[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71NXVPRF532YRHQ8890A0FS":3,"skill-same:01M71NXVPRF532YRHQ8890A0FS":83},{"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":31,"publishedAt":52,"linkOnly":53,"markdownContent":54,"markdownBody":55,"frontMatter":56,"refName":60,"commitSha":61,"skillDirectory":62,"forksCount":63,"repositoryHtmlUrl":64,"repositoryHomepageUrl":6,"ownerHtmlUrl":65,"ownerBlogUrl":66,"ownerDescription":67,"updatedAt":68,"install":69,"resources":82},"01M71NXVPRF532YRHQ8890A0FS","azure-kusto",null,"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.","iDorgham\u002FWorkspace-Factory","iDorgham","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F136521493?v=4","01M6CNGBDAHE4ARW3YJEF4X2FD","Dorgham","01M71E8H3CXZ7DJX59MTM9JEB2",1,"MIT","https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fofficial_microsoft_azure_kusto\u002Fskill.md",[18,23,26],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":27,"categoryName":28,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51],"azure","azure kusto","series","telemetry","for","anomaly","kql","Clusters","time series","analytics","data","time series analysis","analyze","log","analysis","query","when","database","database queries","time","2026-11-10 10:56:23",false,"---\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: \"1.0.1\"\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 KPIs","# 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 KPIs",{"name":5,"license":15,"metadata":57,"description":7},{"author":58,"version":59},"Microsoft","1.0.1","master","c2e410e11df4d80d885e0ecf0583ef3c926df736","factory\u002Flibrary\u002Fskills\u002Fofficial_microsoft_azure_kusto",0,"https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory","https:\u002F\u002Fgithub.com\u002FiDorgham","Dorgham.me","hey I'm Dorgham \r\nI code, I create, and AI codes back. \r\nMaybe we can give AI the chance!! now we begin. 🚀🤖\r\n... . . . git commit -m \"Hello, Future\" ","2026-05-04 18:39:09",{"source":8,"skillName":5,"commands":70,"prompt":80,"zipAvailable":81},[71,74,77],{"tool":72,"command":73},"npx","npx skills add iDorgham\u002FWorkspace-Factory --skill azure-kusto",{"tool":75,"command":76},"bunx","bunx skills add iDorgham\u002FWorkspace-Factory --skill azure-kusto",{"tool":78,"command":79},"pnpm","pnpm dlx skills add iDorgham\u002FWorkspace-Factory --skill azure-kusto","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fofficial_microsoft_azure_kusto\u002Fskill.md",true,[],[84,114,136,157,177,199],{"skillNo":85,"name":86,"nameZh":6,"description":87,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":88,"categories":89,"keywords":93,"publishedAt":113},"01M71P8WVBMB2X3D24R0YT0B0E","vercel-react-view-transitions","Guide for implementing smooth, native-feeling animations using React's View Transition API (`\u003CViewTransition>` component, `addTransitionType`, and CSS view transition pseudo-elements). Use this skill whenever the user wants to add page transitions, animate route changes, create shared element animations, animate enter\u002Fexit of components, animate list reorder, implement directional (forward\u002Fback) navigation animations, or integrate view transitions in Next.js. Also use when the user mentions view transitions, `startViewTransition`, `ViewTransition`, transition types, or asks about animating between UI states in React without third-party animation libraries.","https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fofficial_vercel_labs_react_view_transitions\u002Fskill.md",[90],{"categoryNo":91,"categoryName":92,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[94,95,96,97,98,99,100,101,102,103,104,105,106,107,48,108,109,110,111,112],"react","vercel react view transitions","view","react view transitions","vercel","transitions","shared","back","Animation","css","types","states","element","implementing","components","api","route","create","libraries","2026-11-10 13:02:02",{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":118,"categories":119,"keywords":121,"publishedAt":113},"01M71P8TDGHZ289TSAC5VTG2EW","vercel-react-native-skills","React Native and Expo best practices for building performant mobile apps. Use when building React Native components, optimizing list performance, implementing animations, or working with native modules. Triggers on tasks involving React Native, Expo, mobile performance, or native platform APIs.","https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fofficial_vercel_labs_react_native_skills\u002Fskill.md",[120],{"categoryNo":91,"categoryName":92,"taxonomyType":21,"taxonomyTypeDesc":22},[94,122,123,98,124,125,126,127,107,128,48,129,108,130,131,132,133,134,135,36],"react native","native","vercel react native skills","react native skills","modules","performance","apps","tasks","mobile","expo","list","mobile performance","platform","animations",{"skillNo":137,"name":138,"nameZh":6,"description":139,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":140,"categories":141,"keywords":143,"publishedAt":113},"01M71P8QK4JF5BYRGYDDGPZQG9","vercel-react-best-practices","React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React\u002FNext.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.","https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fofficial_vercel_labs_react_best_practices\u002Fskill.md",[142],{"categoryNo":91,"categoryName":92,"taxonomyType":21,"taxonomyTypeDesc":22},[94,144,145,98,146,127,147,48,148,129,108,149,150,151,152,42,153,154,155,156],"react best practices","vercel react best practices","best practices","writing","code","data fetching","react components","next","refactoring","patterns","Optimization","guidelines","triggers",{"skillNo":158,"name":159,"nameZh":6,"description":160,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":161,"categories":162,"keywords":164,"publishedAt":113},"01M71P8HVEA70K5N6ZVJCG2ED3","vercel-composition-patterns","React composition patterns that scale. Use when refactoring components with boolean prop proliferation, building flexible component libraries, or designing reusable APIs. Triggers on tasks involving compound components, render props, context providers, or component architecture. Includes React 19 API changes.","https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fofficial_vercel_labs_composition_patterns\u002Fskill.md",[163],{"categoryNo":91,"categoryName":92,"taxonomyType":21,"taxonomyTypeDesc":22},[165,98,166,153,167,168,169,170,156,171,172,173,48,174,129,108,175,176,109,94],"Composition","composition patterns","vercel composition patterns","react composition patterns","react 19","render","boolean","component architecture","use","reusable","architecture","react composition",{"skillNo":178,"name":179,"nameZh":6,"description":180,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":181,"categories":182,"keywords":186,"publishedAt":113},"01M71P60XXKKP222ZFJRG7R373","supabase-postgres-best-practices","Postgres performance optimization and best practices from Supabase. Use this skill when writing, reviewing, or optimizing Postgres queries, schema designs, or database configurations.","https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fofficial_supabase_supabase_postgres_best_practices\u002Fskill.md",[183],{"categoryNo":184,"categoryName":185,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",[187,188,189,190,146,191,192,193,36,154,127,194,195,147,196,173,197,198,49,48],"postgres best practices","supabase","postgres","supabase postgres best practices","categories","postgres performance","advanced","correct","schema","impact","Rules","critical",{"skillNo":200,"name":201,"nameZh":6,"description":202,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":203,"categories":204,"keywords":206,"publishedAt":52},"01M71P2ZCEJD0PKC458YTP1FJW","portable-text-serialization","Render and serialize Portable Text to React, Svelte, Vue, Astro, HTML, Markdown, and plain text. Use when implementing Portable Text rendering in any frontend framework, building custom serializers for non-standard block types, converting Portable Text to HTML strings server-side, converting Portable Text to Markdown, extracting plain text from Portable Text, or troubleshooting rendering issues with marks, blocks, lists, or custom types.","https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fofficial_sanity_io_portable_text_serialization\u002Fskill.md",[205],{"categoryNo":91,"categoryName":92,"taxonomyType":21,"taxonomyTypeDesc":22},[207,208,209,210,211,104,212,170,213,107,214,215,48,94,216,217,218,219,220,221],"text","portable text serialization","portable","serialization","extracting","strings","issues","frontend","svelte","sanity","astro","server","markdown","rendering","vue"]