[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6JM1WC9CBKXW0DXFP7PX5H3":3,"skill-same:01M6JM1WC9CBKXW0DXFP7PX5H3":82},{"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":23,"publishedAt":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":48,"commitSha":49,"skillDirectory":50,"forksCount":51,"repositoryHtmlUrl":52,"repositoryHomepageUrl":6,"ownerHtmlUrl":53,"ownerBlogUrl":6,"ownerDescription":54,"updatedAt":55,"install":56,"resources":69},"01M6JM1WC9CBKXW0DXFP7PX5H3","reactome-database",null,"Query Reactome REST API for pathway analysis, enrichment, gene-pathway mapping, disease pathways, molecular interactions, expression analysis, for systems biology studies.","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\u002Fscientific\u002Freactome-database\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"reactome database","Reactome","database","systems biology","gene","for","biology","pathways","mapping","pathway","expression","systems","rest api","molecular","query","api","analysis","REST","enrichment","2026-11-10 10:12:30",false,"---\nname: reactome-database\ndescription: \"Query Reactome REST API for pathway analysis, enrichment, gene-pathway mapping, disease pathways, molecular interactions, expression analysis, for systems biology studies.\"\n---\n\n# Reactome Database\n\n## Overview\n\nReactome is a free, open-source, curated pathway database with 2,825+ human pathways. Query biological pathways, perform overrepresentation and expression analysis, map genes to pathways, explore molecular interactions via REST API and Python client for systems biology research.\n\n## When to Use This Skill\n\nThis skill should be used when:\n- Performing pathway enrichment analysis on gene or protein lists\n- Analyzing gene expression data to identify relevant biological pathways\n- Querying specific pathway information, reactions, or molecular interactions\n- Mapping genes or proteins to biological pathways and processes\n- Exploring disease-related pathways and mechanisms\n- Visualizing analysis results in the Reactome Pathway Browser\n- Conducting comparative pathway analysis across species\n\n## Core Capabilities\n\nReactome provides two main API services and a Python client library:\n\n### 1. Content Service - Data Retrieval\n\nQuery and retrieve biological pathway data, molecular interactions, and entity information.\n\n**Common operations:**\n- Retrieve pathway information and hierarchies\n- Query specific entities (proteins, reactions, complexes)\n- Get participating molecules in pathways\n- Access database version and metadata\n- Explore pathway compartments and locations\n\n**API Base URL:** `https:\u002F\u002Freactome.org\u002FContentService`\n\n### 2. Analysis Service - Pathway Analysis\n\nPerform computational analysis on gene lists and expression data.\n\n**Analysis types:**\n- **Overrepresentation Analysis**: Identify statistically significant pathways from gene\u002Fprotein lists\n- **Expression Data Analysis**: Analyze gene expression datasets to find relevant pathways\n- **Species Comparison**: Compare pathway data across different organisms\n\n**API Base URL:** `https:\u002F\u002Freactome.org\u002FAnalysisService`\n\n### 3. reactome2py Python Package\n\nPython client library that wraps Reactome API calls for easier programmatic access.\n\n**Installation:**\n```bash\nuv pip install reactome2py\n```\n\n**Note:** The reactome2py package (version 3.0.0, released January 2021) is functional but not actively maintained. For the most up-to-date functionality, consider using direct REST API calls.\n\n## Querying Pathway Data\n\n### Using Content Service REST API\n\nThe Content Service uses REST protocol and returns data in JSON or plain text formats.\n\n**Get database version:**\n```python\nimport requests\n\nresponse = requests.get(\"https:\u002F\u002Freactome.org\u002FContentService\u002Fdata\u002Fdatabase\u002Fversion\")\nversion = response.text\nprint(f\"Reactome version: {version}\")\n```\n\n**Query a specific entity:**\n```python\nimport requests\n\nentity_id = \"R-HSA-69278\"  # Example pathway ID\nresponse = requests.get(f\"https:\u002F\u002Freactome.org\u002FContentService\u002Fdata\u002Fquery\u002F{entity_id}\")\ndata = response.json()\n```\n\n**Get participating molecules in a pathway:**\n```python\nimport requests\n\nevent_id = \"R-HSA-69278\"\nresponse = requests.get(\n    f\"https:\u002F\u002Freactome.org\u002FContentService\u002Fdata\u002Fevent\u002F{event_id}\u002FparticipatingPhysicalEntities\"\n)\nmolecules = response.json()\n```\n\n### Using reactome2py Package\n\n```python\nimport reactome2py\nfrom reactome2py import content\n\n# Query pathway information\npathway_info = content.query_by_id(\"R-HSA-69278\")\n\n# Get database version\nversion = content.get_database_version()\n```\n\n**For detailed API endpoints and parameters**, refer to `references\u002Fapi_reference.md` in this skill.\n\n## Performing Pathway Analysis\n\n### Overrepresentation Analysis\n\nSubmit a list of gene\u002Fprotein identifiers to find enriched pathways.\n\n**Using REST API:**\n```python\nimport requests\n\n# Prepare identifier list\nidentifiers = [\"TP53\", \"BRCA1\", \"EGFR\", \"MYC\"]\ndata = \"\\n\".join(identifiers)\n\n# Submit analysis\nresponse = requests.post(\n    \"https:\u002F\u002Freactome.org\u002FAnalysisService\u002Fidentifiers\u002F\",\n    headers={\"Content-Type\": \"text\u002Fplain\"},\n    data=data\n)\n\nresult = response.json()\ntoken = result[\"summary\"][\"token\"]  # Save token to retrieve results later\n\n# Access pathways\nfor pathway in result[\"pathways\"]:\n    print(f\"{pathway['stId']}: {pathway['name']} (p-value: {pathway['entities']['pValue']})\")\n```\n\n**Retrieve analysis by token:**\n```python\n# Token is valid for 7 days\nresponse = requests.get(f\"https:\u002F\u002Freactome.org\u002FAnalysisService\u002Ftoken\u002F{token}\")\nresults = response.json()\n```\n\n### Expression Data Analysis\n\nAnalyze gene expression datasets with quantitative values.\n\n**Input format (TSV with header starting with #):**\n```\n#Gene\tSample1\tSample2\tSample3\nTP53\t2.5\t3.1\t2.8\nBRCA1\t1.2\t1.5\t1.3\nEGFR\t4.5\t4.2\t4.8\n```\n\n**Submit expression data:**\n```python\nimport requests\n\n# Read TSV file\nwith open(\"expression_data.tsv\", \"r\") as f:\n    data = f.read()\n\nresponse = requests.post(\n    \"https:\u002F\u002Freactome.org\u002FAnalysisService\u002Fidentifiers\u002F\",\n    headers={\"Content-Type\": \"text\u002Fplain\"},\n    data=data\n)\n\nresult = response.json()\n```\n\n### Species Projection\n\nMap identifiers to human pathways exclusively using the `\u002Fprojection\u002F` endpoint:\n\n```python\nresponse = requests.post(\n    \"https:\u002F\u002Freactome.org\u002FAnalysisService\u002Fidentifiers\u002Fprojection\u002F\",\n    headers={\"Content-Type\": \"text\u002Fplain\"},\n    data=data\n)\n```\n\n## Visualizing Results\n\nAnalysis results can be visualized in the Reactome Pathway Browser by constructing URLs with the analysis token:\n\n```python\ntoken = result[\"summary\"][\"token\"]\npathway_id = \"R-HSA-69278\"\nurl = f\"https:\u002F\u002Freactome.org\u002FPathwayBrowser\u002F#{pathway_id}&DTAB=AN&ANALYSIS={token}\"\nprint(f\"View results: {url}\")\n```\n\n## Working with Analysis Tokens\n\n- Analysis tokens are valid for **7 days**\n- Tokens allow retrieval of previously computed results without re-submission\n- Store tokens to access results across sessions\n- Use `GET \u002Ftoken\u002F{TOKEN}` endpoint to retrieve results\n\n## Data Formats and Identifiers\n\n### Supported Identifier Types\n\nReactome accepts various identifier formats:\n- UniProt accessions (e.g., P04637)\n- Gene symbols (e.g., TP53)\n- Ensembl IDs (e.g., ENSG00000141510)\n- EntrezGene IDs (e.g., 7157)\n- ChEBI IDs for small molecules\n\nThe system automatically detects identifier types.\n\n### Input Format Requirements\n\n**For overrepresentation analysis:**\n- Plain text list of identifiers (one per line)\n- OR single column in TSV format\n\n**For expression analysis:**\n- TSV format with mandatory header row starting with \"#\"\n- Column 1: identifiers\n- Columns 2+: numeric expression values\n- Use period (.) as decimal separator\n\n### Output Format\n\nAll API responses return JSON containing:\n- `pathways`: Array of enriched pathways with statistical metrics\n- `summary`: Analysis metadata and token\n- `entities`: Matched and unmapped identifiers\n- Statistical values: pValue, FDR (false discovery rate)\n\n## Helper Scripts\n\nThis skill includes `scripts\u002Freactome_query.py`, a helper script for common Reactome operations:\n\n```bash\n# Query pathway information\npython scripts\u002Freactome_query.py query R-HSA-69278\n\n# Perform overrepresentation analysis\npython scripts\u002Freactome_query.py analyze gene_list.txt\n\n# Get database version\npython scripts\u002Freactome_query.py version\n```\n\n## Additional Resources\n\n- **API Documentation**: https:\u002F\u002Freactome.org\u002Fdev\n- **User Guide**: https:\u002F\u002Freactome.org\u002Fuserguide\n- **Documentation Portal**: https:\u002F\u002Freactome.org\u002Fdocumentation\n- **Data Downloads**: https:\u002F\u002Freactome.org\u002Fdownload-data\n- **reactome2py Docs**: https:\u002F\u002Freactome.github.io\u002Freactome2py\u002F\n\nFor comprehensive API endpoint documentation, see `references\u002Fapi_reference.md` in this skill.\n\n## Current Database Statistics (Version 94, September 2025)\n\n- 2,825 human pathways\n- 16,002 reactions\n- 11,630 proteins\n- 2,176 small molecules\n- 1,070 drugs\n- 41,373 literature references\n","# Reactome Database\n\n## Overview\n\nReactome is a free, open-source, curated pathway database with 2,825+ human pathways. Query biological pathways, perform overrepresentation and expression analysis, map genes to pathways, explore molecular interactions via REST API and Python client for systems biology research.\n\n## When to Use This Skill\n\nThis skill should be used when:\n- Performing pathway enrichment analysis on gene or protein lists\n- Analyzing gene expression data to identify relevant biological pathways\n- Querying specific pathway information, reactions, or molecular interactions\n- Mapping genes or proteins to biological pathways and processes\n- Exploring disease-related pathways and mechanisms\n- Visualizing analysis results in the Reactome Pathway Browser\n- Conducting comparative pathway analysis across species\n\n## Core Capabilities\n\nReactome provides two main API services and a Python client library:\n\n### 1. Content Service - Data Retrieval\n\nQuery and retrieve biological pathway data, molecular interactions, and entity information.\n\n**Common operations:**\n- Retrieve pathway information and hierarchies\n- Query specific entities (proteins, reactions, complexes)\n- Get participating molecules in pathways\n- Access database version and metadata\n- Explore pathway compartments and locations\n\n**API Base URL:** `https:\u002F\u002Freactome.org\u002FContentService`\n\n### 2. Analysis Service - Pathway Analysis\n\nPerform computational analysis on gene lists and expression data.\n\n**Analysis types:**\n- **Overrepresentation Analysis**: Identify statistically significant pathways from gene\u002Fprotein lists\n- **Expression Data Analysis**: Analyze gene expression datasets to find relevant pathways\n- **Species Comparison**: Compare pathway data across different organisms\n\n**API Base URL:** `https:\u002F\u002Freactome.org\u002FAnalysisService`\n\n### 3. reactome2py Python Package\n\nPython client library that wraps Reactome API calls for easier programmatic access.\n\n**Installation:**\n```bash\nuv pip install reactome2py\n```\n\n**Note:** The reactome2py package (version 3.0.0, released January 2021) is functional but not actively maintained. For the most up-to-date functionality, consider using direct REST API calls.\n\n## Querying Pathway Data\n\n### Using Content Service REST API\n\nThe Content Service uses REST protocol and returns data in JSON or plain text formats.\n\n**Get database version:**\n```python\nimport requests\n\nresponse = requests.get(\"https:\u002F\u002Freactome.org\u002FContentService\u002Fdata\u002Fdatabase\u002Fversion\")\nversion = response.text\nprint(f\"Reactome version: {version}\")\n```\n\n**Query a specific entity:**\n```python\nimport requests\n\nentity_id = \"R-HSA-69278\"  # Example pathway ID\nresponse = requests.get(f\"https:\u002F\u002Freactome.org\u002FContentService\u002Fdata\u002Fquery\u002F{entity_id}\")\ndata = response.json()\n```\n\n**Get participating molecules in a pathway:**\n```python\nimport requests\n\nevent_id = \"R-HSA-69278\"\nresponse = requests.get(\n    f\"https:\u002F\u002Freactome.org\u002FContentService\u002Fdata\u002Fevent\u002F{event_id}\u002FparticipatingPhysicalEntities\"\n)\nmolecules = response.json()\n```\n\n### Using reactome2py Package\n\n```python\nimport reactome2py\nfrom reactome2py import content\n\n# Query pathway information\npathway_info = content.query_by_id(\"R-HSA-69278\")\n\n# Get database version\nversion = content.get_database_version()\n```\n\n**For detailed API endpoints and parameters**, refer to `references\u002Fapi_reference.md` in this skill.\n\n## Performing Pathway Analysis\n\n### Overrepresentation Analysis\n\nSubmit a list of gene\u002Fprotein identifiers to find enriched pathways.\n\n**Using REST API:**\n```python\nimport requests\n\n# Prepare identifier list\nidentifiers = [\"TP53\", \"BRCA1\", \"EGFR\", \"MYC\"]\ndata = \"\\n\".join(identifiers)\n\n# Submit analysis\nresponse = requests.post(\n    \"https:\u002F\u002Freactome.org\u002FAnalysisService\u002Fidentifiers\u002F\",\n    headers={\"Content-Type\": \"text\u002Fplain\"},\n    data=data\n)\n\nresult = response.json()\ntoken = result[\"summary\"][\"token\"]  # Save token to retrieve results later\n\n# Access pathways\nfor pathway in result[\"pathways\"]:\n    print(f\"{pathway['stId']}: {pathway['name']} (p-value: {pathway['entities']['pValue']})\")\n```\n\n**Retrieve analysis by token:**\n```python\n# Token is valid for 7 days\nresponse = requests.get(f\"https:\u002F\u002Freactome.org\u002FAnalysisService\u002Ftoken\u002F{token}\")\nresults = response.json()\n```\n\n### Expression Data Analysis\n\nAnalyze gene expression datasets with quantitative values.\n\n**Input format (TSV with header starting with #):**\n```\n#Gene\tSample1\tSample2\tSample3\nTP53\t2.5\t3.1\t2.8\nBRCA1\t1.2\t1.5\t1.3\nEGFR\t4.5\t4.2\t4.8\n```\n\n**Submit expression data:**\n```python\nimport requests\n\n# Read TSV file\nwith open(\"expression_data.tsv\", \"r\") as f:\n    data = f.read()\n\nresponse = requests.post(\n    \"https:\u002F\u002Freactome.org\u002FAnalysisService\u002Fidentifiers\u002F\",\n    headers={\"Content-Type\": \"text\u002Fplain\"},\n    data=data\n)\n\nresult = response.json()\n```\n\n### Species Projection\n\nMap identifiers to human pathways exclusively using the `\u002Fprojection\u002F` endpoint:\n\n```python\nresponse = requests.post(\n    \"https:\u002F\u002Freactome.org\u002FAnalysisService\u002Fidentifiers\u002Fprojection\u002F\",\n    headers={\"Content-Type\": \"text\u002Fplain\"},\n    data=data\n)\n```\n\n## Visualizing Results\n\nAnalysis results can be visualized in the Reactome Pathway Browser by constructing URLs with the analysis token:\n\n```python\ntoken = result[\"summary\"][\"token\"]\npathway_id = \"R-HSA-69278\"\nurl = f\"https:\u002F\u002Freactome.org\u002FPathwayBrowser\u002F#{pathway_id}&DTAB=AN&ANALYSIS={token}\"\nprint(f\"View results: {url}\")\n```\n\n## Working with Analysis Tokens\n\n- Analysis tokens are valid for **7 days**\n- Tokens allow retrieval of previously computed results without re-submission\n- Store tokens to access results across sessions\n- Use `GET \u002Ftoken\u002F{TOKEN}` endpoint to retrieve results\n\n## Data Formats and Identifiers\n\n### Supported Identifier Types\n\nReactome accepts various identifier formats:\n- UniProt accessions (e.g., P04637)\n- Gene symbols (e.g., TP53)\n- Ensembl IDs (e.g., ENSG00000141510)\n- EntrezGene IDs (e.g., 7157)\n- ChEBI IDs for small molecules\n\nThe system automatically detects identifier types.\n\n### Input Format Requirements\n\n**For overrepresentation analysis:**\n- Plain text list of identifiers (one per line)\n- OR single column in TSV format\n\n**For expression analysis:**\n- TSV format with mandatory header row starting with \"#\"\n- Column 1: identifiers\n- Columns 2+: numeric expression values\n- Use period (.) as decimal separator\n\n### Output Format\n\nAll API responses return JSON containing:\n- `pathways`: Array of enriched pathways with statistical metrics\n- `summary`: Analysis metadata and token\n- `entities`: Matched and unmapped identifiers\n- Statistical values: pValue, FDR (false discovery rate)\n\n## Helper Scripts\n\nThis skill includes `scripts\u002Freactome_query.py`, a helper script for common Reactome operations:\n\n```bash\n# Query pathway information\npython scripts\u002Freactome_query.py query R-HSA-69278\n\n# Perform overrepresentation analysis\npython scripts\u002Freactome_query.py analyze gene_list.txt\n\n# Get database version\npython scripts\u002Freactome_query.py version\n```\n\n## Additional Resources\n\n- **API Documentation**: https:\u002F\u002Freactome.org\u002Fdev\n- **User Guide**: https:\u002F\u002Freactome.org\u002Fuserguide\n- **Documentation Portal**: https:\u002F\u002Freactome.org\u002Fdocumentation\n- **Data Downloads**: https:\u002F\u002Freactome.org\u002Fdownload-data\n- **reactome2py Docs**: https:\u002F\u002Freactome.github.io\u002Freactome2py\u002F\n\nFor comprehensive API endpoint documentation, see 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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",[139],{"categoryNo":90,"categoryName":91,"taxonomyType":21,"taxonomyTypeDesc":22},[98,99,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,29,111],"Workflow Automation","ai agents","accessibility","workflows","apache","execution","inngest","parallel","orchestrator","resume","n8n","payment","worker","scripts","temporal","with",{"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":163,"publishedAt":43},"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",[],[164,165,166,167,168,144,169,145,146,170,103,171,172,173,174,175,176,156,29,111],"dev","trigger dev","trigger","design","typescript","async execution","first","async","developer experience","expert","task","background jobs","long",{"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":43},"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",[183],{"categoryNo":184,"categoryName":185,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",[187,188,189,156,190,98,191,192,193,194,195,196,103,197,198,199,200,201,202,29],"planning with files","planning","files","with files","storage","tasks","markdown","knowledge","projects","work","manus","structured output","progress","transforms","multi","output",{"skillNo":204,"name":205,"nameZh":6,"description":206,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":207,"categories":208,"keywords":211,"publishedAt":43},"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",[209,210],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":90,"categoryName":91,"taxonomyType":21,"taxonomyTypeDesc":22},[98,212,151,213,94,214,215,144,216,192,188,26,39,217,218,219,220,221,222,223],"n8n workflow patterns","workflow patterns","n8n workflow","ai agent","architecture","planning workflow","structure","database operations","webhook","processing","http api","scheduled"]