[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-microsoft-azure-kusto":3,"mdc--blbaxj-key":34,"related-repo-microsoft-azure-kusto":1747,"related-org-microsoft-azure-kusto":1879},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":22,"repoUrl":23,"updatedAt":24,"license":25,"forks":26,"topics":27,"repo":29,"sourceUrl":32,"mdContent":33},"azure-kusto","query Azure Data Explorer with KQL","Query and analyze data in Azure Data Explorer (Kusto\u002FADX) using KQL for log analytics, telemetry, and time series analysis. WHEN: KQL queries, Kusto database queries, Azure Data Explorer, ADX clusters, log analytics, time series data, IoT telemetry, anomaly detection.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},"microsoft","Microsoft","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fmicrosoft.png",[12,16,19],{"name":13,"slug":14,"type":15},"Azure","azure","tag",{"name":17,"slug":18,"type":15},"Data Analysis","data-analysis",{"name":20,"slug":21,"type":15},"SQL","sql",1324,"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fazure-skills","2026-07-31T05:56:27.062689","MIT",220,[28],"agent-skills",{"repoUrl":23,"stars":22,"forks":26,"topics":30,"description":31},[28],"Official agent plugin providing skills and MCP server configurations for Azure scenarios.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fazure-skills\u002Ftree\u002FHEAD\u002Fskills\u002Fazure-kusto","---\nname: azure-kusto\ndescription: \"Query and analyze data in Azure Data Explorer (Kusto\u002FADX) using KQL for log analytics, telemetry, and time series analysis. WHEN: KQL queries, Kusto database queries, Azure Data Explorer, ADX clusters, log analytics, time series data, IoT telemetry, anomaly detection.\"\nlicense: MIT\nmetadata:\n  author: Microsoft\n  version: \"1.2.1\"\n---\n\n# Azure Data Explorer (Kusto) Query & Analytics\n\nExecute KQL queries and manage Azure Data Explorer resources for fast, scalable big data analytics on log, telemetry, and time series data.\n\n## Skill Activation Triggers\n\n**Use this skill immediately when the user asks to:**\n- \"Query my Kusto database for [data pattern]\"\n- \"Show me events in the last hour from Azure Data Explorer\"\n- \"Analyze logs in my ADX cluster\"\n- \"Run a KQL query on [database]\"\n- \"What tables are in my Kusto database?\"\n- \"Show me the schema for [table]\"\n- \"List my Azure Data Explorer clusters\"\n- \"Aggregate telemetry data by [dimension]\"\n- \"Create a time series chart from my logs\"\n\n**Key Indicators:**\n- Mentions \"Kusto\", \"Azure Data Explorer\", \"ADX\", or \"KQL\"\n- Log analytics or telemetry analysis requests\n- Time series data exploration\n- IoT data analysis queries\n- SIEM or security analytics tasks\n- Requests for data aggregation on large datasets\n- Performance monitoring or APM queries\n\n## Overview\n\nThis skill enables querying and managing Azure Data Explorer (Kusto), a fast and highly scalable data exploration service optimized for log and telemetry data. Azure Data Explorer provides sub-second query performance on billions of records using the Kusto Query Language (KQL).\n\nKey capabilities:\n- **Query Execution**: Run KQL queries against massive datasets\n- **Schema Exploration**: Discover tables, columns, and data types\n- **Resource Management**: List clusters and databases\n- **Analytics**: Aggregations, time series, anomaly detection, machine learning\n\n## Core Workflow\n\n1. **Discover Resources**: List available clusters and databases in subscription\n2. **Explore Schema**: Retrieve table structures to understand data model\n3. **Query Data**: Execute KQL queries for analysis, filtering, aggregation\n4. **Analyze Results**: Process query output for insights and reporting\n\n## Query Patterns\n\n### Pattern 1: Basic Data Retrieval\nFetch recent records from a table with simple filtering.\n\n**Example KQL**:\n```kql\nEvents\n| where Timestamp > ago(1h)\n| take 100\n```\n\n**Use for**: Quick data inspection, recent event retrieval\n\n### Pattern 2: Aggregation Analysis\nSummarize data by dimensions for insights and reporting.\n\n**Example KQL**:\n```kql\nEvents\n| summarize count() by EventType, bin(Timestamp, 1h)\n| order by count_ desc\n```\n\n**Use for**: Event counting, distribution analysis, top-N queries\n\n### Pattern 3: Time Series Analytics\nAnalyze data over time windows for trends and patterns.\n\n**Example KQL**:\n```kql\nTelemetry\n| where Timestamp > ago(24h)\n| summarize avg(ResponseTime), percentiles(ResponseTime, 50, 95, 99) by bin(Timestamp, 5m)\n| render timechart\n```\n\n**Use for**: Performance monitoring, trend analysis, anomaly detection\n\n### Pattern 4: Join and Correlation\nCombine multiple tables for cross-dataset analysis.\n\n**Example KQL**:\n```kql\nEvents\n| where EventType == \"Error\"\n| join kind=inner (\n    Logs\n    | where Severity == \"Critical\"\n) on CorrelationId\n| project Timestamp, EventType, LogMessage, Severity\n```\n\n**Use for**: Root cause analysis, correlated event tracking\n\n### Pattern 5: Schema Discovery\nExplore table structure before querying.\n\n**Tools**: `kusto_table_schema_get`\n\n**Use for**: Understanding data model, query planning\n\n## Key Data Fields\n\nWhen executing queries, common field patterns:\n- **Timestamp**: Time of event (datetime) - use `ago()`, `between()`, `bin()` for time filtering\n- **EventType\u002FCategory**: Classification field for grouping\n- **CorrelationId\u002FSessionId**: For tracing related events\n- **Severity\u002FLevel**: For filtering by importance\n- **Dimensions**: Custom properties for grouping and filtering\n\n## Result Format\n\nQuery results include:\n- **Columns**: Field names and data types\n- **Rows**: Data records matching query\n- **Statistics**: Row count, execution time, resource utilization\n- **Visualization**: Chart rendering hints (timechart, barchart, etc.)\n\n## KQL Best Practices\n\n**🟢 Performance Optimized:**\n- Filter early: Use `where` before joins and aggregations\n- Limit result size: Use `take` or `limit` to reduce data transfer\n- Time filters: Always filter by time range for time series data\n- Indexed columns: Filter on indexed columns first\n\n**🔵 Query Patterns:**\n- Use `summarize` for aggregations instead of `count()` alone\n- Use `bin()` for time bucketing in time series\n- Use `project` to select only needed columns\n- Use `extend` to add calculated fields\n\n**🟡 Common Functions:**\n- `ago(timespan)`: Relative time (ago(1h), ago(7d))\n- `between(start .. end)`: Range filtering\n- `startswith()`, `contains()`, `matches regex`: String filtering\n- `parse`, `extract`: Extract values from strings\n- `percentiles()`, `avg()`, `sum()`, `max()`, `min()`: Aggregations\n\n## Best Practices\n\n- Always include time range filters to optimize query performance\n- Use `take` or `limit` for exploratory queries to avoid large result sets\n- Leverage `summarize` for aggregations instead of client-side processing\n- Store frequently-used queries as functions in the database\n- Use materialized views for repeated aggregations\n- Monitor query performance and resource consumption\n- Apply data retention policies to manage storage costs\n- Use streaming ingestion for real-time analytics (\u003C 1 second latency)\n- Integrate with Azure Monitor for operational insights\n\n## MCP Tools Used\n\n| Tool | Purpose |\n|------|---------|\n| `kusto_cluster_list` | List all Azure Data Explorer clusters in a subscription |\n| `kusto_database_list` | List all databases in a specific Kusto cluster |\n| `kusto_query` | Execute KQL queries against a Kusto database |\n| `kusto_table_schema_get` | Retrieve schema information for a specific table |\n\n**Required Parameters**:\n- `subscription`: Azure subscription ID or display name\n- `cluster`: Kusto cluster name (e.g., \"mycluster\")\n- `database`: Database name\n- `query`: KQL query string (for query operations)\n- `table`: Table name (for schema operations)\n\n**Optional Parameters**:\n- `resource-group`: Resource group name (for listing operations)\n- `tenant`: Azure AD tenant ID\n\n## Fallback Strategy: Azure CLI Commands\n\nIf Azure MCP Kusto tools fail, timeout, or are unavailable, use Azure CLI commands as fallback.\n\n### CLI Command Reference\n\n| Operation | Azure CLI Command |\n|-----------|-------------------|\n| List clusters | `az kusto cluster list --resource-group \u003Crg-name>` |\n| List databases | `az kusto database list --cluster-name \u003Ccluster> --resource-group \u003Crg-name>` |\n| Show cluster | `az kusto cluster show --name \u003Ccluster> --resource-group \u003Crg-name>` |\n| Show database | `az kusto database show --cluster-name \u003Ccluster> --database-name \u003Cdb> --resource-group \u003Crg-name>` |\n\n### KQL Query via Azure CLI\n\nFor queries, use the Kusto REST API or direct cluster URL:\n```bash\naz rest --method post \\\n  --url \"https:\u002F\u002F\u003Ccluster>.\u003Cregion>.kusto.windows.net\u002Fv1\u002Frest\u002Fquery\" \\\n  --body \"{ \\\"db\\\": \\\"\u003Cdatabase>\\\", \\\"csl\\\": \\\"\u003Ckql-query>\\\" }\"\n```\n\n### When to Fallback\n\nSwitch to Azure CLI when:\n- MCP tool returns timeout error (queries > 60 seconds)\n- MCP tool returns \"service unavailable\" or connection errors\n- Authentication failures with MCP tools\n- Empty response when database is known to have data\n\n## Common Issues\n\n- **Access Denied**: Verify database permissions (Viewer role minimum for queries)\n- **Query Timeout**: Optimize query with time filters, reduce result set, or increase timeout\n- **Syntax Error**: Validate KQL syntax - common issues: missing pipes, incorrect operators\n- **Empty Results**: Check time range filters (may be too restrictive), verify table name\n- **Cluster Not Found**: Check cluster name format (exclude \".kusto.windows.net\" suffix)\n- **High CPU Usage**: Query too broad - add filters, reduce time range, limit aggregations\n- **Ingestion Lag**: Streaming data may have 1-30 second delay depending on ingestion method\n\n## Use Cases\n\n- **Log Analytics**: Application logs, system logs, audit logs\n- **IoT Analytics**: Sensor data, device telemetry, real-time monitoring\n- **Security Analytics**: SIEM data, threat detection, security event correlation\n- **APM**: Application performance metrics, user behavior, error tracking\n- **Business Intelligence**: Clickstream analysis, user analytics, operational 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}",{"type":42,"tag":83,"props":1565,"children":1566},{"style":1460},[1567],{"type":48,"value":1568},"\"\n",{"type":42,"tag":310,"props":1570,"children":1572},{"id":1571},"when-to-fallback",[1573],{"type":48,"value":1574},"When to Fallback",{"type":42,"tag":51,"props":1576,"children":1577},{},[1578],{"type":48,"value":1579},"Switch to Azure CLI when:",{"type":42,"tag":73,"props":1581,"children":1582},{},[1583,1588,1593,1598],{"type":42,"tag":77,"props":1584,"children":1585},{},[1586],{"type":48,"value":1587},"MCP tool returns timeout error (queries > 60 seconds)",{"type":42,"tag":77,"props":1589,"children":1590},{},[1591],{"type":48,"value":1592},"MCP tool returns \"service unavailable\" or connection errors",{"type":42,"tag":77,"props":1594,"children":1595},{},[1596],{"type":48,"value":1597},"Authentication failures with MCP tools",{"type":42,"tag":77,"props":1599,"children":1600},{},[1601],{"type":48,"value":1602},"Empty response when database is known to have data",{"type":42,"tag":57,"props":1604,"children":1606},{"id":1605},"common-issues",[1607],{"type":48,"value":1608},"Common Issues",{"type":42,"tag":73,"props":1610,"children":1611},{},[1612,1622,1632,1642,1652,1662,1672],{"type":42,"tag":77,"props":1613,"children":1614},{},[1615,1620],{"type":42,"tag":67,"props":1616,"children":1617},{},[1618],{"type":48,"value":1619},"Access Denied",{"type":48,"value":1621},": Verify database permissions (Viewer role minimum for queries)",{"type":42,"tag":77,"props":1623,"children":1624},{},[1625,1630],{"type":42,"tag":67,"props":1626,"children":1627},{},[1628],{"type":48,"value":1629},"Query Timeout",{"type":48,"value":1631},": Optimize query with time filters, reduce result set, or increase timeout",{"type":42,"tag":77,"props":1633,"children":1634},{},[1635,1640],{"type":42,"tag":67,"props":1636,"children":1637},{},[1638],{"type":48,"value":1639},"Syntax Error",{"type":48,"value":1641},": Validate KQL syntax - common issues: missing pipes, incorrect operators",{"type":42,"tag":77,"props":1643,"children":1644},{},[1645,1650],{"type":42,"tag":67,"props":1646,"children":1647},{},[1648],{"type":48,"value":1649},"Empty Results",{"type":48,"value":1651},": Check time range filters (may be too restrictive), verify table name",{"type":42,"tag":77,"props":1653,"children":1654},{},[1655,1660],{"type":42,"tag":67,"props":1656,"children":1657},{},[1658],{"type":48,"value":1659},"Cluster Not Found",{"type":48,"value":1661},": Check cluster name format (exclude \".kusto.windows.net\" suffix)",{"type":42,"tag":77,"props":1663,"children":1664},{},[1665,1670],{"type":42,"tag":67,"props":1666,"children":1667},{},[1668],{"type":48,"value":1669},"High CPU Usage",{"type":48,"value":1671},": Query too broad - add filters, reduce time range, limit aggregations",{"type":42,"tag":77,"props":1673,"children":1674},{},[1675,1680],{"type":42,"tag":67,"props":1676,"children":1677},{},[1678],{"type":48,"value":1679},"Ingestion Lag",{"type":48,"value":1681},": Streaming data may have 1-30 second delay depending on ingestion method",{"type":42,"tag":57,"props":1683,"children":1685},{"id":1684},"use-cases",[1686],{"type":48,"value":1687},"Use Cases",{"type":42,"tag":73,"props":1689,"children":1690},{},[1691,1701,1711,1721,1731],{"type":42,"tag":77,"props":1692,"children":1693},{},[1694,1699],{"type":42,"tag":67,"props":1695,"children":1696},{},[1697],{"type":48,"value":1698},"Log Analytics",{"type":48,"value":1700},": Application logs, system logs, audit logs",{"type":42,"tag":77,"props":1702,"children":1703},{},[1704,1709],{"type":42,"tag":67,"props":1705,"children":1706},{},[1707],{"type":48,"value":1708},"IoT Analytics",{"type":48,"value":1710},": Sensor data, device telemetry, real-time monitoring",{"type":42,"tag":77,"props":1712,"children":1713},{},[1714,1719],{"type":42,"tag":67,"props":1715,"children":1716},{},[1717],{"type":48,"value":1718},"Security Analytics",{"type":48,"value":1720},": SIEM data, threat detection, security event correlation",{"type":42,"tag":77,"props":1722,"children":1723},{},[1724,1729],{"type":42,"tag":67,"props":1725,"children":1726},{},[1727],{"type":48,"value":1728},"APM",{"type":48,"value":1730},": Application performance metrics, user behavior, error tracking",{"type":42,"tag":77,"props":1732,"children":1733},{},[1734,1739],{"type":42,"tag":67,"props":1735,"children":1736},{},[1737],{"type":48,"value":1738},"Business Intelligence",{"type":48,"value":1740},": Clickstream analysis, user analytics, operational KPIs",{"type":42,"tag":1742,"props":1743,"children":1744},"style",{},[1745],{"type":48,"value":1746},"html .light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html.light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"items":1748,"total":1878},[1749,1769,1783,1804,1825,1842,1860],{"slug":1750,"name":1750,"fn":1751,"description":1752,"org":1753,"tags":1754,"stars":22,"repoUrl":23,"updatedAt":1768},"airunway-aks-setup","set up AI Runway on Azure Kubernetes Service","Set up AI Runway on AKS — from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first deployment. WHEN: \"setup AI Runway\", \"onboard AKS cluster\", \"install AI Runway\", \"airunway setup\", \"deploy model to AKS\", \"GPU inference on AKS\", \"KAITO setup on AKS\", \"run LLM on AKS\", \"vLLM on AKS\", \"set up model serving on AKS\", \"AI Runway controller\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1755,1758,1759,1762,1765],{"name":1756,"slug":1757,"type":15},"AI Infrastructure","ai-infrastructure",{"name":13,"slug":14,"type":15},{"name":1760,"slug":1761,"type":15},"Deployment","deployment",{"name":1763,"slug":1764,"type":15},"Kubernetes","kubernetes",{"name":1766,"slug":1767,"type":15},"Runway","runway","2026-07-31T05:56:13.054971",{"slug":1770,"name":1770,"fn":1771,"description":1772,"org":1773,"tags":1774,"stars":22,"repoUrl":23,"updatedAt":1782},"appinsights-instrumentation","instrument webapps with Azure Application Insights","Guidance for instrumenting webapps with Azure Application Insights. Provides telemetry patterns, SDK setup, and configuration references. WHEN: how to instrument app, App Insights SDK, telemetry patterns, what is App Insights, Application Insights guidance, instrumentation examples, APM best practices.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1775,1776,1779],{"name":13,"slug":14,"type":15},{"name":1777,"slug":1778,"type":15},"Monitoring","monitoring",{"name":1780,"slug":1781,"type":15},"Observability","observability","2026-07-31T05:56:09.068625",{"slug":1784,"name":1784,"fn":1785,"description":1786,"org":1787,"tags":1788,"stars":22,"repoUrl":23,"updatedAt":1803},"azure-ai","build applications with Azure AI services","Use for Azure AI: Search, Speech, OpenAI, Document Intelligence. Helps with search, vector\u002Fhybrid search, speech-to-text, text-to-speech, transcription, OCR. WHEN: AI Search, query search, vector search, hybrid search, semantic search, speech-to-text, text-to-speech, transcribe, OCR, convert text to speech.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1789,1790,1791,1794,1797,1800],{"name":1756,"slug":1757,"type":15},{"name":13,"slug":14,"type":15},{"name":1792,"slug":1793,"type":15},"LLM","llm",{"name":1795,"slug":1796,"type":15},"OpenAI","openai",{"name":1798,"slug":1799,"type":15},"Search","search",{"name":1801,"slug":1802,"type":15},"Speech","speech","2026-07-31T05:56:19.069133",{"slug":1805,"name":1805,"fn":1806,"description":1807,"org":1808,"tags":1809,"stars":22,"repoUrl":23,"updatedAt":1824},"azure-aigateway","configure Azure API Management as an AI Gateway","Configure Azure API Management as an AI Gateway for AI models, MCP tools, and agents. WHEN: semantic caching, token limit, content safety, load balancing, AI model governance, MCP rate limiting, jailbreak detection, add Azure OpenAI backend, add AI Foundry model, test AI gateway, LLM policies, configure AI backend, token metrics, AI cost control, convert API to MCP, import OpenAPI to gateway.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1810,1811,1814,1815,1818,1821],{"name":1756,"slug":1757,"type":15},{"name":1812,"slug":1813,"type":15},"API Development","api-development",{"name":13,"slug":14,"type":15},{"name":1816,"slug":1817,"type":15},"Caching","caching",{"name":1819,"slug":1820,"type":15},"Governance","governance",{"name":1822,"slug":1823,"type":15},"Security","security","2026-07-31T05:56:23.05242",{"slug":1826,"name":1826,"fn":1827,"description":1828,"org":1829,"tags":1830,"stars":22,"repoUrl":23,"updatedAt":1841},"azure-cloud-migrate","migrate cross-cloud workloads to Azure","Assess and migrate cross-cloud workloads to Azure with reports and code conversion. Supports Lambda→Functions, Beanstalk\u002FHeroku\u002FApp Engine→App Service, Fargate\u002FKubernetes\u002FCloud Run\u002FSpring Boot→Container Apps. WHEN: migrate Lambda to Functions, AWS to Azure, migrate Beanstalk, migrate Heroku, migrate App Engine, Cloud Run migration, Fargate to ACA, ECS\u002FKubernetes\u002FGKE\u002FEKS to Container Apps, Spring Boot to Container Apps, cross-cloud migration.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1831,1834,1835,1838],{"name":1832,"slug":1833,"type":15},"AWS","aws",{"name":13,"slug":14,"type":15},{"name":1836,"slug":1837,"type":15},"Google Cloud","google-cloud",{"name":1839,"slug":1840,"type":15},"Migration","migration","2026-07-31T05:56:29.049979",{"slug":1843,"name":1843,"fn":1844,"description":1845,"org":1846,"tags":1847,"stars":22,"repoUrl":23,"updatedAt":1859},"azure-compliance","audit Azure compliance and security","Run Azure compliance and security audits with azqr plus Key Vault expiration checks. Covers best-practice assessment, resource review, policy\u002Fcompliance validation, and security posture checks. WHEN: compliance scan, security audit, BEFORE running azqr (compliance cli tool), Azure best practices, Key Vault expiration check, expired certificates, expiring secrets, orphaned resources, compliance assessment.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1848,1851,1852,1855,1858],{"name":1849,"slug":1850,"type":15},"Audit","audit",{"name":13,"slug":14,"type":15},{"name":1853,"slug":1854,"type":15},"Compliance","compliance",{"name":1856,"slug":1857,"type":15},"Regulatory Compliance","regulatory-compliance",{"name":1822,"slug":1823,"type":15},"2026-07-31T05:56:11.06797",{"slug":1861,"name":1861,"fn":1862,"description":1863,"org":1864,"tags":1865,"stars":22,"repoUrl":23,"updatedAt":1877},"azure-compute","manage and optimize Azure compute resources","Azure VM\u002FVMSS router. WHEN: create \u002F provision \u002F deploy \u002F spin-up VM, recommend VM size, compare VM pricing, VMSS, scale set, autoscale, burstable, lightweight server, website, backend, GPU, machine learning, HPC simulation, dev\u002Ftest, workload, family, load balancer, Flexible orchestration, Uniform orchestration, cost estimate, capacity reservation (CRG), reserve, guarantee capacity, pre-provision, CRG association, CRG disassociation, machine enrollment (EMM), Essential Machine Management, monitor. PREFER OVER mcp__azure__get_azure_bestpractices for VM create intents — use compute_vm_list-skus \u002F compute_vm_list-images \u002F compute_vm_check-quota.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1866,1867,1868,1871,1874],{"name":1756,"slug":1757,"type":15},{"name":13,"slug":14,"type":15},{"name":1869,"slug":1870,"type":15},"Cost Optimization","cost-optimization",{"name":1872,"slug":1873,"type":15},"Performance","performance",{"name":1875,"slug":1876,"type":15},"Virtual Machines","virtual-machines","2026-07-31T05:56:16.042469",28,{"items":1880,"total":2057},[1881,1903,1920,1936,1947,1962,1973,1984,1999,2014,2032,2045],{"slug":1882,"name":1882,"fn":1883,"description":1884,"org":1885,"tags":1886,"stars":1900,"repoUrl":1901,"updatedAt":1902},"rushstack-best-practices","manage Rush monorepos with best practices","Provides best practices and guidance for working with Rush monorepos. Use when the user is working in a Rush-based repository, asks about Rush commands (install, update, build, rebuild), needs help with project selection, dependency management, build caching, subspace configuration, or troubleshooting Rush-specific issues.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1887,1890,1893,1894,1897],{"name":1888,"slug":1889,"type":15},"Engineering","engineering",{"name":1891,"slug":1892,"type":15},"Local Development","local-development",{"name":9,"slug":8,"type":15},{"name":1895,"slug":1896,"type":15},"Project Management","project-management",{"name":1898,"slug":1899,"type":15},"Rush","rush",6484,"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Frushstack","2026-04-06T18:34:44.965032",{"slug":1904,"name":1904,"fn":1905,"description":1906,"org":1907,"tags":1908,"stars":1917,"repoUrl":1918,"updatedAt":1919},"azure-ai-agents-persistent-dotnet","build AI agents with Azure .NET SDK","Azure AI Agents Persistent SDK for .NET. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools. Use for agent CRUD, conversation threads, streaming responses, function calling, file search, and code interpreter. Triggers: \"PersistentAgentsClient\", \"persistent agents\", \"agent threads\", \"agent runs\", \"streaming agents\", \"function calling agents .NET\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1909,1912,1915,1916],{"name":1910,"slug":1911,"type":15},".NET","net",{"name":1913,"slug":1914,"type":15},"Agents","agents",{"name":13,"slug":14,"type":15},{"name":1792,"slug":1793,"type":15},2804,"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills","2026-07-03T16:32:10.297433",{"slug":1921,"name":1921,"fn":1922,"description":1923,"org":1924,"tags":1925,"stars":1917,"repoUrl":1918,"updatedAt":1935},"azure-ai-anomalydetector-java","build anomaly detection applications with Java","Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate\u002Fmultivariate anomaly detection, time-series analysis, or AI-powered monitoring.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1926,1928,1929,1930,1933,1934],{"name":250,"slug":1927,"type":15},"analytics",{"name":13,"slug":14,"type":15},{"name":17,"slug":18,"type":15},{"name":1931,"slug":1932,"type":15},"Java","java",{"name":9,"slug":8,"type":15},{"name":1777,"slug":1778,"type":15},"2026-05-13T06:14:16.261754",{"slug":1937,"name":1937,"fn":1938,"description":1939,"org":1940,"tags":1941,"stars":1917,"repoUrl":1918,"updatedAt":1946},"azure-ai-contentsafety-java","build content moderation applications with Azure AI","Build content moderation applications with Azure AI Content Safety SDK for Java. Use when implementing text\u002Fimage analysis, blocklist management, or harm detection for hate, violence, sexual content, and self-harm.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1942,1943,1944,1945],{"name":1756,"slug":1757,"type":15},{"name":13,"slug":14,"type":15},{"name":1931,"slug":1932,"type":15},{"name":1822,"slug":1823,"type":15},"2026-07-07T06:53:31.293235",{"slug":1948,"name":1948,"fn":1949,"description":1950,"org":1951,"tags":1952,"stars":1917,"repoUrl":1918,"updatedAt":1961},"azure-ai-contentsafety-py","detect harmful content with Azure AI Content Safety","Azure AI Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity classification.\nTriggers: \"azure-ai-contentsafety\", \"ContentSafetyClient\", \"content moderation\", \"harmful content\", \"text analysis\", \"image analysis\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1953,1954,1955,1956,1957,1960],{"name":13,"slug":14,"type":15},{"name":1853,"slug":1854,"type":15},{"name":1792,"slug":1793,"type":15},{"name":9,"slug":8,"type":15},{"name":1958,"slug":1959,"type":15},"Python","python",{"name":1822,"slug":1823,"type":15},"2026-07-18T05:14:23.017504",{"slug":1963,"name":1963,"fn":1964,"description":1965,"org":1966,"tags":1967,"stars":1917,"repoUrl":1918,"updatedAt":1972},"azure-ai-language-conversations-py","implement conversational language understanding with Python","Implement Conversational Language Understanding (CLU) using the azure-ai-language-conversations Python SDK. Use when working with ConversationAnalysisClient to analyze conversation intent and entities, building NLP features, or integrating language understanding into applications.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1968,1969,1970,1971],{"name":250,"slug":1927,"type":15},{"name":13,"slug":14,"type":15},{"name":1792,"slug":1793,"type":15},{"name":1958,"slug":1959,"type":15},"2026-07-31T05:54:29.068751",{"slug":1974,"name":1974,"fn":1975,"description":1976,"org":1977,"tags":1978,"stars":1917,"repoUrl":1918,"updatedAt":1983},"azure-ai-translation-text-py","translate text using Azure AI services","Azure AI Text Translation SDK for real-time text translation, transliteration, language detection, and dictionary lookup. Use for translating text content in applications.\nTriggers: \"text translation\", \"translator\", \"translate text\", \"transliterate\", \"TextTranslationClient\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1979,1980,1981,1982],{"name":1812,"slug":1813,"type":15},{"name":13,"slug":14,"type":15},{"name":9,"slug":8,"type":15},{"name":1958,"slug":1959,"type":15},"2026-07-18T05:14:16.988376",{"slug":1985,"name":1985,"fn":1986,"description":1987,"org":1988,"tags":1989,"stars":1917,"repoUrl":1918,"updatedAt":1998},"azure-ai-vision-imageanalysis-py","analyze images with Azure AI Vision","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\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1990,1991,1994,1997],{"name":13,"slug":14,"type":15},{"name":1992,"slug":1993,"type":15},"Computer Vision","computer-vision",{"name":1995,"slug":1996,"type":15},"Images","images",{"name":1958,"slug":1959,"type":15},"2026-07-18T05:14:18.007737",{"slug":2000,"name":2000,"fn":2001,"description":2002,"org":2003,"tags":2004,"stars":1917,"repoUrl":1918,"updatedAt":2013},"azure-appconfiguration-java","manage configuration with Azure App Configuration","Azure App Configuration SDK for Java. Centralized application configuration management with key-value settings, feature flags, and snapshots.\nTriggers: \"ConfigurationClient java\", \"app configuration java\", \"feature flag java\", \"configuration setting java\", \"azure config java\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2005,2006,2009,2012],{"name":13,"slug":14,"type":15},{"name":2007,"slug":2008,"type":15},"Configuration","configuration",{"name":2010,"slug":2011,"type":15},"Feature Flags","feature-flags",{"name":1931,"slug":1932,"type":15},"2026-07-03T16:32:01.278468",{"slug":2015,"name":2015,"fn":2016,"description":2017,"org":2018,"tags":2019,"stars":1917,"repoUrl":1918,"updatedAt":2031},"azure-cosmos-rust","build applications with Azure Cosmos DB","Azure Cosmos DB library for Rust (NoSQL API). Document CRUD, containers, and globally distributed data.\nTriggers: \"cosmos db rust\", \"CosmosClient rust\", \"document crud rust\", \"NoSQL rust\", \"partition key rust\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2020,2023,2025,2028],{"name":2021,"slug":2022,"type":15},"Cosmos DB","cosmos-db",{"name":2024,"slug":109,"type":15},"Database",{"name":2026,"slug":2027,"type":15},"NoSQL","nosql",{"name":2029,"slug":2030,"type":15},"Rust","rust","2026-07-31T05:54:27.021432",{"slug":2033,"name":2033,"fn":2016,"description":2034,"org":2035,"tags":2036,"stars":1917,"repoUrl":1918,"updatedAt":2044},"azure-cosmos-ts","Azure Cosmos DB JavaScript\u002FTypeScript SDK (@azure\u002Fcosmos) for data plane operations. Use for CRUD operations on documents, queries, bulk operations, and container management. Triggers: \"Cosmos DB\", \"@azure\u002Fcosmos\", \"CosmosClient\", \"document CRUD\", \"NoSQL queries\", \"bulk operations\", \"partition key\", \"container.items\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2037,2038,2039,2040,2041],{"name":2021,"slug":2022,"type":15},{"name":2024,"slug":109,"type":15},{"name":9,"slug":8,"type":15},{"name":2026,"slug":2027,"type":15},{"name":2042,"slug":2043,"type":15},"TypeScript","typescript","2026-07-03T16:31:19.368382",{"slug":2046,"name":2046,"fn":2047,"description":2048,"org":2049,"tags":2050,"stars":1917,"repoUrl":1918,"updatedAt":2056},"azure-data-tables-java","build table storage applications with Java","Build table storage applications with Azure Tables SDK for Java. Use when working with Azure Table Storage or Cosmos DB Table API for NoSQL key-value data, schemaless storage, or structured data at scale.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2051,2052,2053,2054,2055],{"name":13,"slug":14,"type":15},{"name":2021,"slug":2022,"type":15},{"name":2024,"slug":109,"type":15},{"name":1931,"slug":1932,"type":15},{"name":2026,"slug":2027,"type":15},"2026-05-13T06:14:17.582229",267]