[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6JHJYPSCDYVQFCWMP8VCN8X":3,"skill-same:01M6JHJYPSCDYVQFCWMP8VCN8X":106},{"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":28,"publishedAt":49,"linkOnly":50,"markdownContent":51,"markdownBody":52,"frontMatter":53,"refName":54,"commitSha":55,"skillDirectory":56,"forksCount":57,"repositoryHtmlUrl":58,"repositoryHomepageUrl":6,"ownerHtmlUrl":59,"ownerBlogUrl":6,"ownerDescription":60,"updatedAt":61,"install":62,"resources":75},"01M6JHJYPSCDYVQFCWMP8VCN8X","google-analytics",null,"Analyze Google Analytics data, review website performance metrics, identify traffic patterns, and suggest data-driven improvements. Use when the user asks about analytics, website metrics, traffic analysis, conversion rates, user behavior, or performance optimization.","transaurus\u002Fstaging-davila7-claude-code-templates","transaurus","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F210882017?v=4","01M6JH1R6ZCRXGJCTDP5ZKZBE6","Page Turner Agent","01M6JH1RBBR2P6Y0XWDGM4B0G7",0,"MIT","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fanalytics\u002Fgoogle-analytics\u002FSKILL.md",[18,23],{"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":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48],"analytics","google analytics","google","analyze","data-driven","traffic","about","traffic analysis","performance metrics","patterns","conversion","use","behavior","performance optimization","analysis","data","Optimization","website","performance","review","2026-11-10 10:12:30",false,"---\nname: google-analytics\ndescription: Analyze Google Analytics data, review website performance metrics, identify traffic patterns, and suggest data-driven improvements. Use when the user asks about analytics, website metrics, traffic analysis, conversion rates, user behavior, or performance optimization.\n---\n\n# Google Analytics Analysis\n\nAnalyze website performance using Google Analytics data to provide actionable insights and improvement recommendations.\n\n## Quick Start\n\n### 1. Setup Authentication\n\nThis Skill requires Google Analytics API credentials. Set up environment variables:\n\n```bash\nexport GOOGLE_ANALYTICS_PROPERTY_ID=\"your-property-id\"\nexport GOOGLE_APPLICATION_CREDENTIALS=\"\u002Fpath\u002Fto\u002Fservice-account-key.json\"\n```\n\nOr create a `.env` file in your project root:\n\n```env\nGOOGLE_ANALYTICS_PROPERTY_ID=123456789\nGOOGLE_APPLICATION_CREDENTIALS=\u002Fpath\u002Fto\u002Fservice-account-key.json\n```\n\n**Never commit credentials to version control.** The service account JSON file should be stored securely outside your repository.\n\n### 2. Install Required Packages\n\n```bash\n# Option 1: Install from requirements file (recommended)\npip install -r cli-tool\u002Fcomponents\u002Fskills\u002Fanalytics\u002Fgoogle-analytics\u002Frequirements.txt\n\n# Option 2: Install individually\npip install google-analytics-data python-dotenv pandas\n```\n\n### 3. Analyze Your Project\n\nOnce configured, I can:\n- Review current traffic and user behavior metrics\n- Identify top-performing and underperforming pages\n- Analyze traffic sources and conversion funnels\n- Compare performance across time periods\n- Suggest data-driven improvements\n\n## How to Use\n\nAsk me questions like:\n- \"Review our Google Analytics performance for the last 30 days\"\n- \"What are our top traffic sources?\"\n- \"Which pages have the highest bounce rates?\"\n- \"Analyze user engagement and suggest improvements\"\n- \"Compare this month's performance to last month\"\n\n## Analysis Workflow\n\nWhen you ask me to analyze Google Analytics data, I will:\n\n1. **Connect to the API** using the helper script\n2. **Fetch relevant metrics** based on your question\n3. **Analyze the data** looking for:\n   - Traffic trends and patterns\n   - User behavior insights\n   - Performance bottlenecks\n   - Conversion opportunities\n4. **Provide recommendations** with:\n   - Specific improvement suggestions\n   - Priority level (high\u002Fmedium\u002Flow)\n   - Expected impact\n   - Implementation guidance\n\n## Common Metrics\n\nFor detailed metric definitions and dimensions, see [REFERENCE.md](REFERENCE.md).\n\n### Traffic Metrics\n- Sessions, Users, New Users\n- Page views, Screens per Session\n- Average Session Duration\n\n### Engagement Metrics\n- Bounce Rate, Engagement Rate\n- Event Count, Conversions\n- Scroll Depth, Click-through Rate\n\n### Acquisition Metrics\n- Traffic Source\u002FMedium\n- Campaign Performance\n- Channel Grouping\n\n### Conversion Metrics\n- Goal Completions\n- E-commerce Transactions\n- Conversion Rate by Source\n\n## Analysis Examples\n\nFor complete analysis patterns and use cases, see [EXAMPLES.md](EXAMPLES.md).\n\n## Scripts\n\nThe Skill includes utility scripts for API interaction:\n\n### Fetch Current Performance\n```bash\npython scripts\u002Fga_client.py --days 30 --metrics sessions,users,bounceRate\n```\n\n### Analyze and Generate Report\n```bash\npython scripts\u002Fanalyze.py --period last-30-days --compare previous-period\n```\n\nThe scripts handle API authentication, data fetching, and basic analysis. I'll interpret the results and provide actionable recommendations.\n\n## Troubleshooting\n\n**Authentication Error**: Verify that:\n- `GOOGLE_APPLICATION_CREDENTIALS` points to a valid service account JSON file\n- The service account has \"Viewer\" access to your GA4 property\n- `GOOGLE_ANALYTICS_PROPERTY_ID` matches your GA4 property ID (not the measurement ID)\n\n**No Data Returned**: Check that:\n- The property ID is correct (find it in GA4 Admin > Property Settings)\n- The date range contains data\n- The service account has been granted access in GA4\n\n**Import Errors**: Install required packages:\n```bash\npip install google-analytics-data python-dotenv pandas\n```\n\n## Security Notes\n\n- **Never hardcode** API credentials or property IDs in code\n- Store service account JSON files **outside** version control\n- Use environment variables or `.env` files for configuration\n- Add `.env` and credential files to `.gitignore`\n- Rotate service account keys periodically\n- Use least-privilege access (Viewer role only)\n\n## Data Privacy\n\nThis Skill accesses aggregated analytics data only. It does not:\n- Access personally identifiable information (PII)\n- Store analytics data persistently\n- Share data with external services\n- Modify your Google Analytics configuration\n\nAll data is processed locally and used only to generate recommendations during the conversation.\n","# Google Analytics Analysis\n\nAnalyze website performance using Google Analytics data to provide actionable insights and improvement recommendations.\n\n## Quick Start\n\n### 1. Setup Authentication\n\nThis Skill requires Google Analytics API credentials. Set up environment variables:\n\n```bash\nexport GOOGLE_ANALYTICS_PROPERTY_ID=\"your-property-id\"\nexport GOOGLE_APPLICATION_CREDENTIALS=\"\u002Fpath\u002Fto\u002Fservice-account-key.json\"\n```\n\nOr create a `.env` file in your project root:\n\n```env\nGOOGLE_ANALYTICS_PROPERTY_ID=123456789\nGOOGLE_APPLICATION_CREDENTIALS=\u002Fpath\u002Fto\u002Fservice-account-key.json\n```\n\n**Never commit credentials to version control.** The service account JSON file should be stored securely outside your repository.\n\n### 2. Install Required Packages\n\n```bash\n# Option 1: Install from requirements file (recommended)\npip install -r cli-tool\u002Fcomponents\u002Fskills\u002Fanalytics\u002Fgoogle-analytics\u002Frequirements.txt\n\n# Option 2: Install individually\npip install google-analytics-data python-dotenv pandas\n```\n\n### 3. Analyze Your Project\n\nOnce configured, I can:\n- Review current traffic and user behavior metrics\n- Identify top-performing and underperforming pages\n- Analyze traffic sources and conversion funnels\n- Compare performance across time periods\n- Suggest data-driven improvements\n\n## How to Use\n\nAsk me questions like:\n- \"Review our Google Analytics performance for the last 30 days\"\n- \"What are our top traffic sources?\"\n- \"Which pages have the highest bounce rates?\"\n- \"Analyze user engagement and suggest improvements\"\n- \"Compare this month's performance to last month\"\n\n## Analysis Workflow\n\nWhen you ask me to analyze Google Analytics data, I will:\n\n1. **Connect to the API** using the helper script\n2. **Fetch relevant metrics** based on your question\n3. **Analyze the data** looking for:\n   - Traffic trends and patterns\n   - User behavior insights\n   - Performance bottlenecks\n   - Conversion opportunities\n4. **Provide recommendations** with:\n   - Specific improvement suggestions\n   - Priority level (high\u002Fmedium\u002Flow)\n   - Expected impact\n   - Implementation guidance\n\n## Common Metrics\n\nFor detailed metric definitions and dimensions, see [REFERENCE.md](REFERENCE.md).\n\n### Traffic Metrics\n- Sessions, Users, New Users\n- Page views, Screens per Session\n- Average Session Duration\n\n### Engagement Metrics\n- Bounce Rate, Engagement Rate\n- Event Count, Conversions\n- Scroll Depth, Click-through Rate\n\n### Acquisition Metrics\n- Traffic Source\u002FMedium\n- Campaign Performance\n- Channel Grouping\n\n### Conversion Metrics\n- Goal Completions\n- E-commerce Transactions\n- Conversion Rate by Source\n\n## Analysis Examples\n\nFor complete analysis patterns and use cases, see [EXAMPLES.md](EXAMPLES.md).\n\n## Scripts\n\nThe Skill includes utility scripts for API interaction:\n\n### Fetch Current Performance\n```bash\npython scripts\u002Fga_client.py --days 30 --metrics sessions,users,bounceRate\n```\n\n### Analyze and Generate Report\n```bash\npython scripts\u002Fanalyze.py --period last-30-days --compare previous-period\n```\n\nThe scripts handle API authentication, data fetching, and basic analysis. I'll interpret the results and provide actionable recommendations.\n\n## Troubleshooting\n\n**Authentication Error**: Verify that:\n- `GOOGLE_APPLICATION_CREDENTIALS` points to a valid service account JSON file\n- The service account has \"Viewer\" access to your GA4 property\n- `GOOGLE_ANALYTICS_PROPERTY_ID` matches your GA4 property ID (not the measurement ID)\n\n**No Data Returned**: Check that:\n- The property ID is correct (find it in GA4 Admin > Property Settings)\n- The date range contains data\n- The service account has been granted access in GA4\n\n**Import Errors**: Install required packages:\n```bash\npip install google-analytics-data python-dotenv pandas\n```\n\n## Security Notes\n\n- **Never hardcode** API credentials or property IDs in code\n- Store service account JSON files **outside** version control\n- Use environment variables or `.env` files for configuration\n- Add `.env` and credential files to `.gitignore`\n- Rotate service account keys periodically\n- Use least-privilege access (Viewer role only)\n\n## Data Privacy\n\nThis Skill accesses aggregated analytics data only. It does not:\n- Access personally identifiable information (PII)\n- Store analytics data persistently\n- Share data with external services\n- Modify your Google Analytics configuration\n\nAll data is processed locally and used only to generate recommendations during the conversation.\n",{"name":5,"description":7},"main","34fa6acf794b9a6310d298cbb8fb8c9420a5edcf","cli-tool\u002Fcomponents\u002Fskills\u002Fanalytics\u002Fgoogle-analytics",2,"https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates","https:\u002F\u002Fgithub.com\u002Ftransaurus","Automated works of Page Turner","2026-03-28 10:12:59",{"source":8,"skillName":5,"commands":63,"prompt":73,"zipAvailable":74},[64,67,70],{"tool":65,"command":66},"npx","npx skills add transaurus\u002Fstaging-davila7-claude-code-templates --skill google-analytics",{"tool":68,"command":69},"bunx","bunx skills add transaurus\u002Fstaging-davila7-claude-code-templates --skill google-analytics",{"tool":71,"command":72},"pnpm","pnpm dlx skills add transaurus\u002Fstaging-davila7-claude-code-templates --skill google-analytics","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fanalytics\u002Fgoogle-analytics\u002FSKILL.md",true,[76,82,88,92,96,102],{"path":77,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":80,"sourceHtmlUrl":81},".env.example","OTHER","其他",1153,"https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fanalytics\u002Fgoogle-analytics\u002F.env.example",{"path":83,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":86,"sourceHtmlUrl":87},"EXAMPLES.md","REFERENCE","参考文档",13867,"https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fanalytics\u002Fgoogle-analytics\u002FEXAMPLES.md",{"path":89,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":90,"sourceHtmlUrl":91},"REFERENCE.md",8853,"https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fanalytics\u002Fgoogle-analytics\u002FREFERENCE.md",{"path":93,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":94,"sourceHtmlUrl":95},"requirements.txt",152,"https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fanalytics\u002Fgoogle-analytics\u002Frequirements.txt",{"path":97,"resourceType":98,"resourceTypeDesc":99,"sizeBytes":100,"sourceHtmlUrl":101},"scripts\u002Fanalyze.py","SCRIPT","脚本",15777,"https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fanalytics\u002Fgoogle-analytics\u002Fscripts\u002Fanalyze.py",{"path":103,"resourceType":98,"resourceTypeDesc":99,"sizeBytes":104,"sourceHtmlUrl":105},"scripts\u002Fga_client.py",11325,"https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fanalytics\u002Fgoogle-analytics\u002Fscripts\u002Fga_client.py",[107,135,156,180,200,226],{"skillNo":108,"name":109,"nameZh":6,"description":110,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":111,"categories":112,"keywords":116,"publishedAt":49},"01M6JMRJR3BE0HGWFWDZSB853S","zapier-make-patterns","No-code automation democratizes workflow building. Zapier and Make (formerly Integromat) let non-developers automate business processes without writing code. But no-code doesn't mean no-complexity - these platforms have their own patterns, pitfalls, and breaking points.  This skill covers when to use which platform, how to build reliable automations, and when to graduate to code-based solutions. Key insight: Zapier optimizes for simplicity and integrations (7000+ apps), Make optimizes for power","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fzapier-make-patterns\u002FSKILL.md",[113],{"categoryNo":114,"categoryName":115,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[117,38,118,119,120,121,122,123,124,125,40,126,127,128,129,130,131,132,133,134],"zapier make patterns","make","zapier","writing","workflow","automation","code","integrations","business","power","complexity","platform","apps","how to","No-Code","which","for","spawner",{"skillNo":136,"name":137,"nameZh":6,"description":138,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":139,"categories":140,"keywords":141,"publishedAt":49},"01M6JMR8YK6SWPCTG1MCQTMN9S","yeet","Use only when the user explicitly asks to stage, commit, push, and open a GitHub pull request in one flow using the GitHub CLI (`gh`).","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fyeet\u002FSKILL.md",[],[137,142,143,144,145,146,147,148,149,150,151,152,40,153,154,155],"cli","github","commit","pull request","openai","pull","push","flow","open","gh","user","only","stage","github cli",{"skillNo":157,"name":158,"nameZh":6,"description":159,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":160,"categories":161,"keywords":163,"publishedAt":49},"01M6JMQNFE64ZASQK90HARRXW8","workflow-automation","Workflow automation is the infrastructure that makes AI agents reliable. Without durable execution, a network hiccup during a 10-step payment flow means lost money and angry customers. With it, workflows resume exactly where they left off.  This skill covers the platforms (n8n, Temporal, Inngest) and patterns (sequential, parallel, orchestrator-worker) that turn brittle scripts into production-grade automation.  Key insight: The platforms make different tradeoffs. n8n optimizes for accessibility","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fworkflow-automation\u002FSKILL.md",[162],{"categoryNo":114,"categoryName":115,"taxonomyType":21,"taxonomyTypeDesc":22},[121,122,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,133,134],"Workflow Automation","ai agents","accessibility","workflows","apache","execution","inngest","parallel","orchestrator","resume","n8n","payment","worker","scripts","temporal","with",{"skillNo":181,"name":182,"nameZh":6,"description":183,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":184,"categories":185,"keywords":186,"publishedAt":49},"01M6JMQDYJ0XETERPHGDR3EVGN","trigger-dev","Trigger.dev expert for background jobs, AI workflows, and reliable async execution with excellent developer experience and TypeScript-first design. Use when: trigger.dev, trigger dev, background task, ai background job, long running task.","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Ftrigger-dev\u002FSKILL.md",[],[187,188,189,190,191,167,192,168,169,193,40,194,195,196,197,198,199,179,133,134],"dev","trigger dev","trigger","design","typescript","async execution","first","async","developer experience","expert","task","background jobs","long",{"skillNo":201,"name":202,"nameZh":6,"description":203,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":204,"categories":205,"keywords":209,"publishedAt":49},"01M6JMQ5J6KK3GSX9FJCWWK6G6","planning-with-files","Transforms workflow to use Manus-style persistent markdown files for planning, progress tracking, and knowledge storage. Use when starting complex tasks, multi-step projects, research tasks, or when the user mentions planning, organizing work, tracking progress, or wants structured output.","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fplanning-with-files\u002FSKILL.md",[206],{"categoryNo":207,"categoryName":208,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",[210,211,212,179,213,121,214,215,216,217,218,219,40,220,221,222,223,224,225,133],"planning with files","planning","files","with files","storage","tasks","markdown","knowledge","projects","work","manus","structured output","progress","transforms","multi","output",{"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":236,"publishedAt":49},"01M6JMPWAT60RXXNEC4V9TRBRS","n8n-workflow-patterns","Proven workflow architectural patterns from real n8n workflows. Use when building new workflows, designing workflow structure, choosing workflow patterns, planning workflow architecture, or asking about webhook processing, HTTP API integration, database operations, AI agent workflows, or scheduled tasks.","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fn8n\u002Fn8n-workflow-patterns\u002FSKILL.md",[232,235],{"categoryNo":233,"categoryName":234,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":114,"categoryName":115,"taxonomyType":21,"taxonomyTypeDesc":22},[121,237,174,238,38,239,240,167,241,215,211,242,243,244,245,246,247,248,249,250],"n8n workflow patterns","workflow patterns","n8n workflow","ai agent","architecture","database","api","planning workflow","structure","database operations","webhook","processing","http api","scheduled"]