\n        async with TableClient(\n            endpoint=\"https:\u002F\u002F\u003Caccount>.table.core.windows.net\",\n            table_name=\"mytable\",\n            credential=credential\n        ) as client:\n            # Create\n            await client.create_entity(entity={\n                \"PartitionKey\": \"async\",\n                \"RowKey\": \"1\",\n                \"data\": \"test\"\n            })\n            \n            # Query\n            async for entity in client.query_entities(\"PartitionKey eq 'async'\"):\n                print(entity)\n\nimport asyncio\nasyncio.run(table_operations())\n```\n\n## Data Types\n\n| Python Type | Table Storage Type |\n|-------------|-------------------|\n| `str` | String |\n| `int` | Int64 |\n| `float` | Double |\n| `bool` | Boolean |\n| `datetime` | DateTime |\n| `bytes` | Binary |\n| `UUID` | Guid |\n\n## Best Practices\n\n1. **Pick sync OR async and stay consistent.** Do not mix `azure.data.tables` sync clients with `azure.data.tables.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 TableClient(...) as client:` (sync) or `async with TableClient(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.\n3. **Use `DefaultAzureCredential`** for portable auth across local dev and Azure (avoid connection strings \u002F API keys when possible).\n4. **Design partition keys** for query patterns and even distribution\n5. **Query within partitions** whenever possible (cross-partition is expensive)\n6. **Use batch operations** for multiple entities in same partition\n7. **Use `upsert_entity`** for idempotent writes\n8. **Use parameterized queries** to prevent injection\n9. **Keep entities small** — max 1MB per entity\n10. **Use async client** for high-throughput scenarios\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 Tables SDK for Python\n\nNoSQL key-value store for structured data (Azure Storage Tables or Cosmos DB Table API).\n\n## Installation\n\n```bash\npip install azure-data-tables azure-identity\n```\n\n## Environment Variables\n\n```bash\n# Azure Storage Tables\nAZURE_STORAGE_ACCOUNT_URL=https:\u002F\u002F\u003Caccount>.table.core.windows.net  # Required for Azure Storage Tables\n\n# Cosmos DB Table API\nCOSMOS_TABLE_ENDPOINT=https:\u002F\u002F\u003Caccount>.table.cosmos.azure.com  # Required for Cosmos DB Table API\nAZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production\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.data.tables import TableServiceClient, TableClient\n\n# Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=\u003Cspecific_credential>\ncredential = DefaultAzureCredential(require_envvar=True)\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\nendpoint = \"https:\u002F\u002F\u003Caccount>.table.core.windows.net\"\n\n# Service client (manage tables)\nwith TableServiceClient(endpoint=endpoint, credential=credential) as service_client:\n    # Use service_client here (see following sections for operations)\n    ...\n\n# Table client (work with entities)\nwith TableClient(endpoint=endpoint, table_name=\"mytable\", credential=credential) as table_client:\n    # Use table_client here (see following sections for operations)\n    ...\n```\n\n## Client Types\n\n| Client | Purpose |\n|--------|---------|\n| `TableServiceClient` | Create\u002Fdelete tables, list tables |\n| `TableClient` | Entity CRUD, queries |\n\n## Table Operations\n\n```python\n# Create table\nservice_client.create_table(\"mytable\")\n\n# Create if not exists\nservice_client.create_table_if_not_exists(\"mytable\")\n\n# Delete table\nservice_client.delete_table(\"mytable\")\n\n# List tables\nfor table in service_client.list_tables():\n    print(table.name)\n\n# Get table client\ntable_client = service_client.get_table_client(\"mytable\")\n```\n\n## Entity Operations\n\n**Important**: Every entity requires `PartitionKey` and `RowKey` (together form unique ID).\n\n### Create Entity\n\n```python\nentity = {\n    \"PartitionKey\": \"sales\",\n    \"RowKey\": \"order-001\",\n    \"product\": \"Widget\",\n    \"quantity\": 5,\n    \"price\": 9.99,\n    \"shipped\": False\n}\n\n# Create (fails if exists)\ntable_client.create_entity(entity=entity)\n\n# Upsert (create or replace)\ntable_client.upsert_entity(entity=entity)\n```\n\n### Get Entity\n\n```python\n# Get by key (fastest)\nentity = table_client.get_entity(\n    partition_key=\"sales\",\n    row_key=\"order-001\"\n)\nprint(f\"Product: {entity['product']}\")\n```\n\n### Update Entity\n\n```python\n# Replace entire entity\nentity[\"quantity\"] = 10\ntable_client.update_entity(entity=entity, mode=\"replace\")\n\n# Merge (update specific fields only)\nupdate = {\n    \"PartitionKey\": \"sales\",\n    \"RowKey\": \"order-001\",\n    \"shipped\": True\n}\ntable_client.update_entity(entity=update, mode=\"merge\")\n```\n\n### Delete Entity\n\n```python\ntable_client.delete_entity(\n    partition_key=\"sales\",\n    row_key=\"order-001\"\n)\n```\n\n## Query Entities\n\n### Query Within Partition\n\n```python\n# Query by partition (efficient)\nentities = table_client.query_entities(\n    query_filter=\"PartitionKey eq 'sales'\"\n)\nfor entity in entities:\n    print(entity)\n```\n\n### Query with Filters\n\n```python\n# Filter by properties\nentities = table_client.query_entities(\n    query_filter=\"PartitionKey eq 'sales' and quantity gt 3\"\n)\n\n# With parameters (safer)\nentities = table_client.query_entities(\n    query_filter=\"PartitionKey eq @pk and price lt @max_price\",\n    parameters={\"pk\": \"sales\", \"max_price\": 50.0}\n)\n```\n\n### Select Specific Properties\n\n```python\nentities = table_client.query_entities(\n    query_filter=\"PartitionKey eq 'sales'\",\n    select=[\"RowKey\", \"product\", \"price\"]\n)\n```\n\n### List All Entities\n\n```python\n# List all (cross-partition - use sparingly)\nfor entity in table_client.list_entities():\n    print(entity)\n```\n\n## Batch Operations\n\n```python\nfrom azure.data.tables import TableTransactionError\n\n# Batch operations (same partition only!)\noperations = [\n    (\"create\", {\"PartitionKey\": \"batch\", \"RowKey\": \"1\", \"data\": \"first\"}),\n    (\"create\", {\"PartitionKey\": \"batch\", \"RowKey\": \"2\", \"data\": \"second\"}),\n    (\"upsert\", {\"PartitionKey\": \"batch\", \"RowKey\": \"3\", \"data\": \"third\"}),\n]\n\ntry:\n    table_client.submit_transaction(operations)\nexcept TableTransactionError as e:\n    print(f\"Transaction failed: {e}\")\n```\n\n## Async Client\n\n```python\nfrom azure.data.tables.aio import TableServiceClient, TableClient\nfrom azure.identity.aio import DefaultAzureCredential\n\nasync def table_operations():\n    async with DefaultAzureCredential() as credential:\n        async with TableClient(\n            endpoint=\"https:\u002F\u002F\u003Caccount>.table.core.windows.net\",\n            table_name=\"mytable\",\n            credential=credential\n        ) as client:\n            # Create\n            await client.create_entity(entity={\n                \"PartitionKey\": \"async\",\n                \"RowKey\": \"1\",\n                \"data\": \"test\"\n            })\n            \n            # Query\n            async for entity in client.query_entities(\"PartitionKey eq 'async'\"):\n                print(entity)\n\nimport asyncio\nasyncio.run(table_operations())\n```\n\n## Data Types\n\n| Python Type | Table Storage Type |\n|-------------|-------------------|\n| `str` | String |\n| `int` | Int64 |\n| `float` | Double |\n| `bool` | Boolean |\n| `datetime` | DateTime |\n| `bytes` | Binary |\n| `UUID` | Guid |\n\n## Best Practices\n\n1. **Pick sync OR async and stay consistent.** Do not mix `azure.data.tables` sync clients with `azure.data.tables.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 TableClient(...) as client:` (sync) or `async with TableClient(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.\n3. **Use `DefaultAzureCredential`** for portable auth across local dev and Azure (avoid connection strings \u002F API keys when possible).\n4. **Design partition keys** for query patterns and even distribution\n5. **Query within partitions** whenever possible (cross-partition is expensive)\n6. **Use batch operations** for multiple entities in same partition\n7. **Use `upsert_entity`** for idempotent writes\n8. **Use parameterized queries** to prevent injection\n9. **Keep entities small** — max 1MB per entity\n10. **Use async client** for high-throughput scenarios\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":48,"description":51},{"author":12,"package":49,"version":50},"azure-data-tables","1.0.0","Azure Tables SDK for Python (Storage and Cosmos DB). 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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",[120,123,126,129],{"categoryNo":121,"categoryName":122,"taxonomyType":96,"taxonomyTypeDesc":97},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":124,"categoryName":125,"taxonomyType":96,"taxonomyTypeDesc":97},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":127,"categoryName":128,"taxonomyType":96,"taxonomyTypeDesc":97},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":94,"categoryName":95,"taxonomyType":96,"taxonomyTypeDesc":97},[131,132,133,134,37,135,136,24,137,138,139,140,141,142,143,144,145,146,147,148,149,112,150],"podcast generation","podcast","generation","frontend","react","realtime","azure openai","generate","streaming","api","Audio","stack","text","model","fastapi","creation","python fastapi","narrative","features","fastapi backend",{"skillNo":152,"name":153,"nameZh":6,"description":154,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":155,"categories":156,"keywords":158,"publishedAt":43},"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",[157],{"categoryNo":94,"categoryName":95,"taxonomyType":96,"taxonomyTypeDesc":97},[159,9,160,161,162,163,164,165,166,167,168,24,169,170,171,36,172,65,173,174,175,176,177,31],"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":179,"name":180,"nameZh":6,"description":181,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":182,"categories":183,"keywords":185,"publishedAt":43},"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",[184],{"categoryNo":94,"categoryName":95,"taxonomyType":96,"taxonomyTypeDesc":97},[161,186,187,188,37,189,42,190,9,191,192,144,193,194,107,36,195,196,197,109,31,112],"mcp builder","builder","typescript","llms","model context protocol","fastmcp","Tools","node","context","integrate","mcp sdk","building mcp servers",{"skillNo":199,"name":200,"nameZh":6,"description":201,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":202,"categories":203,"keywords":212,"publishedAt":43},"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",[204,207,208,209],{"categoryNo":205,"categoryName":206,"taxonomyType":96,"taxonomyTypeDesc":97},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":94,"categoryName":95,"taxonomyType":96,"taxonomyTypeDesc":97},{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":210,"categoryName":211,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000036","内容创作者",[200,162,213,214,215,24,216,217,218,219,220,221,222,223,224,225,226,227,228,31,229,230,231],"summarize","memory","analytics","vector","time series","tool","discipline","log","geo","exploration","pipe","time","project","regex","join","datetime","section","anomaly","parse",{"skillNo":233,"name":234,"nameZh":6,"description":235,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":236,"categories":237,"keywords":238,"publishedAt":43},"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",[],[239,240,241,242,100,243,244,245,246,247,248,249,36,250,251,252,253,254,255,31,112],"github","github issue creator","github issue","issue","issue creator","screenshots","notes","logs","error messages","messages","convert","evidence","reports","images","info","visual","error"]