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Use for low-level OpenTelemetry export to Application Insights.\nTriggers: \"azure-monitor-opentelemetry-exporter\", \"AzureMonitorTraceExporter\", \"AzureMonitorMetricExporter\", \"AzureMonitorLogExporter\".","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-monitor-opentelemetry-exporter-py\u002FSKILL.md",[],[19,20,21,22,23,24,9,25,26,27,28,29,30,31],"azure","azure monitor opentelemetry exporter py","monitor","OpenTelemetry","azure monitor","python","azure-monitor","use","export","application","triggers","insights","for","2026-11-10 10:12:30",false,"---\nname: azure-monitor-opentelemetry-exporter-py\ndescription: |\n  Azure Monitor OpenTelemetry Exporter for Python. Use for low-level OpenTelemetry export to Application Insights.\n  Triggers: \"azure-monitor-opentelemetry-exporter\", \"AzureMonitorTraceExporter\", \"AzureMonitorMetricExporter\", \"AzureMonitorLogExporter\".\nlicense: MIT\nmetadata:\n  author: Microsoft\n  version: \"1.0.0\"\n  package: azure-monitor-opentelemetry-exporter\n---\n\n# Azure Monitor OpenTelemetry Exporter for Python\n\nLow-level exporter for sending OpenTelemetry traces, metrics, and logs to Application Insights.\n\n## Installation\n\n```bash\npip install azure-monitor-opentelemetry-exporter\n```\n\n## Environment Variables\n\n```bash\nAPPLICATIONINSIGHTS_CONNECTION_STRING=InstrumentationKey=xxx;IngestionEndpoint=https:\u002F\u002Fxxx.in.applicationinsights.azure.com\u002F  # Required for all auth methods\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` for ingestion auth when supported.** `APPLICATIONINSIGHTS_CONNECTION_STRING` identifies the target Application Insights resource, and `credential=DefaultAzureCredential(...)` provides Microsoft Entra authentication.\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. **Providers are not context managers.** Flush and shut down telemetry providers explicitly at process exit so buffers are exported deterministically.\n>\n> Snippets may abbreviate this setup, but production code should always follow both rules.\n\n## When to Use\n\n| Scenario | Use |\n|----------|-----|\n| Quick setup, auto-instrumentation | `azure-monitor-opentelemetry` (distro) |\n| Custom OpenTelemetry pipeline | `azure-monitor-opentelemetry-exporter` (this) |\n| Fine-grained control over telemetry | `azure-monitor-opentelemetry-exporter` (this) |\n\n## Trace Exporter\n\n```python\nfrom azure.identity import DefaultAzureCredential\nfrom opentelemetry import trace\nfrom opentelemetry.sdk.trace import TracerProvider\nfrom opentelemetry.sdk.trace.export import BatchSpanProcessor\nfrom azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter\n\n# Reads APPLICATIONINSIGHTS_CONNECTION_STRING from env to identify the resource;\n# DefaultAzureCredential authenticates ingestion via Microsoft Entra ID.\nexporter = AzureMonitorTraceExporter(\n    credential=DefaultAzureCredential(),\n)\n\n# Configure tracer provider\ntrace.set_tracer_provider(TracerProvider())\ntrace.get_tracer_provider().add_span_processor(\n    BatchSpanProcessor(exporter)\n)\n\n# Use tracer\ntracer = trace.get_tracer(__name__)\nwith tracer.start_as_current_span(\"my-span\"):\n    print(\"Hello, World!\")\n```\n\n## Metric Exporter\n\n```python\nfrom azure.identity import DefaultAzureCredential\nfrom opentelemetry import metrics\nfrom opentelemetry.sdk.metrics import MeterProvider\nfrom opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader\nfrom azure.monitor.opentelemetry.exporter import AzureMonitorMetricExporter\n\n# Reads APPLICATIONINSIGHTS_CONNECTION_STRING from env; AAD-authenticated ingestion via DefaultAzureCredential.\nexporter = AzureMonitorMetricExporter(\n    credential=DefaultAzureCredential(),\n)\n\n# Configure meter provider\nreader = PeriodicExportingMetricReader(exporter, export_interval_millis=60000)\nmetrics.set_meter_provider(MeterProvider(metric_readers=[reader]))\n\n# Use meter\nmeter = metrics.get_meter(__name__)\ncounter = meter.create_counter(\"requests_total\")\ncounter.add(1, {\"route\": \"\u002Fapi\u002Fusers\"})\n```\n\n## Log Exporter\n\n```python\nimport logging\nfrom azure.identity import DefaultAzureCredential\nfrom opentelemetry._logs import set_logger_provider\nfrom opentelemetry.sdk._logs import LoggerProvider, LoggingHandler\nfrom opentelemetry.sdk._logs.export import BatchLogRecordProcessor\nfrom azure.monitor.opentelemetry.exporter import AzureMonitorLogExporter\n\n# Reads APPLICATIONINSIGHTS_CONNECTION_STRING from env; AAD-authenticated ingestion via DefaultAzureCredential.\nexporter = AzureMonitorLogExporter(\n    credential=DefaultAzureCredential(),\n)\n\n# Configure logger provider\nlogger_provider = LoggerProvider()\nlogger_provider.add_log_record_processor(BatchLogRecordProcessor(exporter))\nset_logger_provider(logger_provider)\n\n# Add handler to Python logging\nhandler = LoggingHandler(level=logging.INFO, logger_provider=logger_provider)\nlogging.getLogger().addHandler(handler)\n\n# Use logging\nlogger = logging.getLogger(__name__)\nlogger.info(\"This will be sent to Application Insights\")\n```\n\n## From Environment Variable\n\nExporters read `APPLICATIONINSIGHTS_CONNECTION_STRING` automatically:\n\n```python\nfrom azure.identity import DefaultAzureCredential\nfrom azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter\n\n# Connection string from environment; AAD-authenticated ingestion via DefaultAzureCredential.\nexporter = AzureMonitorTraceExporter(\n    credential=DefaultAzureCredential(),\n)\n```\n\n## Azure AD Authentication\n\n```python\nfrom azure.identity import DefaultAzureCredential, ManagedIdentityCredential\nfrom azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter\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\nexporter = AzureMonitorTraceExporter(\n    credential=credential\n)\n```\n\n## Sampling\n\nUse `ApplicationInsightsSampler` for consistent sampling:\n\n```python\nfrom opentelemetry.sdk.trace import TracerProvider\nfrom opentelemetry.sdk.trace.sampling import ParentBasedTraceIdRatio\nfrom azure.monitor.opentelemetry.exporter import ApplicationInsightsSampler\n\n# Sample 10% of traces\nsampler = ApplicationInsightsSampler(sampling_ratio=0.1)\n\ntrace.set_tracer_provider(TracerProvider(sampler=sampler))\n```\n\n## Offline Storage\n\nConfigure offline storage for retry:\n\n```python\nfrom azure.identity import DefaultAzureCredential\nfrom azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter\n\nexporter = AzureMonitorTraceExporter(\n    credential=DefaultAzureCredential(),\n    storage_directory=\"\u002Fpath\u002Fto\u002Fstorage\",  # Custom storage path\n    disable_offline_storage=False  # Enable retry (default)\n)\n```\n\n## Disable Offline Storage\n\n```python\nexporter = AzureMonitorTraceExporter(\n    credential=DefaultAzureCredential(),\n    disable_offline_storage=True  # No retry on failure\n)\n```\n\n## Sovereign Clouds\n\n```python\nfrom azure.identity import AzureAuthorityHosts, DefaultAzureCredential\nfrom azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter\n\n# Azure Government\ncredential = DefaultAzureCredential(authority=AzureAuthorityHosts.AZURE_GOVERNMENT)\nexporter = AzureMonitorTraceExporter(\n    connection_string=\"InstrumentationKey=xxx;IngestionEndpoint=https:\u002F\u002Fxxx.in.applicationinsights.azure.us\u002F\",\n    credential=credential\n)\n```\n\n## Exporter Types\n\n| Exporter | Telemetry Type | Application Insights Table |\n|----------|---------------|---------------------------|\n| `AzureMonitorTraceExporter` | Traces\u002FSpans | requests, dependencies, exceptions |\n| `AzureMonitorMetricExporter` | Metrics | customMetrics, performanceCounters |\n| `AzureMonitorLogExporter` | Logs | traces, customEvents |\n\n## Configuration Options\n\n| Parameter | Description | Default |\n|-----------|-------------|---------|\n| `connection_string` | Application Insights connection string | From env var |\n| `credential` | Azure credential for AAD auth | None |\n| `disable_offline_storage` | Disable retry storage | False |\n| `storage_directory` | Custom storage path | Temp directory |\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. **Call `provider.shutdown()` \u002F `force_flush()` at process exit to flush telemetry — providers are not context managers.**\n3. **Use BatchSpanProcessor** for production (not SimpleSpanProcessor)\n4. **Use ApplicationInsightsSampler** for consistent sampling across services\n5. **Enable offline storage** for reliability in production\n6. **Use Microsoft Entra authentication** instead of instrumentation keys\n7. **Set export intervals** appropriate for your workload\n8. **Use the distro** (`azure-monitor-opentelemetry`) unless you need custom pipelines\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 Monitor OpenTelemetry Exporter for Python\n\nLow-level exporter for sending OpenTelemetry traces, metrics, and logs to Application Insights.\n\n## Installation\n\n```bash\npip install azure-monitor-opentelemetry-exporter\n```\n\n## Environment Variables\n\n```bash\nAPPLICATIONINSIGHTS_CONNECTION_STRING=InstrumentationKey=xxx;IngestionEndpoint=https:\u002F\u002Fxxx.in.applicationinsights.azure.com\u002F  # Required for all auth methods\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` for ingestion auth when supported.** `APPLICATIONINSIGHTS_CONNECTION_STRING` identifies the target Application Insights resource, and `credential=DefaultAzureCredential(...)` provides Microsoft Entra authentication.\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. **Providers are not context managers.** Flush and shut down telemetry providers explicitly at process exit so buffers are exported deterministically.\n>\n> Snippets may abbreviate this setup, but production code should always follow both rules.\n\n## When to Use\n\n| Scenario | Use |\n|----------|-----|\n| Quick setup, auto-instrumentation | `azure-monitor-opentelemetry` (distro) |\n| Custom OpenTelemetry pipeline | `azure-monitor-opentelemetry-exporter` (this) |\n| Fine-grained control over telemetry | `azure-monitor-opentelemetry-exporter` (this) |\n\n## Trace Exporter\n\n```python\nfrom azure.identity import DefaultAzureCredential\nfrom opentelemetry import trace\nfrom opentelemetry.sdk.trace import TracerProvider\nfrom opentelemetry.sdk.trace.export import BatchSpanProcessor\nfrom azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter\n\n# Reads APPLICATIONINSIGHTS_CONNECTION_STRING from env to identify the resource;\n# DefaultAzureCredential authenticates ingestion via Microsoft Entra ID.\nexporter = AzureMonitorTraceExporter(\n    credential=DefaultAzureCredential(),\n)\n\n# Configure tracer provider\ntrace.set_tracer_provider(TracerProvider())\ntrace.get_tracer_provider().add_span_processor(\n    BatchSpanProcessor(exporter)\n)\n\n# Use tracer\ntracer = trace.get_tracer(__name__)\nwith tracer.start_as_current_span(\"my-span\"):\n    print(\"Hello, World!\")\n```\n\n## Metric Exporter\n\n```python\nfrom azure.identity import DefaultAzureCredential\nfrom opentelemetry import metrics\nfrom opentelemetry.sdk.metrics import MeterProvider\nfrom opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader\nfrom azure.monitor.opentelemetry.exporter import AzureMonitorMetricExporter\n\n# Reads APPLICATIONINSIGHTS_CONNECTION_STRING from env; AAD-authenticated ingestion via DefaultAzureCredential.\nexporter = AzureMonitorMetricExporter(\n    credential=DefaultAzureCredential(),\n)\n\n# Configure meter provider\nreader = PeriodicExportingMetricReader(exporter, export_interval_millis=60000)\nmetrics.set_meter_provider(MeterProvider(metric_readers=[reader]))\n\n# Use meter\nmeter = metrics.get_meter(__name__)\ncounter = meter.create_counter(\"requests_total\")\ncounter.add(1, {\"route\": \"\u002Fapi\u002Fusers\"})\n```\n\n## Log Exporter\n\n```python\nimport logging\nfrom azure.identity import DefaultAzureCredential\nfrom opentelemetry._logs import set_logger_provider\nfrom opentelemetry.sdk._logs import LoggerProvider, LoggingHandler\nfrom opentelemetry.sdk._logs.export import BatchLogRecordProcessor\nfrom azure.monitor.opentelemetry.exporter import AzureMonitorLogExporter\n\n# Reads APPLICATIONINSIGHTS_CONNECTION_STRING from env; AAD-authenticated ingestion via DefaultAzureCredential.\nexporter = AzureMonitorLogExporter(\n    credential=DefaultAzureCredential(),\n)\n\n# Configure logger provider\nlogger_provider = LoggerProvider()\nlogger_provider.add_log_record_processor(BatchLogRecordProcessor(exporter))\nset_logger_provider(logger_provider)\n\n# Add handler to Python logging\nhandler = LoggingHandler(level=logging.INFO, logger_provider=logger_provider)\nlogging.getLogger().addHandler(handler)\n\n# Use logging\nlogger = logging.getLogger(__name__)\nlogger.info(\"This will be sent to Application Insights\")\n```\n\n## From Environment Variable\n\nExporters read `APPLICATIONINSIGHTS_CONNECTION_STRING` automatically:\n\n```python\nfrom azure.identity import DefaultAzureCredential\nfrom azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter\n\n# Connection string from environment; AAD-authenticated ingestion via DefaultAzureCredential.\nexporter = AzureMonitorTraceExporter(\n    credential=DefaultAzureCredential(),\n)\n```\n\n## Azure AD Authentication\n\n```python\nfrom azure.identity import DefaultAzureCredential, ManagedIdentityCredential\nfrom azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter\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\nexporter = AzureMonitorTraceExporter(\n    credential=credential\n)\n```\n\n## Sampling\n\nUse `ApplicationInsightsSampler` for consistent sampling:\n\n```python\nfrom opentelemetry.sdk.trace import TracerProvider\nfrom opentelemetry.sdk.trace.sampling import ParentBasedTraceIdRatio\nfrom azure.monitor.opentelemetry.exporter import ApplicationInsightsSampler\n\n# Sample 10% of traces\nsampler = ApplicationInsightsSampler(sampling_ratio=0.1)\n\ntrace.set_tracer_provider(TracerProvider(sampler=sampler))\n```\n\n## Offline Storage\n\nConfigure offline storage for retry:\n\n```python\nfrom azure.identity import DefaultAzureCredential\nfrom azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter\n\nexporter = AzureMonitorTraceExporter(\n    credential=DefaultAzureCredential(),\n    storage_directory=\"\u002Fpath\u002Fto\u002Fstorage\",  # Custom storage path\n    disable_offline_storage=False  # Enable retry (default)\n)\n```\n\n## Disable Offline Storage\n\n```python\nexporter = AzureMonitorTraceExporter(\n    credential=DefaultAzureCredential(),\n    disable_offline_storage=True  # No retry on failure\n)\n```\n\n## Sovereign Clouds\n\n```python\nfrom azure.identity import AzureAuthorityHosts, DefaultAzureCredential\nfrom azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter\n\n# Azure Government\ncredential = DefaultAzureCredential(authority=AzureAuthorityHosts.AZURE_GOVERNMENT)\nexporter = AzureMonitorTraceExporter(\n    connection_string=\"InstrumentationKey=xxx;IngestionEndpoint=https:\u002F\u002Fxxx.in.applicationinsights.azure.us\u002F\",\n    credential=credential\n)\n```\n\n## Exporter Types\n\n| Exporter | Telemetry Type | Application Insights Table |\n|----------|---------------|---------------------------|\n| `AzureMonitorTraceExporter` | Traces\u002FSpans | requests, dependencies, exceptions |\n| `AzureMonitorMetricExporter` | Metrics | customMetrics, performanceCounters |\n| `AzureMonitorLogExporter` | Logs | traces, customEvents |\n\n## Configuration Options\n\n| Parameter | Description | Default |\n|-----------|-------------|---------|\n| `connection_string` | Application Insights connection string | From env var |\n| `credential` | Azure credential for AAD auth | None |\n| `disable_offline_storage` | Disable retry storage | False |\n| `storage_directory` | Custom storage path | Temp directory |\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. **Call `provider.shutdown()` \u002F `force_flush()` at process exit to flush telemetry — providers are not context managers.**\n3. **Use BatchSpanProcessor** for production (not SimpleSpanProcessor)\n4. **Use ApplicationInsightsSampler** for consistent sampling across services\n5. **Enable offline storage** for reliability in production\n6. **Use Microsoft Entra authentication** instead of instrumentation keys\n7. **Set export intervals** appropriate for your workload\n8. **Use the distro** (`azure-monitor-opentelemetry`) unless you need custom pipelines\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":37,"description":40},{"author":12,"package":38,"version":39},"azure-monitor-opentelemetry-exporter","1.0.0","Azure Monitor OpenTelemetry Exporter for Python. 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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",[109,112,115,118],{"categoryNo":110,"categoryName":111,"taxonomyType":85,"taxonomyTypeDesc":86},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":113,"categoryName":114,"taxonomyType":85,"taxonomyTypeDesc":86},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":116,"categoryName":117,"taxonomyType":85,"taxonomyTypeDesc":86},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":83,"categoryName":84,"taxonomyType":85,"taxonomyTypeDesc":86},[120,121,122,123,24,124,125,19,126,127,128,129,130,131,132,133,134,135,136,137,138,101,139],"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":141,"name":142,"nameZh":6,"description":143,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":144,"categories":145,"keywords":147,"publishedAt":32},"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",[146],{"categoryNo":83,"categoryName":84,"taxonomyType":85,"taxonomyTypeDesc":86},[148,9,149,150,151,152,153,154,155,156,157,19,158,159,160,26,161,54,162,163,164,165,166,31],"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":168,"name":169,"nameZh":6,"description":170,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":171,"categories":172,"keywords":174,"publishedAt":32},"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",[173],{"categoryNo":83,"categoryName":84,"taxonomyType":85,"taxonomyTypeDesc":86},[150,175,176,177,24,178,179,180,9,181,182,133,183,184,96,26,185,186,187,98,31,101],"mcp builder","builder","typescript","llms","sdk","model context protocol","fastmcp","Tools","node","context","integrate","mcp sdk","building mcp servers",{"skillNo":189,"name":190,"nameZh":6,"description":191,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":192,"categories":193,"keywords":206,"publishedAt":32},"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",[194,197,198,203],{"categoryNo":195,"categoryName":196,"taxonomyType":85,"taxonomyTypeDesc":86},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":83,"categoryName":84,"taxonomyType":85,"taxonomyTypeDesc":86},{"categoryNo":199,"categoryName":200,"taxonomyType":201,"taxonomyTypeDesc":202},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",{"categoryNo":204,"categoryName":205,"taxonomyType":201,"taxonomyTypeDesc":202},"01JBSEEDCAT000000000000036","内容创作者",[190,151,207,208,209,19,210,211,212,213,214,215,216,217,218,219,220,221,222,31,223,224,225],"summarize","memory","analytics","vector","time series","tool","discipline","log","geo","exploration","pipe","time","project","regex","join","datetime","section","anomaly","parse",{"skillNo":227,"name":228,"nameZh":6,"description":229,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":230,"categories":231,"keywords":232,"publishedAt":32},"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",[],[233,234,235,236,89,237,238,239,240,241,242,243,26,244,245,246,247,248,249,31,101],"github","github issue creator","github issue","issue","issue creator","screenshots","notes","logs","error messages","messages","convert","evidence","reports","images","info","visual","error"]