# workflow_versions

workflow_id: 2yvmpoabN4rjILDd
workflow_name: [MCP-TEST] Partial - Continue On Error 1775078214254
trigger: partial_update
created_at: 2026-04-01 21:16:54
workflow_snapshot:
  {"updatedAt":"2026-04-01T21:16:54.326Z","createdAt":"2026-04-01T21:16:54.326Z","id":"2yvmpoabN4rjILDd","name":"[MCP-TEST] Partial - Continue On Error 1775078214254","description":null,"active":false,"isArchived":false,"nodes":[{"id":"webhook-1","name":"Webhook","type":"n8n-nodes-base.webhook","typeVersion":2,"position":[250,300],"parameters":{"httpMethod":"GET","path":"test-webhook"},"webhookId":"ebfda755-dd2d-46f1-93d4-8822e7c95bcf"}],"connections":{},"settings":{"executionOrder":"v1","callerPolicy":"workflowsFromSameOwner","availableInMCP":false},"staticData":null,"meta":null,"pinData":null,"versionId":"559f6ff4-429e-426c-852b-68a9c3cc0f39","activeVersionId":null,"versionCounter":1,"triggerCount":0,"shared":[{"updatedAt":"2026-04-01T21:16:54.342Z","createdAt":"2026-04-01T21:16:54.342Z","role":"workflow:owner","workflowId":"2yvmpoabN4rjILDd","projectId":"K0HtvGddtPI24Iyt","project":{"updatedAt":"2025-10-03T09:49:33.347Z","createdAt":"2025-10-03T09:48:05.062Z","id":"K0HtvGddtPI24Iyt","name":"Romuald Członkowski <romuald@n8n-mcp.com>","type":"personal","icon":null,"description":null,"creatorId":"ebb1bcfe-d103-4506-a994-9b18ef25e289"}}],"tags":[],"activeVersion":null}

operations:
  [{"type":"updateName","name":"[MCP-TEST] Partial - Continue On Error Updated 1775078214354"},{"type":"removeNode","nodeName":"NonExistentNode"},{"type":"addTag","tag":"new-tag"}]


workflow_id: TlWBEPerwjU9wHzv
workflow_name: [MCP-TEST] Partial - Reject Empty Connections 1775078214759
trigger: partial_update
operations: [{"type":"removeConnection","source":"Webhook","target":"HTTP Request"}]
created_at: 2026-04-01 21:16:54
workflow_snapshot:
  {"updatedAt":"2026-04-01T21:16:54.817Z","createdAt":"2026-04-01T21:16:54.817Z","id":"TlWBEPerwjU9wHzv","name":"[MCP-TEST] Partial - Reject Empty Connections 1775078214759","description":null,"active":false,"isArchived":false,"nodes":[{"id":"webhook-1","name":"Webhook","type":"n8n-nodes-base.webhook","typeVersion":2,"position":[250,300],"parameters":{"httpMethod":"GET","path":"trigger"},"webhookId":"d0f269c5-74ab-4293-96ab-aa85a1da7e75"},{"id":"http-1","name":"HTTP Request","type":"n8n-nodes-base.httpRequest","typeVersion":4.2,"position":[450,300],"parameters":{"url":"https://httpbin.org/get","method":"GET"}}],"connections":{"Webhook":{"main":[[{"node":"HTTP Request","type":"main","index":0}]]}},"settings":{"executionOrder":"v1","callerPolicy":"workflowsFromSameOwner","availableInMCP":false},"staticData":null,"meta":null,"pinData":null,"versionId":"eeba5b96-aa5d-4a14-b43d-94db4c5ce06f","activeVersionId":null,"versionCounter":1,"triggerCount":0,"shared":[{"updatedAt":"2026-04-01T21:16:54.819Z","createdAt":"2026-04-01T21:16:54.819Z","role":"workflow:owner","workflowId":"TlWBEPerwjU9wHzv","projectId":"K0HtvGddtPI24Iyt","project":{"updatedAt":"2025-10-03T09:49:33.347Z","createdAt":"2025-10-03T09:48:05.062Z","id":"K0HtvGddtPI24Iyt","name":"Romuald Członkowski <romuald@n8n-mcp.com>","type":"personal","icon":null,"description":null,"creatorId":"ebb1bcfe-d103-4506-a994-9b18ef25e289"}}],"tags":[],"activeVersion":null}


workflow_id: hSJNjsGqKj2QE9Yx
workflow_name: [MCP-TEST] Partial - Reject Single Non-Webhook 1775078214950
trigger: partial_update
created_at: 2026-04-01 21:16:55
workflow_snapshot:
  {"updatedAt":"2026-04-01T21:16:55.011Z","createdAt":"2026-04-01T21:16:55.011Z","id":"hSJNjsGqKj2QE9Yx","name":"[MCP-TEST] Partial - Reject Single Non-Webhook 1775078214950","description":null,"active":false,"isArchived":false,"nodes":[{"id":"webhook-1","name":"Webhook","type":"n8n-nodes-base.webhook","typeVersion":2,"position":[250,300],"parameters":{"httpMethod":"POST","path":"multi-node"},"webhookId":"79de468c-a412-45df-9881-f7fc305be5b4"},{"id":"set-1","name":"Set 1","type":"n8n-nodes-base.set","typeVersion":3.4,"position":[450,200],"parameters":{"assignments":{"assignments":[{"id":"assign-1","name":"branch","value":"top","type":"string"}]},"options":{}}},{"id":"set-2","name":"Set 2","type":"n8n-nodes-base.set","typeVersion":3.4,"position":[450,400],"parameters":{"assignments":{"assignments":[{"id":"assign-2","name":"branch","value":"bottom","type":"string"}]},"options":{}}},{"id":"merge-1","name":"Merge","type":"n8n-nodes-base.merge","typeVersion":3,"position":[650,300],"parameters":{"mode":"append","options":{}}}],"connections":{"Webhook":{"main":[[{"node":"Set 1","type":"main","index":0},{"node":"Set 2","type":"main","index":0}]]},"Set 1":{"main":[[{"node":"Merge","type":"main","index":0}]]},"Set 2":{"main":[[{"node":"Merge","type":"main","index":1}]]}},"settings":{"executionOrder":"v1","callerPolicy":"workflowsFromSameOwner","availableInMCP":false},"staticData":null,"meta":null,"pinData":null,"versionId":"95b4a5e4-68b6-4539-8f24-995675b9fdec","activeVersionId":null,"versionCounter":1,"triggerCount":0,"shared":[{"updatedAt":"2026-04-01T21:16:55.013Z","createdAt":"2026-04-01T21:16:55.013Z","role":"workflow:owner","workflowId":"hSJNjsGqKj2QE9Yx","projectId":"K0HtvGddtPI24Iyt","project":{"updatedAt":"2025-10-03T09:49:33.347Z","createdAt":"2025-10-03T09:48:05.062Z","id":"K0HtvGddtPI24Iyt","name":"Romuald Członkowski <romuald@n8n-mcp.com>","type":"personal","icon":null,"description":null,"creatorId":"ebb1bcfe-d103-4506-a994-9b18ef25e289"}}],"tags":[],"activeVersion":null}

operations:
[cell repeats an earlier one in this file: 126 characters]


workflow_id: IZ14PUwlNaYFJpDP
workflow_name: [MCP-TEST] Partial - Valid Update 1775078215142
trigger: partial_update
created_at: 2026-04-01 21:16:55
workflow_snapshot:
  {"updatedAt":"2026-04-01T21:16:55.206Z","createdAt":"2026-04-01T21:16:55.206Z","id":"IZ14PUwlNaYFJpDP","name":"[MCP-TEST] Partial - Valid Update 1775078215142","description":null,"active":false,"isArchived":false,"nodes":[{"id":"webhook-1","name":"Webhook","type":"n8n-nodes-base.webhook","typeVersion":2,"position":[250,300],"parameters":{"httpMethod":"POST","path":"multi-node"},"webhookId":"79de468c-a412-45df-9881-f7fc305be5b4"},{"id":"set-1","name":"Set 1","type":"n8n-nodes-base.set","typeVersion":3.4,"position":[450,200],"parameters":{"assignments":{"assignments":[{"id":"assign-1","name":"branch","value":"top","type":"string"}]},"options":{}}},{"id":"set-2","name":"Set 2","type":"n8n-nodes-base.set","typeVersion":3.4,"position":[450,400],"parameters":{"assignments":{"assignments":[{"id":"assign-2","name":"branch","value":"bottom","type":"string"}]},"options":{}}},{"id":"merge-1","name":"Merge","type":"n8n-nodes-base.merge","typeVersion":3,"position":[650,300],"parameters":{"mode":"append","options":{}}}],"connections":{"Webhook":{"main":[[{"node":"Set 1","type":"main","index":0},{"node":"Set 2","type":"main","index":0}]]},"Set 1":{"main":[[{"node":"Merge","type":"main","index":0}]]},"Set 2":{"main":[[{"node":"Merge","type":"main","index":1}]]}},"settings":{"executionOrder":"v1","callerPolicy":"workflowsFromSameOwner","availableInMCP":false},"staticData":null,"meta":null,"pinData":null,"versionId":"e3c08f11-f301-46e7-bcbd-dc2edfe96e95","activeVersionId":null,"versionCounter":1,"triggerCount":0,"shared":[{"updatedAt":"2026-04-01T21:16:55.208Z","createdAt":"2026-04-01T21:16:55.208Z","role":"workflow:owner","workflowId":"IZ14PUwlNaYFJpDP","projectId":"K0HtvGddtPI24Iyt","project":{"updatedAt":"2025-10-03T09:49:33.347Z","createdAt":"2025-10-03T09:48:05.062Z","id":"K0HtvGddtPI24Iyt","name":"Romuald Członkowski <romuald@n8n-mcp.com>","type":"personal","icon":null,"description":null,"creatorId":"ebb1bcfe-d103-4506-a994-9b18ef25e289"}}],"tags":[],"activeVersion":null}

operations:
[cell repeats an earlier one in this file: 232 characters]


workflow_id: C4OUDwyVBriXCK4R
workflow_name: [MCP-TEST] Partial - Reject Disconnected Node 1775078215511
trigger: partial_update
created_at: 2026-04-01 21:16:55
workflow_snapshot:
  {"updatedAt":"2026-04-01T21:16:55.564Z","createdAt":"2026-04-01T21:16:55.564Z","id":"C4OUDwyVBriXCK4R","name":"[MCP-TEST] Partial - Reject Disconnected Node 1775078215511","description":null,"active":false,"isArchived":false,"nodes":[{"id":"webhook-1","name":"Webhook","type":"n8n-nodes-base.webhook","typeVersion":2,"position":[250,300],"parameters":{"httpMethod":"GET","path":"trigger"},"webhookId":"d0f269c5-74ab-4293-96ab-aa85a1da7e75"},{"id":"http-1","name":"HTTP Request","type":"n8n-nodes-base.httpRequest","typeVersion":4.2,"position":[450,300],"parameters":{"url":"https://httpbin.org/get","method":"GET"}}],"connections":{"Webhook":{"main":[[{"node":"HTTP Request","type":"main","index":0}]]}},"settings":{"executionOrder":"v1","callerPolicy":"workflowsFromSameOwner","availableInMCP":false},"staticData":null,"meta":null,"pinData":null,"versionId":"eadf3679-a545-450e-bf4b-61f6e67bba49","activeVersionId":null,"versionCounter":1,"triggerCount":0,"shared":[{"updatedAt":"2026-04-01T21:16:55.565Z","createdAt":"2026-04-01T21:16:55.565Z","role":"workflow:owner","workflowId":"C4OUDwyVBriXCK4R","projectId":"K0HtvGddtPI24Iyt","project":{"updatedAt":"2025-10-03T09:49:33.347Z","createdAt":"2025-10-03T09:48:05.062Z","id":"K0HtvGddtPI24Iyt","name":"Romuald Członkowski <romuald@n8n-mcp.com>","type":"personal","icon":null,"description":null,"creatorId":"ebb1bcfe-d103-4506-a994-9b18ef25e289"}}],"tags":[],"activeVersion":null}

operations:
[cell repeats an earlier one in this file: 170 characters]


workflow_id: 85DwR7TvNrS61mDM
workflow_name: [MCP-TEST] Autofix - Apply Webhook Path 1775078219232
trigger: partial_update
created_at: 2026-04-01 21:16:59
workflow_snapshot:
  {"updatedAt":"2026-04-01T21:16:59.297Z","createdAt":"2026-04-01T21:16:59.297Z","id":"85DwR7TvNrS61mDM","name":"[MCP-TEST] Autofix - Apply Webhook Path 1775078219232","description":null,"active":false,"isArchived":false,"nodes":[{"id":"webhook-1","name":"Webhook","type":"n8n-nodes-base.webhook","typeVersion":2,"position":[250,300],"parameters":{"httpMethod":"GET"},"webhookId":"06237e86-5def-43f6-a097-7860c1db2196"}],"connections":{},"settings":{"executionOrder":"v1","saveDataErrorExecution":"all","saveDataSuccessExecution":"all","saveManualExecutions":true,"saveExecutionProgress":true,"callerPolicy":"workflowsFromSameOwner","availableInMCP":false},"staticData":null,"meta":null,"pinData":null,"versionId":"c9e07e67-3399-4a43-a0f0-5f2538b50af9","activeVersionId":null,"versionCounter":1,"triggerCount":0,"shared":[{"updatedAt":"2026-04-01T21:16:59.300Z","createdAt":"2026-04-01T21:16:59.300Z","role":"workflow:owner","workflowId":"85DwR7TvNrS61mDM","projectId":"K0HtvGddtPI24Iyt","project":{"updatedAt":"2025-10-03T09:49:33.347Z","createdAt":"2025-10-03T09:48:05.062Z","id":"K0HtvGddtPI24Iyt","name":"Romuald Członkowski <romuald@n8n-mcp.com>","type":"personal","icon":null,"description":null,"creatorId":"ebb1bcfe-d103-4506-a994-9b18ef25e289"}}],"tags":[],"activeVersion":null}

operations:
  [{"type":"updateNode","nodeId":"Webhook","updates":{"parameters.path":"e7843f1f-3e86-4aa8-88b7-0a78102930e3","webhookId":"e7843f1f-3e86-4aa8-88b7-0a78102930e3","typeVersion":2.1}}]


workflow_id: 9dDx36e235NZo29A
workflow_name: [MCP-TEST] Update - Full Replacement 1775078220258
trigger: full_update
created_at: 2026-04-01 21:17:00
workflow_snapshot:
  {"updatedAt":"2026-04-01T21:17:00.419Z","createdAt":"2026-04-01T21:17:00.419Z","id":"9dDx36e235NZo29A","name":"[MCP-TEST] Update - Full Replacement 1775078220258","description":null,"active":false,"isArchived":false,"nodes":[{"id":"webhook-1","name":"Webhook","type":"n8n-nodes-base.webhook","typeVersion":2,"position":[250,300],"parameters":{"httpMethod":"GET","path":"test-webhook"},"webhookId":"798e5031-4554-45f2-9bd7-0edd3b9af114"}],"connections":{},"settings":{"executionOrder":"v1","callerPolicy":"workflowsFromSameOwner","availableInMCP":false},"staticData":null,"meta":null,"pinData":null,"versionId":"11f1259e-f716-4a0f-a5d2-20cc49840cd9","activeVersionId":null,"versionCounter":1,"triggerCount":0,"shared":[{"updatedAt":"2026-04-01T21:17:00.421Z","createdAt":"2026-04-01T21:17:00.421Z","role":"workflow:owner","workflowId":"9dDx36e235NZo29A","projectId":"K0HtvGddtPI24Iyt","project":{"updatedAt":"2025-10-03T09:49:33.347Z","createdAt":"2025-10-03T09:48:05.062Z","id":"K0HtvGddtPI24Iyt","name":"Romuald Członkowski <romuald@n8n-mcp.com>","type":"personal","icon":null,"description":null,"creatorId":"ebb1bcfe-d103-4506-a994-9b18ef25e289"}}],"tags":[],"activeVersion":null}


workflow_id: NzofSnIjkiNzhsd8
workflow_name: [MCP-TEST] Update - Nodes Only 1775078220752
trigger: full_update
created_at: 2026-04-01 21:17:00
workflow_snapshot:
  {"updatedAt":"2026-04-01T21:17:00.827Z","createdAt":"2026-04-01T21:17:00.827Z","id":"NzofSnIjkiNzhsd8","name":"[MCP-TEST] Update - Nodes Only 1775078220752","description":null,"active":false,"isArchived":false,"nodes":[{"id":"webhook-1","name":"Webhook","type":"n8n-nodes-base.webhook","typeVersion":2,"position":[250,300],"parameters":{"httpMethod":"GET","path":"test-webhook"},"webhookId":"798e5031-4554-45f2-9bd7-0edd3b9af114"}],"connections":{},"settings":{"executionOrder":"v1","callerPolicy":"workflowsFromSameOwner","availableInMCP":false},"staticData":null,"meta":null,"pinData":null,"versionId":"3278a779-664f-4bf4-bead-aff6071f8c01","activeVersionId":null,"versionCounter":1,"triggerCount":0,"shared":[{"updatedAt":"2026-04-01T21:17:00.829Z","createdAt":"2026-04-01T21:17:00.829Z","role":"workflow:owner","workflowId":"NzofSnIjkiNzhsd8","projectId":"K0HtvGddtPI24Iyt","project":{"updatedAt":"2025-10-03T09:49:33.347Z","createdAt":"2025-10-03T09:48:05.062Z","id":"K0HtvGddtPI24Iyt","name":"Romuald Członkowski <romuald@n8n-mcp.com>","type":"personal","icon":null,"description":null,"creatorId":"ebb1bcfe-d103-4506-a994-9b18ef25e289"}}],"tags":[],"activeVersion":null}


workflow_id: 7M1S8cNq2J1KXqEv
workflow_name: [MCP-TEST] Update - Settings 1775078221138
trigger: full_update
created_at: 2026-04-01 21:17:01
workflow_snapshot:
  {"updatedAt":"2026-04-01T21:17:01.196Z","createdAt":"2026-04-01T21:17:01.196Z","id":"7M1S8cNq2J1KXqEv","name":"[MCP-TEST] Update - Settings 1775078221138","description":null,"active":false,"isArchived":false,"nodes":[{"id":"webhook-1","name":"Webhook","type":"n8n-nodes-base.webhook","typeVersion":2,"position":[250,300],"parameters":{"httpMethod":"GET","path":"test-webhook"},"webhookId":"798e5031-4554-45f2-9bd7-0edd3b9af114"}],"connections":{},"settings":{"executionOrder":"v1","callerPolicy":"workflowsFromSameOwner","availableInMCP":false},"staticData":null,"meta":null,"pinData":null,"versionId":"8aa7f273-1596-46aa-abbe-eec8ac58edd4","activeVersionId":null,"versionCounter":1,"triggerCount":0,"shared":[{"updatedAt":"2026-04-01T21:17:01.199Z","createdAt":"2026-04-01T21:17:01.199Z","role":"workflow:owner","workflowId":"7M1S8cNq2J1KXqEv","projectId":"K0HtvGddtPI24Iyt","project":{"updatedAt":"2025-10-03T09:49:33.347Z","createdAt":"2025-10-03T09:48:05.062Z","id":"K0HtvGddtPI24Iyt","name":"Romuald Członkowski <romuald@n8n-mcp.com>","type":"personal","icon":null,"description":null,"creatorId":"ebb1bcfe-d103-4506-a994-9b18ef25e289"}}],"tags":[],"activeVersion":null}


workflow_id: SwUw7mIVVg1LiiLZ
workflow_name: [MCP-TEST] Update - Invalid Node Type 1775078221602
trigger: full_update
created_at: 2026-04-01 21:17:01
workflow_snapshot:
  {"updatedAt":"2026-04-01T21:17:01.675Z","createdAt":"2026-04-01T21:17:01.675Z","id":"SwUw7mIVVg1LiiLZ","name":"[MCP-TEST] Update - Invalid Node Type 1775078221602","description":null,"active":false,"isArchived":false,"nodes":[{"id":"webhook-1","name":"Webhook","type":"n8n-nodes-base.webhook","typeVersion":2,"position":[250,300],"parameters":{"httpMethod":"GET","path":"test-webhook"},"webhookId":"798e5031-4554-45f2-9bd7-0edd3b9af114"}],"connections":{},"settings":{"executionOrder":"v1","callerPolicy":"workflowsFromSameOwner","availableInMCP":false},"staticData":null,"meta":null,"pinData":null,"versionId":"09c2d116-3dc1-4a40-b8e4-69c436ca05ca","activeVersionId":null,"versionCounter":1,"triggerCount":0,"shared":[{"updatedAt":"2026-04-01T21:17:01.677Z","createdAt":"2026-04-01T21:17:01.677Z","role":"workflow:owner","workflowId":"SwUw7mIVVg1LiiLZ","projectId":"K0HtvGddtPI24Iyt","project":{"updatedAt":"2025-10-03T09:49:33.347Z","createdAt":"2025-10-03T09:48:05.062Z","id":"K0HtvGddtPI24Iyt","name":"Romuald Członkowski <romuald@n8n-mcp.com>","type":"personal","icon":null,"description":null,"creatorId":"ebb1bcfe-d103-4506-a994-9b18ef25e289"}}],"tags":[],"activeVersion":null}


workflow_id: m7iUop4ZCJCZn9ES
workflow_name: [MCP-TEST] Autofix - Response Format Apply 1775078221685
trigger: partial_update
created_at: 2026-04-01 21:17:01
workflow_snapshot:
  {"updatedAt":"2026-04-01T21:17:01.760Z","createdAt":"2026-04-01T21:17:01.760Z","id":"m7iUop4ZCJCZn9ES","name":"[MCP-TEST] Autofix - Response Format Apply 1775078221685","description":null,"active":false,"isArchived":false,"nodes":[{"id":"webhook-1","name":"Webhook","type":"n8n-nodes-base.webhook","typeVersion":2,"position":[250,300],"parameters":{"httpMethod":"GET"},"webhookId":"8802ce76-9528-4a75-8e61-dc27a8f40922"}],"connections":{},"settings":{"executionOrder":"v1","saveDataErrorExecution":"all","saveDataSuccessExecution":"all","saveManualExecutions":true,"saveExecutionProgress":true,"callerPolicy":"workflowsFromSameOwner","availableInMCP":false},"staticData":null,"meta":null,"pinData":null,"versionId":"2d745f57-ce54-47e8-b48e-ab806a549223","activeVersionId":null,"versionCounter":1,"triggerCount":0,"shared":[{"updatedAt":"2026-04-01T21:17:01.763Z","createdAt":"2026-04-01T21:17:01.763Z","role":"workflow:owner","workflowId":"m7iUop4ZCJCZn9ES","projectId":"K0HtvGddtPI24Iyt","project":{"updatedAt":"2025-10-03T09:49:33.347Z","createdAt":"2025-10-03T09:48:05.062Z","id":"K0HtvGddtPI24Iyt","name":"Romuald Członkowski <romuald@n8n-mcp.com>","type":"personal","icon":null,"description":null,"creatorId":"ebb1bcfe-d103-4506-a994-9b18ef25e289"}}],"tags":[],"activeVersion":null}

operations:
  [{"type":"updateNode","nodeId":"Webhook","updates":{"parameters.path":"cdd5b6c7-7736-4219-9360-68675f3ff739","webhookId":"cdd5b6c7-7736-4219-9360-68675f3ff739","typeVersion":2.1}},{"type":"updateNode","nodeId":"webhook-1","nodeName":"Webhook","updates":{"typeVersion":2.1,"parameters":{"httpMethod":"GET"},"webhookId":"72b559bd-ef42-49d6-b4a1-a85cd010b9fb"}}]


workflow_id: UGzNaL8X6g7SgmWC
workflow_name: [MCP-TEST] Update - Name Original 1775078221945
trigger: full_update
created_at: 2026-04-01 21:17:02
workflow_snapshot:
  {"updatedAt":"2026-04-01T21:17:02.006Z","createdAt":"2026-04-01T21:17:02.006Z","id":"UGzNaL8X6g7SgmWC","name":"[MCP-TEST] Update - Name Original 1775078221945","description":null,"active":false,"isArchived":false,"nodes":[{"id":"webhook-1","name":"Webhook","type":"n8n-nodes-base.webhook","typeVersion":2,"position":[250,300],"parameters":{"httpMethod":"GET","path":"test-webhook"},"webhookId":"798e5031-4554-45f2-9bd7-0edd3b9af114"}],"connections":{},"settings":{"executionOrder":"v1","callerPolicy":"workflowsFromSameOwner","availableInMCP":false},"staticData":null,"meta":null,"pinData":null,"versionId":"c69d5742-87db-46e9-a2ec-4c75dbf97ebf","activeVersionId":null,"versionCounter":1,"triggerCount":0,"shared":[{"updatedAt":"2026-04-01T21:17:02.017Z","createdAt":"2026-04-01T21:17:02.017Z","role":"workflow:owner","workflowId":"UGzNaL8X6g7SgmWC","projectId":"K0HtvGddtPI24Iyt","project":{"updatedAt":"2025-10-03T09:49:33.347Z","createdAt":"2025-10-03T09:48:05.062Z","id":"K0HtvGddtPI24Iyt","name":"Romuald Członkowski <romuald@n8n-mcp.com>","type":"personal","icon":null,"description":null,"creatorId":"ebb1bcfe-d103-4506-a994-9b18ef25e289"}}],"tags":[],"activeVersion":null}


workflow_id: 6EttOnGfXtU3HeBp
workflow_name: [MCP-TEST] Update - Multiple Props 1775078222317
trigger: full_update
created_at: 2026-04-01 21:17:02
workflow_snapshot:
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# templates

name: Create a Google Analytics Data Report with AI and sent it to E-Mail and Telegram
author_name: Friedemann Schuetz
author_username: freddy-schuetz
categories: []
created_at: 2024-12-25T14:56:34.079Z
updated_at: 2024-12-25T14:56:34.079Z
url: https://n8n.io/workflows/2673
scraped_at: 2025-12-24 13:02:44
description:
  What this workflow does
  This workflow retrieves Google Analytics data from the last 7 days and the same period in the previous year. The data is then prepared by AI as a table, analyzed and provided with a small summary.
  The summary is then sent by email to a desired address and, shortened and summarized again, sent to a Telegram account.

  This workflow has the following sequence:

  time trigger (e.g. every Monday at 7 a.m.)
  retrieval of Google Analytics data from the last 7 days
  assignment and summary of the data
  retrieval of Google Analytics data from the last 7 days of the previous year
  allocation and summary of the data
  preparation in tabular form and brief analysis by AI.
  sending the report as an email
  preparation in short form by AI for Telegram (optional)
  sending as Telegram message.

  Requirements
  The following accesses are required for the workflow:
  Google Analytics (via Google Analytics API): Documentation
  AI API access (e.g. via OpenAI, Anthropic, Google or Ollama)
  SMTP access data (for sending the mail)
  Telegram access data (optional for sending as Telegram message): Documentation

  Feel free to contact me via LinkedIn, if you have any questions!

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.googleAnalytics","n8n-nodes-base.emailSend","n8n-nodes-base.telegram","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.toolCalculator","n8n-nodes-base.googleAnalytics","n8n-nodes-base.summarize","n8n-nodes-base.summarize","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.openAi"]

workflow_json_compressed:
  [encoded data, 4840 characters]


name: Generate Stripe invoice and send it by email
author_name: Solomon
author_username: solomon
categories: []
created_at: 2024-12-26T23:01:02.981Z
updated_at: 2024-12-26T23:01:02.981Z
url: https://n8n.io/workflows/2675
scraped_at: 2025-12-24 13:02:47
description:
  Generating Stripe invoices through the API can be tricky since it requires four steps to generate and send it via email to the customer.

  With this workflow you can create Stripe invoices automatically and make Stripe send the invoices to the customer email.

  How it works

  To generate a Stripe invoice, you need to create a customer, specify the invoice items, create the invoice, and finalize it.

  What should be a simple task involves multiple steps.

  This workflow simplifies the process by providing everything pre-built for you.

  Who is this for?

  Anyone who wants to generate invoices automatically and send them to the customer’s email.

  Stripe will only send invoices to customers if you generate the invoice correctly through the API.
  .

  Check out my other templates

  👉 https://n8n.io/creators/solomon/

nodes_used:
  ["n8n-nodes-base.stripe","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.manualTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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name: Analyze Email Headers for IP Reputation and Spoofing Detection - Outlook
author_name: Angel Menendez
author_username: djangelic
categories: []
created_at: 2024-12-27T06:09:11.324Z
updated_at: 2024-12-27T06:09:11.324Z
url: https://n8n.io/workflows/2676
scraped_at: 2025-12-24 13:02:46
description:
  Analyze Emails for Security Insights

  Who is this for?

  This workflow is ideal for security teams, IT Ops professionals, and managed service providers (MSPs) responsible for monitoring and validating email traffic. It’s especially useful for organizations that need to identify potential phishing attempts, spam, or compromised accounts by analyzing email headers and IP reputation.

  What problem is this workflow solving?

  This workflow helps identify malicious or suspicious emails by verifying email authentication headers (SPF, DKIM, DMARC) and analyzing the reputation of the originating IP address. By automating these checks, it reduces manual analysis time and flags potential threats efficiently.

  What this workflow does

  Email Monitoring:** Polls a specified Microsoft Outlook folder for new emails in real-time.
  Header Analysis:** Retrieves and processes email headers to extract critical information such as authentication results and the sender’s IP address.
  IP Reputation Check:** Leverages external APIs (IP Quality Score and IP-API) to analyze the originating IP for potential spam or malicious activity.
  Authentication Validation:** Validates SPF, DKIM, and DMARC headers, determining if the email passes industry-standard authentication protocols.
  Data Aggregation and Reporting:** Combines all analyzed data into a unified format, ready for reporting or integration into downstream systems.
  Webhook Integration:** Outputs the findings via a webhook, enabling integration with alerting tools or security information and event management (SIEM) platforms.

  Setup

  Connect to Outlook:
     Configure the Microsoft Outlook trigger node with valid OAuth2 credentials.
     Specify the email folder to monitor for new messages.

  API Keys (Optional):
     Obtain an API key for IP Quality Score (https://ipqualityscore.com).
     Ensure the IP-API endpoint is accessible.
     This step is optional as ipqualityscore.com will provide a limited number of free lookups each month. See more details here.  

  Webhook Configuration:
     Set up a webhook endpoint to receive the output of the workflow.

  Optional Adjustments:
     Customize polling intervals in the trigger node.
     Modify header filters or extend the validation logic as needed.

  How to customize this workflow to your needs

  Add Alerts:** Use the Respond to Webhook node to trigger notifications in Slack, email, or any other communication channel.
  Integrate with SIEM:** Forward the workflow output to SIEM tools like Splunk or ELK Stack for further analysis.
  Modify Validation Rules:** Update SPF, DKIM, or DMARC logic in the Set nodes to align with your organization’s security policies.
  Expand IP Analysis:** Add more APIs or services to enrich IP reputation data, such as VirusTotal or AbuseIPDB.

  This workflow provides a robust foundation for email security monitoring and can be tailored to fit your organization's unique requirements. With its modular design and integration options, it’s a versatile tool to enhance your cybersecurity operations.

nodes_used:
  ["n8n-nodes-base.microsoftOutlookTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.limit","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.if","n8n-nodes-base.code","n8n-nodes-base.if","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.merge","n8n-nodes-base.aggregate","n8n-nodes-base.noOp","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.respondToWebhook","n8n-nodes-base.webhook","n8n-nodes-base.set","n8n-nodes-base.aggregate","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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name: Analyze Email Headers for IP Reputation and Spoofing Detection - Gmail
author_name: Angel Menendez
author_username: djangelic
categories: []
created_at: 2024-12-27T06:47:56.660Z
updated_at: 2024-12-27T06:47:56.660Z
url: https://n8n.io/workflows/2677
scraped_at: 2025-12-24 13:02:46
description:
  Analyze Emails for Security Insights

  Who is this for?

  This workflow is ideal for IT professionals, security analysts, and organizations looking to enhance their email security practices. It is particularly useful for those who need to analyze Gmail email headers for IP tracking, spoofing detection, and sender reputation assessment. 

  What problem is this workflow solving?

  Email spoofing and phishing attacks are significant cybersecurity threats. By analyzing email headers, this workflow provides detailed insights into the email's origin, authentication status, and the reputation of the sending IP address. It helps detect potential spoofing attempts and assess the trustworthiness of incoming emails.

  What this workflow does

  This n8n workflow automates the process of analyzing email headers received in Gmail. It performs the following key functions:

  Triggering and Email Header Extraction: It monitors Gmail inboxes for new emails and extracts their headers for analysis.
  Authentication Analysis: It validates SPF, DKIM, and DMARC authentication results to ensure the email adheres to industry-standard security protocols.
  IP Analysis: The workflow extracts the originating IP address and evaluates its reputation and geographic details using external APIs.
  Reputation Scoring: It integrates with IP Quality Score to detect spam activity and assess the sender's reputation.
  Consolidation and Webhook Response: All results are aggregated into a single JSON response, making it easy to integrate with third-party platforms or tools for further automation.

  Setup

  Authenticate Gmail: Configure the Gmail Trigger node with your Gmail account credentials.
  API Keys (Optional):
     Obtain an API key for IP Quality Score (https://ipqualityscore.com).
     Ensure the IP-API endpoint is accessible.
     This step is optional as ipqualityscore.com will provide a limited number of free lookups each month. See more details here.  
  Activate the Workflow: Ensure the workflow is active to process incoming emails in real-time.

  How to customize this workflow to your needs

  Add Alerts:** Use the Gmail - Respond to Webhook node to trigger notifications in Slack, email, or any other communication channel.
  Integrate with SIEM:** Forward the workflow output to SIEM tools like Splunk or ELK Stack for further analysis.
  Modify Validation Rules:** Update SPF, DKIM, or DMARC logic in the Set nodes to align with your organization’s security policies.
  Expand IP Analysis:** Add more APIs or services to enrich IP reputation data, such as VirusTotal or AbuseIPDB.

  This workflow provides a robust foundation for email security monitoring and can be tailored to fit your organization's unique requirements. With its modular design and integration options, it’s a versatile tool to enhance your cybersecurity operations.

nodes_used:
  ["n8n-nodes-base.gmailTrigger","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.if","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.if","n8n-nodes-base.if","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.webhook","n8n-nodes-base.limit","n8n-nodes-base.merge","n8n-nodes-base.aggregate","n8n-nodes-base.set","n8n-nodes-base.respondToWebhook","n8n-nodes-base.aggregate","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.noOp"]

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name: Export all Strava Activity Data to Google Sheets
author_name: Sherlockes
author_username: sherlockes
categories: []
created_at: 2024-12-28T22:23:06.727Z
updated_at: 2024-12-28T22:23:06.727Z
url: https://n8n.io/workflows/2678
scraped_at: 2025-12-24 13:02:47
description:
  What does this template help with?
  Save the data of activities recorded and stored in Strava to a Google Sheets document.

  How it works:
  We have a Google Sheets spreadsheet where each row represents a Strava activity with the date, reference, distance, time, and elevation.
  Periodically, the workflow checks the latest activities in our Strava account to see if any are missing from the spreadsheet and adds them to the list.
  All fields must be properly formatted according to how they are stored in the Google Sheets spreadsheet.

  Set up instructions
  Complete the Set up credentials step when you first open the workflow. You'll need a Google Sheets and Strava account.
  In the 'activities' node, you must enter the name of the file and the sheet where you want to save the imported data.
  In the 'Strava' node, you must select the corresponding credential.
  You can adjust the format of dates, times, and distances according to your needs in the 'strava_last' node.

  The rest of the information is available at  sherblog.es

  Template was created in n8n v1.72.1

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.strava","n8n-nodes-base.code","n8n-nodes-base.googleSheets","n8n-nodes-base.set","n8n-nodes-base.removeDuplicates","n8n-nodes-base.googleSheets","n8n-nodes-base.sort","n8n-nodes-base.limit","n8n-nodes-base.set","n8n-nodes-base.sort","n8n-nodes-base.limit","n8n-nodes-base.sort"]

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name: ⚡AI-Powered YouTube Video Summarization & Analysis
author_name: Joseph LePage
author_username: joe
categories: []
created_at: 2024-12-29T03:01:45.635Z
updated_at: 2024-12-29T03:01:45.635Z
url: https://n8n.io/workflows/2679
scraped_at: 2025-12-24 13:02:41
description:
  -- Disclaimer: This workflow uses a community node and therefore only works for self-hosted n8n users --



  Transform YouTube videos into comprehensive summaries and structured analysis instantly. This n8n workflow automatically extracts, processes, and analyzes video transcripts to deliver clear, organized insights without watching the entire video.

  Time-Saving Features
  🚀 Instant Processing
  Simply provide a YouTube URL and receive a structured summary within seconds, eliminating the need to watch lengthy videos. Perfect for research, learning, or content analysis.

  🤖 AI-Powered Analysis
  Leverages GPT-4o-mini to analyze video transcripts, organizing key concepts and insights into a clear, hierarchical structure with main topics and essential points.

  Smart Processing Pipeline
  📝 Automated Transcript Extraction
  Supports public YouTube video
  Handles multiple URL formats
  Extracts complete video transcripts automatically

  🧠 Intelligent Content Organization
  Breaks down content into main topics
  Highlights key concepts and terminology
  Maintains technical accuracy while improving clarity
  Structures information logically with markdown formatting

  Perfect For
  📚 Researchers & Students
  Quick comprehension of educational content and lectures without watching entire videos.

  💼 Business Professionals
  Efficient analysis of industry talks, presentations, and training materials.

  🎯 Content Creators
  Rapid research and competitive analysis of video content in your niche.

  Technical Implementation
  🔄 Workflow Components
  Webhook endpoint for URL submission
  YouTube API integration for video details
  Transcript extraction system
  GPT-4 powered analysis engine
  Telegram notification system (optional)

  Transform your video content consumption with an intelligent system that delivers structured, comprehensive summaries while saving hours of viewing time.

nodes_used:
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name: AI-Powered Social Media Amplifier
author_name: Mudit Juneja
author_username: beingmudit
categories: []
created_at: 2024-12-29T21:28:30.325Z
updated_at: 2024-12-29T21:28:30.325Z
url: https://n8n.io/workflows/2681
scraped_at: 2025-12-24 13:02:44
description:
  &gt; Reach out to me  for any setup help/consulting.


  Automate the curation and sharing of trending GitHub discussions from Hacker News to Twitter and LinkedIn. This workflow leverages AI to generate engaging posts, streamlining your social media content creation and distribution.

  How it Works

  Crawl Hacker News for GitHub Posts: The workflow fetches trending GitHub-related discussions from Hacker News.
  Extract Key Information: Relevant data such as post titles, URLs, and metadata are extracted and filtered to focus only on unposted content.
  Fetch Additional Details: For each GitHub post, the workflow retrieves extra information from the GitHub repository page to enrich the post content.
  Generate Social Media Posts: Using AI, the workflow automatically generates tailored posts for Twitter and LinkedIn based on the collected data.
  Post to Twitter & LinkedIn: The generated content is posted to your Twitter and LinkedIn accounts.
  Track and Log Posts: Each post is logged in Airtable for tracking, and its status is updated to ensure no duplicate posts are made.
  Telegram Notification: After posting, a summary of the posts is sent to your Telegram chat for real-time updates.

  Requirements

  n8n Account**: Set up and configured. Sign up here.
  API Credentials**: Valid keys for LinkedIn, Twitter, Airtable, OpenAI, and Telegram.
  Airtable Base**: Configured with fields such as Title, URL, Post Content, Status, and Timestamp. Get started with Airtable.
  Telegram Chat ID**: For receiving real-time notifications.


  Set Up Steps

  Clone the Workflow: Import the workflow into your n8n environment using the provided JSON.
  Configure API Credentials: Enter your API keys for LinkedIn, Twitter, Airtable, OpenAI, and Telegram into the respective nodes.
  Set Up Airtable Base: Create an Airtable base with fields such as Title, URL, Post Content, Status, and Timestamp.
  Customize Telegram Chat ID: Modify the 'Ping me' node with your Telegram chat ID to receive notifications.
  Run the Workflow: Activate the workflow to start the automated content curation and posting process.

  Additional Resources

  n8n AI Agentic Workflows Guide
  n8n AI Workflow Tutorial
  n8n Community Tutorial on Building an AI-Powered Telegram Bot


  &gt; Note: Chat GPT prompt should/can be tweaked in the step to give the desired behaviour.

  Sample Posts from my X and LinkedIn -** 

nodes_used:
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name: 🔍 Perplexity Research to HTML: AI-Powered Content Creation
author_name: Joseph LePage
author_username: joe
categories: []
created_at: 2024-12-29T22:20:41.173Z
updated_at: 2024-12-29T22:20:41.173Z
url: https://n8n.io/workflows/2682
scraped_at: 2025-12-24 13:02:42
description:
  Transform simple queries into comprehensive, well-structured content with this n8n workflow that leverages Perplexity AI for research and GPT-4 for content transformation. Create professional blog posts and HTML content automatically while maintaining accuracy and depth.

  Intelligent Research & Analysis

  🚀 Automated Research Pipeline
  Harnesses Perplexity AI's advanced research capabilities
  Processes complex topics into structured insights
  Delivers comprehensive analysis in minutes instead of hours

  🧠 Smart Content Organization
  Automatically structures content with clear hierarchies
  Identifies and highlights key concepts
  Maintains technical accuracy while improving readability
  Creates SEO-friendly content structure

  Content Transformation Features

  📝 Dynamic Content Generation
  Converts research into professional blog articles
  Generates clean, responsive HTML output
  Implements proper semantic structure
  Includes metadata and categorization

  🎨 Professional Formatting
  Responsive Tailwind CSS styling
  Clean, modern HTML structure
  Proper heading hierarchy
  Mobile-friendly layouts
  Blockquote highlighting for key insights 

  Perfect For

  📚 Content Researchers
  Save hours of manual research by automating the information gathering and structuring process.

  ✍️ Content Writers
  Focus on creativity while the workflow handles research and technical formatting.

  🌐 Web Publishers
  Generate publication-ready HTML content with modern styling and proper structure.

  Technical Implementation

  ⚡ Workflow Components
  Webhook endpoint for query submission
  Perplexity AI integration for research
  GPT-4 powered content structuring
  HTML transformation engine
  Telegram notification system (optional)

  Transform your content creation process with an intelligent system that handles research, writing, and formatting while you focus on strategy and creativity.

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name: AI Agent for project management and meetings with Airtable and Fireflies
author_name: Mark Shcherbakov
author_username: lowcodingdev
categories: []
created_at: 2024-12-30T10:31:08.754Z
updated_at: 2024-12-30T10:31:08.754Z
url: https://n8n.io/workflows/2683
scraped_at: 2025-12-24 13:02:44
description:
  Video Guide

  I prepared a comprehensive guide detailing how to create a Smart Agent that automates meeting task management by analyzing transcripts, generating tasks in Airtable, and scheduling follow-ups when necessary.



  Youtube Link

  Who is this for?
  This workflow is ideal for project managers, team leaders, and business owners looking to enhance productivity during meetings. It is particularly helpful for those who need to convert discussions into actionable items swiftly and effectively.

  What problem does this workflow solve?
  Managing action items from meetings can often lead to missed tasks and poor follow-up. This automation alleviates that issue by automatically generating tasks from meeting transcripts, keeping everyone informed about their responsibilities and streamlining communication.

  What this workflow does
  The workflow leverages n8n to create a Smart Agent that listens for completed meeting transcripts, processes them using AI, and generates tasks in Airtable. Key functionalities include:
  Capturing completed meeting events through webhooks.
  Extracting relevant meeting details such as transcripts and participants using API calls.
  Generating structured tasks from meeting discussions and sending notifications to clients.

  Webhook Integration: Listens for meeting completion events to trigger subsequent actions.
  API Requests for Data: Pulls necessary details like transcripts and participant information from Fireflies.
  Task and Notification Generation: Automatically creates tasks in Airtable and notifies clients of their responsibilities.

  Setup

  N8N Workflow
  Configure the Webhook: 
     Set up a webhook to capture meeting completion events and integrate it with Fireflies.

  Retrieve Meeting Content: 
     Use GraphQL API requests to extract meeting details and transcripts, ensuring appropriate authentication through Bearer tokens.

  AI Processing Setup: 
     Define system messages for AI tasks and configure connections to the AI chat model (e.g., OpenAI's GPT) to process transcripts.

  Task Creation Logic: 
     Create structured tasks based on AI output, ensuring necessary details are captured and records are created in Airtable.

  Client Notifications: 
     Use an email node to notify clients about their tasks, ensuring communications are client-specific.

  Scheduling Follow-Up Calls: 
     Set up Google Calendar events if follow-up meetings are required, populating details from the original meeting context.

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name: Check if a Twitch Stream is Live
author_name: Roger Filomeno
author_username: godie
categories: []
created_at: 2024-12-30T11:06:35.454Z
updated_at: 2024-12-30T11:06:35.454Z
url: https://n8n.io/workflows/2684
scraped_at: 2025-12-24 13:02:47
description:
  Introduction:

  This workflow template helps you determine if a Twitch user's stream is currently live or offline.

  Setup Instructions:

  The Document node holds the sample Twitch username you wish to check, you may adapt it in your workflow by replacing this with a chain that contains the Twitch username you want to check. This value is passed to the GraphQL node query as $('Document').item.json.twitch so make sure to change this based on your workflow.

  How it Works:

  The important nodes here are the GrapQL and IF nodes. The GrapQL queries the Twitch API, and then the output returns a document with the stream property. The IF node then checks if this property has a value, if null means the user is offline, otherwise the user is online or live.

  Common Use Cases:
  You can use this with other workflow templates to post live stream alerts to Twitter/X, Bluesky, and Discord via webhooks, etc to notify your community to join youR stream. You may also use an LLM node to write a custom alert based on the value of property title

  How to adjust this template
  If you want to check a list of Twitch channels, you can simply exchange the Document set node in the beginning with your list of channels.


  For more information on the GraphQL output please see the official Twitch API documentation: Get Streams

nodes_used:
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name: TW-Request-Agri Data Open Platform-Daily Market Sheep Pricing
author_name: darrell_tw
author_username: darrelltw
categories: []
created_at: 2025-01-01T19:24:45.689Z
updated_at: 2025-01-01T19:24:45.689Z
url: https://n8n.io/workflows/2687
scraped_at: 2025-12-24 13:02:47
description:
  This workflow automates the process of fetching agricultural transaction data from the Taiwan Agricultural Products Open Data Platform and storing it in a Google Sheets document for further analysis.

  Key Features
  Manual Trigger: Allows manual execution of the workflow to control when data is fetched.
  HTTP Request: Sends a request to the Open Data Platform's API to retrieve detailed transaction data, including:
     Pricing (Upper, Middle, Lower, Average)
     Transaction quantities
     Crop and market details
  Split Out Node: Processes each record individually, ensuring accurate handling of every data entry.
  Google Sheets Integration: Appends the data into a structured Google Sheets document for easy access and analysis.

  Node Configurations

  1. Manual Trigger
  Purpose**: Start the workflow manually.
  Configuration**: No setup needed.

  2. HTTP Request
  Purpose**: Fetch agricultural data.
  Configuration**:
    URL: https://data.moa.gov.tw/api/v1/SheepQuotation
    Query Parameters:
      Start_time: 2024/12/01
      End_time: 2024/12/31
      MarketName: 台北二
      api_key: &lt;your_api_key&gt;
    Headers:
      accept: application/json

  3. Split Out
  Purpose**: Split the API response data array into individual items.
  Configuration**:
    Field to Split Out: Data

  4. Google Sheets
  Purpose**: Append the data to Google Sheets.
  Configuration**:
    Operation: Append
    Document ID: &lt;your_document_id&gt;
    Sheet Name: Sheet1
    Mapped Fields:
      TransDate, TcType, CropCode, CropName, MarketCode, MarketName
      Upper_Price, Middle_Price, Lower_Price, Avg_Price, Trans_Quantity


  此 Workflow 從 台灣農業產品開放資料平臺 獲取農產品交易數據，並將其儲存到 Google Sheets 文件 中進行進一步分析。

  主要功能
  Manual Trigger：允許手動執行工作流程，以控制數據獲取的時間。
  HTTP Request：向開放資料平臺的 API 發送請求，獲取詳細的交易數據，包括：
     價格 (Upper, Middle, Lower, Average)
     交易數量
     作物和市場詳細資料
  Split Out Node：逐筆處理每一筆記錄，確保數據準確無誤。
  Google Sheets Integration：將數據追加到結構化的 Google Sheets 文件中，方便存取和分析。

  節點設定

  1. Manual Trigger
  用途**：手動啟動工作流程。
  設定**：無需額外設定。

  2. HTTP Request
  用途**：抓取農產品數據。
  設定**：
    URL: https://data.moa.gov.tw/api/v1/SheepQuotation
    查詢參數 (Query Parameters)：
      Start_time: 2024/12/01
      End_time: 2024/12/31
      MarketName: 台北二
      api_key: &lt;your_api_key&gt;
    標頭 (Headers)：
      accept: application/json

  3. Split Out
  用途**：將 API 回應的數據陣列分解為個別項目。
  設定**：
    Field to Split Out: Data

  4. Google Sheets
  用途**：將數據追加至 Google Sheets。
  設定**：
    Operation：Append
    Document ID：&lt;your_document_id&gt;
    Sheet Name：Sheet1
    映射欄位 (Mapped Fields)：
      TransDate, TcType, CropCode, CropName, MarketCode, MarketName
      Upper_Price, Middle_Price, Lower_Price, Avg_Price, Trans_Quantity

  請多利用 Curl Import 功能
  例如
  curl -X GET "https://data.moa.gov.tw/api/v1/AgriProductsTransType/?Start_time=114.01.01&End_time=114.01.01&MarketName=%E5%8F%B0%E5%8C%97%E4%BA%8C" -H "accept: application/json"

  農業資料開放平台 文件

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.splitOut","n8n-nodes-base.googleSheets"]

workflow_json_compressed:
  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


name: Hacker News Throwback Machine - See What Was Hot on This Day, Every Year!
author_name: ibrhdotme
author_username: ibrhdotme
categories: []
created_at: 2025-01-01T22:21:01.664Z
updated_at: 2025-01-01T22:21:01.664Z
url: https://n8n.io/workflows/2688
scraped_at: 2025-12-24 13:02:46
description:
  This is a simple workflow that grabs HackerNews front-page headlines from today's date across every year since 2007 and uses a little AI magic (Google Gemini) to sort 'em into themes, sends a neat Markdown summary on Telegram.

  How it works

  Runs daily, grabs Hacker News front page for this day across every year since 2007.
  Pulls headlines & dates.
  Uses Google Gemini to sort headlines into topics & spot trends.
  Sends a Markdown summary to Telegram.

  Set up steps

  Clone the workflow.
  Add your Google Gemini API key.
  Add your Telegram bot token and chat ID.

  **Built on Day-01 as part of the #100DaysOfAgenticAi
  Fork it, tweak it, have fun!**

nodes_used:
  ["@n8n/n8n-nodes-langchain.chainLlm","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.splitOut","n8n-nodes-base.httpRequest","n8n-nodes-base.html","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.merge","n8n-nodes-base.aggregate","n8n-nodes-base.telegram"]

workflow_json_compressed:
  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


name: Reusable Subworkflow Zip Multiple Files Dynamically (Compress)
author_name: Simon
author_username: simonfes
categories: []
created_at: 2025-01-02T13:55:41.676Z
updated_at: 2025-01-02T13:55:41.676Z
url: https://n8n.io/workflows/2690
scraped_at: 2025-12-24 13:02:47
description:
  📦 Zip Multiple Files Dynamically  

  This template enables you to dynamically bundle multiple files into a ZIP archive. Designed to be used as a Subworkflow, it’s modular, flexible, and easy to integrate into various workflows. The output is a single ZIP file with a name that includes the current date, time, and fileName.

  Shoutout:
  Code from: Tom (mutedjam)

  👤 Who is this for?  
  This workflow is perfect for:  
  🚀 Businesses automating file archiving tasks.  
  💻 Developers managing files programmatically.  
  📂 Anyone needing a reusable solution for bundling files into ZIP archives.

  ❓ What problem is this workflow solving?  
  Manually zipping multiple files is:  
  🕒 Time-consuming.  
  🤔 Prone to errors.  

  This workflow automates the process and, as a Subworkflow, ensures:  
  ⚡ Consistent file archiving across different workflows.  
  🛠️ Reduced manual effort.  
  📈 Streamlined integration into existing automation.

  🔧 What this workflow does  
  🗂️ Dynamically collects binary files from the input.  
  📦 Bundles them into a single ZIP archive.  
  🕒 Names the ZIP file with the current date, time, and a customizable fileName.  
  ✅ Outputs the ZIP file, ready for storage or further processing.  

  ⚙️ Setup  
  🔗 Add this Subworkflow to your existing workflows.  
  📥 Pass the binary files as input to the Subworkflow.  
  ▶️ Call the Subworkflow to generate a ZIP file.

  🛠️ How to customize this workflow to your needs  
  🌐 File Sources**: Adjust the input nodes in your parent workflow to connect to your preferred file sources.  
  📝 File Naming**: Customize the logic for the output fileName in the Subworkflow.  
  🚀 Additional Use Cases**: Use this Subworkflow in various scenarios, such as:  
    ✉️ Sending ZIP files via email.  
    ☁️ Uploading ZIP files to cloud storage.  
    🔄 Triggering further automation.

  🎉 Why use this as a Subworkflow?  
  Instead of building a fixed ZIP functionality for every workflow, this template offers a reusable solution that can be integrated into many different workflows effortlessly. Save time and ensure consistency across your automation projects! 💡

nodes_used:
  ["n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.compression","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.code"]

workflow_json_compressed:
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name: Currency Conversion Workflow
author_name: Mauricio Perera
author_username: rckflr
categories: []
created_at: 2025-01-02T18:21:14.460Z
updated_at: 2025-01-02T18:21:14.460Z
url: https://n8n.io/workflows/2691
scraped_at: 2025-12-24 13:02:47
description:
  Purpose: This workflow exemplifies a sophisticated yet pragmatic mechanism for automating currency conversions by leveraging simple HTTP queries routed through a webhook. By intercepting user requests, sourcing real-time exchange rate data via Google search results, and formatting the data into actionable responses, it obviates the reliance on third-party APIs. This efficiency renders it an indispensable instrument for diverse applications, including dynamic pricing strategies for AI-driven systems, financial data automation, and real-time currency computation within complex workflows. The workflow's architectural simplicity ensures seamless integration across professional and academic domains, optimizing both scalability and reliability.

  Workflow Steps:
  Capture Conversion Query:
     The workflow initiates by intercepting user input delivered as a GET request through a configured webhook. Inputs should adhere to a structured syntax, such as 5usd to mxn, to ensure flawless processing.
     Testing Tip: Use tools like Postman or a browser to test GET requests and verify that the Webhook receives inputs correctly.
  Fetch Exchange Rate:
     Utilizing the HTTP Request node, a Google search query is executed to retrieve current exchange rate data. This method ensures the workflow remains economical and adaptable while circumventing API dependencies.
  Extract Conversion Data:
     By processing the returned HTML from Google's search results, this node extracts precise exchange rate figures and contextual information critical for accurate conversions.
     Error Handling: If extraction fails, verify that the input format is correct and update CSS selectors to reflect any changes in Google's HTML structure.
  Format Currency Response:
     The extracted data undergoes refinement and is formatted into a structured, user-friendly string that conveys the conversion results with clarity and precision.
  Send Conversion Response:
     The workflow culminates by dispatching the formatted response back to the request origin, completing the loop with efficiency and reliability.

  Required Configuration:
  Configure the Webhook node to accommodate GET requests. The query parameters should follow the format: https://your-webhook-url/currency-converter?q=5usd+to+mxn.
  Inputs must adhere strictly to the predefined syntax (e.g., 5usd to mxn). Deviations may induce processing errors or yield erroneous outputs.

  Customization Options:
  The Extract Conversion Data node’s CSS selectors can be fine-tuned to align with modifications in Google’s HTML structure, ensuring long-term operability.
  Adjustments to the Format Currency Response node enable bespoke output formatting, incorporating additional metadata or altering the response structure to meet specific project requisites.

  Advanced Features:
  This workflow’s modular design supports seamless integration into expansive systems. For instance, an e-commerce platform could employ it to dynamically localize product pricing based on user geolocation.
  Enhanced functionality can be achieved by appending nodes to log conversions, monitor performance metrics, or trigger auxiliary workflows conditioned on conversion outputs.

  Expected Results:
  For a query like 5usd to mxn, the workflow generates a response formatted as: 5 USD = 95 MXN. This output is optimized for readability and practical application.

  Use Case Examples:
  AI Integration:** Enables artificial intelligence agents to process real-time price conversions efficiently across diverse currencies, enhancing their computational capabilities.
  Financial Operations:** Automates precise currency conversions for corporate reports, international transactions, and market analytics.
  Personal Financial Planning:** Assists users in calculating currency conversions for investment decisions or travel budgeting with minimal manual effort.
  E-commerce Applications:** Facilitates dynamic price adjustments on online marketplaces, displaying localized prices to augment user experience and conversion rates.
  Workflow Integration:** Embeds seamlessly into larger systems, such as CRMs or ERPs, to streamline financial operations and enhance interoperability.

  Key Benefits:
  No API Dependency:** By leveraging publicly available data from Google, the workflow eliminates the complexities and costs associated with API integration, reducing overhead and enhancing accessibility.
  Precision and Currency:** Ensures accurate and real-time results by querying Google directly.
  Flexibility:** Designed to adapt to various operational contexts and input formats, making it a versatile asset in computational and commercial applications.

  Tags:
  currency conversion, automation, webhook, data extraction, AI integration, financial automation, e-commerce, real-time data, scalable workflows.

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.html","n8n-nodes-base.set","n8n-nodes-base.webhook","n8n-nodes-base.respondToWebhook","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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name: Integrating AI with Open-Meteo API for Enhanced Weather Forecasting
author_name: Davi Saranszky Mesquita
author_username: frkr
categories: []
created_at: 2025-01-02T19:36:50.723Z
updated_at: 2025-01-02T19:36:50.723Z
url: https://n8n.io/workflows/2692
scraped_at: 2025-12-24 13:02:46
description:
  Use case

  Workshop

  We are using this workflow in our workshops to teach how to use Tools a.k.a functions with artificial intelligence. In this specific case, we will use a generic "AI Agent" node to illustrate that it could use other models from different data providers.

  Enhanced Weather Forecasting

  In this small example, it's easy to demonstrate how to obtain weather forecast results from the Open-Meteo site to accurately display the upcoming days.

  This can be used to plan travel decisions, for example.

  What this workflow does

  We will make an HTTP request to find out the geographic coordinates of a city.
  Then, we will make other HTTP requests to discover the weather for the upcoming days.

  In this workshop, we demonstrate that the AI will be able to determine which tool to call first—it will first call the geolocation tool and then the weather forecast tool. All of this within a single client conversation call.

  Setup

  Insert an OpenAI Key and activate the workflow.

  by Davi Saranszky Mesquita
  https://www.linkedin.com/in/mesquitadavi/

nodes_used:
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name: Make OpenAI Citation for File Retrieval RAG
author_name: Davi Saranszky Mesquita
author_username: frkr
categories: []
created_at: 2025-01-02T20:17:33.155Z
updated_at: 2025-01-02T20:17:33.155Z
url: https://n8n.io/workflows/2693
scraped_at: 2025-12-24 13:02:46
description:
  Make OpenAI Citation for File Retrieval RAG

  Use case

  In this example, we will ensure that all texts from the OpenAI assistant search for citations and sources in the vector store files. We can also format the output for Markdown or HTML tags.

  This is necessary because the assistant sometimes generates strange characters, and we can also use dynamic references such as citations 1, 2, 3, for example.

  What this workflow does

  In this workflow, we will use an OpenAI assistant created within their interface, equipped with a vector store containing some files for file retrieval.

  The assistant will perform the file search within the OpenAI infrastructure and will return the content with citations.

  We will make an HTTP request to retrieve all the details we need to format the text output.

  Setup

  Insert an OpenAI Key

  How to adjust it to your needs

  At the end of the workflow, we have a block of code that will format the output, and there we can add Markdown tags to create links. Optionally, we can transform the Markdown formatting into HTML.

nodes_used:
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name: SSL Expiry Alert with SSL-Checker.io
author_name: Vishal Kumar
author_username: vishalquantana
categories: []
created_at: 2025-01-03T12:28:32.839Z
updated_at: 2025-01-03T12:28:32.839Z
url: https://n8n.io/workflows/2694
scraped_at: 2025-12-24 13:02:44
description:
  Use Case

  Managing SSL certificates manually can be time-consuming and error-prone, often leading to unexpected downtime or security risks due to expired certificates.

  What This Workflow Does

  This workflow automatically monitors SSL certificates for a list of websites, checks their expiry status using SSL-Checker.io, and sends timely notifications if a certificate is about to expire.

  Setup
  Add your credentials for Google Sheets, Gmail, and SSL-Checker.io.

  Create a Google Sheet with a list of URLs for the websites you want to monitor.

  Configure the workflow to check the SSL status weekly.

  Set up email notifications to alert you when a certificate is close to expiry.

  Activate the workflow to automate monitoring and notification.

  How to Adjust It to Your Needs

  Customize the URL Source: Replace Google Sheets with another data source like Airtable or CSV files.
  Modify Notification Thresholds: Change the expiry threshold (e.g., notify for 14 days instead of 7).
  Add Additional Actions: Integrate with tools like Slack or Teams for team-wide notifications.
  Automate Renewal Requests: Add a step to send renewal requests directly to your SSL provider if a certificate is nearing expiry.

nodes_used:
  ["n8n-nodes-base.googleSheets","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.googleSheets","n8n-nodes-base.httpRequest","n8n-nodes-base.if","n8n-nodes-base.gmail","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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name: Generating New Keywords and their Search Volumes using the Google Ads API
author_name: Zacharia Kimotho
author_username: imperolq
categories: []
created_at: 2025-01-03T14:59:40.018Z
updated_at: 2025-01-03T14:59:40.018Z
url: https://n8n.io/workflows/2695
scraped_at: 2025-12-24 13:02:44
description:
  Generate new keywords for SEO with the monthly Search volumes

  This workflow is an improvement on the workflows below. It can be used to generate new keywords that you can use for your SEO campaigns or Google ads campaigns


  Generate SEO Keyword Search Volume Data using Google API and Generating Keywords using Google Autosuggest

  Usage
  Send the keywords you need as an array to this workflow
  Pin the data and map it to the set Keywords  node
  Map the keywords to the Google ads API with the location and Language of your choice
  Split the results and set them data 
  Pass this to the next nodes as needed for storage
  Make a copy of this spreedsheet and update the data accordingly

  Having challenges with the google Ads API? Read this blog 

  Setup

  Replace the trigger with your desired trigger eg a webhook or manual trigger

  Map the data correctly to the set Keywords node
  On the Generate new keywords, Update the {customer_id} on the url and login-customer-id with your actual one. Update the developer-token` also with your values. 

  The url should be corrected as below https://googleads.googleapis.com/v18/customers/{customer-id}:generateKeywordIdeas

  You should send the headers as below



              {
                "name": "content-type",
                "value": "application/json"
              },
              {
                "name": "developer-token",
                "value": "5j-tyzivCNmiCcoW-xkaxw"
              },
              {
                "name": "login-customer-id",
                "value": "513554 "
              }
           
      



  and the json body should take the following format 


  {
    "geoTargetConstants": ["geoTargetConstants/2840"], 
    "includeAdultKeywords": false,
    "pageToken": "",
    "pageSize": 2,
    "keywordPlanNetwork": "GOOGLE_SEARCH",
    "language": "languageConstants/1000", 
    "keywordSeed": {
      "keywords": {{ $json.Keyword }}
    }
  }

  Troubleshooting

  If you get an error with the workflow, check the credentials you are using

  Check the account you are using eg the right customer id and developer token

  Follow the guide on the blog to set up your Google ads account 


  Made by @Imperol

nodes_used:
  ["n8n-nodes-base.splitOut","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.googleSheets","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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name: Complete Guide to Setting Up and Generating TOTP Codes in n8n 🔐
author_name: AmirHossein MnasouriZade
author_username: amir676080
nodes_used: ["n8n-nodes-base.manualTrigger","n8n-nodes-base.totp"]
categories: []
created_at: 2025-01-04T21:19:45.650Z
updated_at: 2025-01-04T21:19:45.650Z
url: https://n8n.io/workflows/2696
scraped_at: 2025-12-24 13:02:47
description:
  Setting Up and Generating TOTP

  Step 1: Receive QR Code and Extract the Link

  1. Receive the QR Code from the 2FA service
  After enabling two-factor authentication (2FA) on services like OpenAI, Google, GitHub, etc., a QR Code will be given to you, which you need to scan. This QR Code contains the TOTP link used to generate one-time passcodes.

  2. Extract the link from the received QR Code
  To extract the link from the QR Code, use online tools. These tools will help you extract the corresponding link.

  After using an online tool, the extracted link will appear in the following format:

  otpauth://totp/ServiceName:username?secret=secret_key&issuer=ServiceName
  For example:
  otpauth://totp/OpenAI:amir676080@gmail.com?secret=test-test-test&issuer=OpenAI

  Step 2: Create TOTP Credential in n8n

  Create a new Credential
  To use TOTP in n8n, you need to create a new TOTP Credential.

  Enter the details in the Credential
  In the Secret field:* Enter the *secret key** (extracted from the QR Code link). 
  For example:
   test-test-test

  In the Label field:* Enter *ServiceName:username** 
  For example:
  OpenAI:amir676080@gmail.com




  Save the Credential
  After entering the information, click Save to save the Credential.

  Step 3: Get the TOTP Code
  Click on Test Workflow
  After setting up the credentials in n8n, click on Test, and the corresponding code will be delivered to you.

  Output:
  [
    {
      "token": "720769",
      "secondsRemaining": 18
    }
  ]



  ==This code is exactly the same as the one generated by apps and services like Google Authenticator or Authy. 🔐==


  Contact me on [Telegram]: https://t.me/amir676080

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name: Learn Anything from HN - Get Top Resource Recommendations from Hacker News
author_name: ibrhdotme
author_username: ibrhdotme
categories: []
created_at: 2025-01-05T01:31:45.526Z
updated_at: 2025-01-05T01:31:45.526Z
url: https://n8n.io/workflows/2697
scraped_at: 2025-12-24 13:02:46
description:
  Learning something new? Endlessly searching to find the best resources? This workflow finds top community-recommended learning resources on any topic from Hacker News, delivered to your inbox.

  How it works
  User submits a topic they want to learn via a simple form.
  The workflow searches for relevant "Ask HN" posts on Hacker News and extracts top-level comments.
  An LLM analyzes the comments and identifies the best learning resources.
  A personalized email is sent to the user with a Markdown formatted list of top recommendations, categorized by resource type (e.g., book, course, article) and difficulty level.

  Set up steps

  Add your Google Gemini API credentials. You'll need to create a project and enable the Generative Language API.
  Add your SMTP credentials for sending emails.
  Customize the Form and email subject (optional)
  Activate the workflow

  Screenshots for Workflow, Form and Email



  Built on Day-03 as part of the #100DaysOfAgenticAi
  Fork it, tweak it, have fun!

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.hackerNews","n8n-nodes-base.httpRequest","n8n-nodes-base.aggregate","n8n-nodes-base.splitOut","n8n-nodes-base.formTrigger","n8n-nodes-base.emailSend","n8n-nodes-base.markdown","n8n-nodes-base.noOp"]

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name: Scrape ProductHunt using Google Gemini
author_name: Mauricio Perera
author_username: rckflr
categories: []
created_at: 2025-01-05T05:47:39.642Z
updated_at: 2025-01-05T05:47:39.642Z
url: https://n8n.io/workflows/2698
scraped_at: 2025-12-24 13:02:47
description:
  Workflow Description: Product Data Extractor

  This workflow automates the extraction of product data from Product Hunt by combining webhook interactions, HTML processing, AI-based data analysis, and structured output formatting. It is designed to handle incoming requests dynamically and return detailed JSON responses for further usage.

  Overview

  The workflow processes a product name submitted through a webhook. It fetches the corresponding Product Hunt page, extracts and analyzes inline scripts, and structures the data into a well-defined JSON format using AI tools. The final JSON response is returned to the client through the webhook.

  Workflow Steps

  1. Webhook Listener

  Node:** Receive Product Request
  Function:** Captures incoming requests containing the product name to process.
  Details:** Accepts HTTP requests and extracts the product parameter from the query string, such as &lt;custom_webhook_url&gt;/?product=epigram.

  2. Fetch Product HTML

  Node:** Fetch Product HTML
  Function:** Sends an HTTP request to retrieve the HTML content of the specified Product Hunt page.
  Details:** Constructs a dynamic URL using the product name and fetches the page data.

  3. Extract Inline Scripts

  Node:** Extract Inline Scripts
  Function:** Parses the HTML content to extract inline scripts located within the &lt;head&gt; section.
  Details:** Excludes scripts containing src attributes and validates the presence of inline scripts.

  4. Process Data with LLM

  Node:** Process Script with LLM
  Function:** Analyzes the extracted scripts using a language model to identify key product data.
  Details:** Processes the script to derive structured and meaningful insights.

  5. Refine Data with Google Gemini

  Node:** Analyze Script with Google Gemini
  Function:** Leverages Google Gemini AI for enhanced analysis of script data.
  Details:** Ensures the extracted data is precise and enriched.

  6. Format Product Data to JSON

  Node:** Format Product Data to JSON
  Function:** Structures the processed data into a clean JSON format.
  Details:** Defines a schema to ensure all relevant fields are included in the output.

  7. Send JSON Response to Client

  Node:** Send JSON Response to Client
  Function:** Returns the final structured JSON response to the client.
  Details:** Sends the response back via the same webhook that initiated the request. For example, &lt;custom_webhook_url&gt;.

  Key Features

  Versatile Use Cases:** This workflow can be used to gather Product Hunt data for creating blog posts or as a tool for AI agents to research products efficiently.
  Dynamic Processing:** Adapts to various product names through dynamic URL construction.
  AI Integration:** Utilizes the Gemini 1.5 8B AI model, offering reduced latency and minimal or no cost depending on the use case.
  Selector Independence:** Functions even if Product Hunt's DOM structure changes, as it does not rely on direct DOM selectors.
  Reliable Data Output:** A low temperature setting (0) and a precisely defined JSON schema ensure accurate and real data extraction.
  Dynamic Processing:** Adapts to various product names through dynamic URL construction.
  AI Integration:** Utilizes advanced language models for data extraction and refinement.
  Structured Output:** Ensures the output JSON adheres to a predefined schema for consistency.
  Error Handling:** Includes validations to handle missing or malformed data gracefully.

  Customization Options

  Limitations
  Dependency on Product Hunt:** Significant changes to the way Product Hunt loads data on its pages might require modifications to the workflow.
  Adaptability:** Even if changes occur, the workflow can be updated to maintain functionality due to its reliance on AI and not direct DOM selectors.


  Modify the webhook path to suit your application.
  Adjust the prompt for the language model to include additional fields.
  Extend the JSON schema to capture more data fields as needed.

  Expected Output

  Performance Metrics
  Response Time:** Typically ~6 seconds per product.
  Accuracy:** Data extracted with &gt;95% precision due to the pre-defined JSON schema.


  A JSON object containing detailed information about the specified product. Below is an example of a complete response for the product Epigram:

  {
    "id": "861675",
    "slug": "epigram",
    "followersCount": 181,
    "name": "Epigram",
    "tagline": "Open-Source, Free, and AI-Powered News in Short",
    "reviewsRating": 0,
    "logoUuid": "735c2528-554c-467c-9dcf-745ee4b8bbdd.png",
    "postsCount": 1,
    "websiteUrl": "https://epigram.news",
    "websiteDomain": "epigram.news",
    "metaTitle": "Epigram - Open-source, free, and ai-powered news in short",
    "postName": "Epigram",
    "postTagline": "Open-source, free, and ai-powered news in short",
    "dailyRank": "3",
    "description": "An open-source, AI-powered news app for busy people. Stay updated with bite-sized news, real-time updates, and in-depth analysis. Experience balanced, trustworthy reporting tailored for fast-paced lifestyles in a sleek, user-friendly interface.",
    "pricingType": "free",
    "userName": "Fazle Rahman",
    "userHeadline": "Co-founder & CEO, Hashnode",
    "userUsername": "fazlerocks",
    "userAvatarUrl": "https://ph-avatars.imgix.net/129147/f84e1796-548b-4d6f-9dcf-745ee4b8bbdd.jpeg",
    "makerName1": "Fazle Rahman",
    "makerHeadline1": "Co-founder & CEO, Hashnode",
    "makerUsername1": "fazlerocks",
    "makerAvatarUrl1": "https://ph-avatars.imgix.net/129147/f84e1796-548b-4d6f-9dcf-745ee4b8bbdd.jpeg",
    "makerName2": "Sandeep Panda",
    "makerHeadline2": "Co-Founder @ Hashnode",
    "makerUsername2": "sandeepg33k",
    "makerAvatarUrl2": "https://ph-avatars.imgix.net/101872/80b0b618-a540-4110-a6d1-74df39675ad0.jpeg",
    "primaryLinkUrl": "https://epigram.news/",
    "media1OriginalHeight": 1080,
    "media1OriginalWidth": 1440,
    "media1ImageUuid": "ac426fd1-3854-4734-b43d-34a5e06347ea.gif",
    "media1MediaType": "video",
    "media1MetadataUrl": "https://www.loom.com/share/b1a48a9b3cac4ba89ce772a3fbcc2847?sid=75efc771-25fa-4ac0-bb1b-5e38fc447deb",
    "media1VideoId": "b1a48a9b3cac4ba89ce772a3fbcc2847",
    "media2OriginalHeight": 630,
    "media2OriginalWidth": 1200,
    "media2ImageUuid": "8521a6bd-7640-487b-abd6-29b9f65fee32",
    "media2MediaType": "image",
    "media2MetadataUrl": null,
    "launchState": "featured",
    "thumbnailImageUuid": "735c2528-554c-467c-9dcf-745ee4b8bbdd.png",
    "link1StoreName": "Website",
    "link1WebsiteName": "epigram.news",
    "link2StoreName": "Github",
    "link2WebsiteName": "github.com",
    "latestScore": 233,
    "launchDayScore": 233,
    "userId": "129147",
    "topic1": "News",
    "topic2": "Open Source",
    "topic3": "Artificial Intelligence",
    "weeklyRank": "24",
    "commentsCount": 20,
    "postUrl": "https://www.producthunt.com/posts/epigram"
  }

  Target Audience

  This workflow is ideal for developers, marketers, and data analysts seeking to automate the extraction and structuring of product data from Product Hunt for analytics, reporting, or integration with other tools.

nodes_used:
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name: Obsidian Notes Read Aloud using AI: Available as a Podcast Feed
author_name: Obsidi8n
author_username: derruttie
categories: []
created_at: 2025-01-05T20:13:09.649Z
updated_at: 2025-01-05T20:13:09.649Z
url: https://n8n.io/workflows/2699
scraped_at: 2025-12-24 13:02:46
description:
  How it works:

  Send notes from Obsidian via Webhook to start the audio conversion
  OpenAI converts your text to natural-sounding audio and generates episode descriptions
  Audio files are stored in Cloudinary and automatically attached to your notes in Obsidian
  A professional podcast feed is generated, compatible with all major podcast platforms (Apple, Spotify, Google)

  Set up steps:

  Install and configure the Post Webhook Plugin in Obsidian
  Set up Custom Auth credentials in n8n for Cloudinary using the following JSON:

  {
    "name": "Cloudinary API",
    "type": "httpHeaderAuth",
    "authParameter": {
      "type": "header",
      "key": "Authorization",
      "value": "Basic {{Buffer.from('your_api_key:your_api_secret').toString('base64')}}"
    }
  }

  Configure podcast feed metadata (title, author, cover image, etc.)

  Note: The second flow is a generic Podcast Feed module that can be reused in any '[...]-to-Podcast' workflow. It generates a standard RSS feed from Google Sheets data and podcast metadata, making it compatible with all major podcast platforms.

nodes_used:
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name: AI Agent to chat with Airtable and analyze data
author_name: Mark Shcherbakov
author_username: lowcodingdev
categories: []
created_at: 2025-01-06T19:57:13.408Z
updated_at: 2025-01-06T19:57:13.408Z
url: https://n8n.io/workflows/2700
scraped_at: 2025-12-24 13:02:44
description:
  Video Guide

  I prepared a detailed guide that shows the entire process of building an AI agent that integrates with Airtable data in n8n. This template covers everything from data preparation to advanced configurations.



  Youtube Link

  Who is this for?
  This workflow is designed for developers, data analysts, and business owners who want to create an AI-powered conversational agent integrated with Airtable datasets. It is particularly useful for users looking to enhance data interaction through chat interfaces.

  What problem does this workflow solve?
  Engaging with data stored in Airtable often requires manual navigation and time-consuming searches. This workflow allows users to interact conversationally with their datasets, retrieving essential information quickly while minimizing the need for complex queries.

  What this workflow does
  This workflow enables an AI agent to facilitate chat interactions over Airtable data. The agent can:
  Retrieve order records, product details, and other relevant data.
  Execute mathematical functions to analyze data such as calculating averages and totals.
  Optionally generate maps for geographic data visualization.

  Dynamic Data Retrieval: The agent uses user prompts to dynamically query the dataset.
  Memory Management: It retains context during conversations, allowing users to engage in a more natural dialogue.
  Search and Filter Capabilities: Users can perform tailored searches with specific parameters or filters to refine their results.

  Set up steps

  Separate workflows:
  	Create additional workflow and move there Workflow 2.

  Replace credentials:
  	Replace connections and credentials in all nodes.

  Start chat:
  	Ask questions and don't forget to mention required base name.

nodes_used:
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name: Reschedule overdue Asana tasks and clean up completed tasks
author_name: Bastien Laval
author_username: bastien-laval
categories: []
created_at: 2025-01-06T20:52:57.831Z
updated_at: 2025-01-06T20:52:57.831Z
url: https://n8n.io/workflows/2701
scraped_at: 2025-12-24 13:02:46
description:
  Description

  Boost your productivity and keep your Asana workspace clutter-free with this n8n workflow. 

  It automatically scans for tasks whose due dates have passed and reschedules them to the current date, ensuring no important to-dos slip through the cracks. 

  Additionally, any completed tasks in Asana with an overdue date are removed, maintaining a clear, organized task list.

  Key Benefits

  Streamline Task Management**: No more manual updates—let the workflow reschedule overdue tasks for you.
  Optimize Workspace Organization**: Eliminate finished tasks to focus on active priorities and reduce clutter.
  Save Time and Effort**: Automate repetitive maintenance, freeing you to concentrate on what truly matters.

  Configuration Steps

  Add your Asana credentials
  Schedule the workflow to run at desired intervals (e.g., daily or weekly).
  Select your Workspace Name and your Assignee Name (user) in the Get user tasks node
  (Optional) Tailor filtering conditions to match your preferred due-date rules and removal criteria.
  Activate the workflow and watch your Asana workspace stay up to date and clutter-free. 

nodes_used:
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name: Save Hotmart events to Google Sheets
author_name: Solomon
author_username: solomon
categories: []
created_at: 2025-01-06T23:55:50.691Z
updated_at: 2025-01-06T23:55:50.691Z
url: https://n8n.io/workflows/2702
scraped_at: 2025-12-24 13:02:47
description:
  Português

  Acompanhe todos os seus eventos do Hotmart em um só lugar e mantenha seus dados organizados para análise.

  Com este fluxo você pode registrar compras, reembolsos, eventos de assinatura e abandono de carrinho diretamente no Google Sheets.

  Como funciona

  O fluxo recebe os eventos da Hotmart e registra automaticamente no Google Sheets.

  Por exemplo:

  Compras são registradas com informações como nome do produto, valor pago e dados do comprador.
  Abandono de carrinho registra informações sobre o produto visualizado, dados de contato do cliente e horários.
  Eventos de assinatura mostram atualizações como cancelamentos ou renovações.

  Para quem é?

  Criadores, empreendedores e negócios que usam o Hotmart e precisam de uma forma clara e automatizada de acompanhar suas vendas e dados de assinatura.

  Remova a necessidade de transferir dados manualmente – este fluxo de trabalho faz isso por você, economizando tempo e reduzindo erros.

  Confira meus outros templates

  👉 https://n8n.io/creators/solomon/

  English

  Track all your Hotmart events in one place and keep your data organized for analysis.

  With this workflow, you can register purchases, refunds, subscription events, and cart abandonment directly in Google Sheets.

  How it works

  The workflow listens for events from Hotmart and automatically records the details in Google Sheets.

  For example:

  Purchases are logged with details like product name, amount paid, and buyer information.
  Cart abandonment records include the product viewed, customer contact details, and timestamps.
  Subscription events show updates like cancellations or renewals.

  Who is this for?

  Creators, entrepreneurs, and businesses using Hotmart who need a clear and automated way to track their sales and subscription data.

  No need to manually transfer data – this workflow does it for you, saving time and reducing errors.

  Check out my other templates

  👉 https://n8n.io/creators/solomon/

nodes_used:
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name: AI Agent : Google calendar assistant using OpenAI
author_name: Dataki
author_username: dataki
categories: []
created_at: 2025-01-07T15:09:52.723Z
updated_at: 2025-01-07T15:09:52.723Z
url: https://n8n.io/workflows/2703
scraped_at: 2025-12-24 13:02:42
description:
  This template is a simple AI Agent that acts as a Google Calendar Assistant. 
  It is designed for beginners to have their "first AI Agent" performing common tasks and to help them understand how it works.

  For new users of n8n, AI Agents, and OpenAI:
  This template involves using an OpenAI API Key. If you are new to AI Agents, make sure to research and understand key concepts such as:
  "Tokens"** (used for API requests),
  "Tool calling"** (how the AI interacts with external tools),
  OpenAI's usage costs** (how you will be billed for API usage).

  Functionality

  It has two main functionalities:
  Create events** in a calendar
  Retrieve events** from a calendar

  How you can use it
  Everything is explained with sticky notes in the workflow. 
  It is ready-to-use: all you need to do is connect your OpenAI credentials, and you can start using the workflow.

nodes_used:
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name: ServiceNow Incident Notifications to Slack Workflow
author_name: Angel Menendez
author_username: djangelic
categories: []
created_at: 2025-01-07T17:36:49.670Z
updated_at: 2025-01-07T17:36:49.670Z
url: https://n8n.io/workflows/2704
scraped_at: 2025-12-24 13:02:46
description:
  Who is this for?

  This workflow is ideal for IT operations teams or system administrators who use ServiceNow to track incidents and Slack for team communication. It provides real-time updates on new ServiceNow incidents directly in a designated Slack channel, ensuring timely response and collaboration.

  What problem is this workflow solving? / Use case

  Manually monitoring ServiceNow for new incidents can be time-consuming and prone to delays. This workflow automates the process, ensuring that team members are instantly notified of new incidents, complete with all relevant details, in a Slack channel. It enhances operational efficiency and incident response time.

  What this workflow does

  Schedule or Manual Trigger: The workflow can be triggered manually or set to run automatically every 5 minutes.
  Retrieve New Incidents: Fetches incidents created in ServiceNow within the last 5 minutes.
  Error Handling: Posts an error message in Slack if there are issues connecting to ServiceNow.
  Incident Processing:
      If new incidents are found, they are sorted in ascending order by their number.
      Detailed incident information is formatted and sent to a specified Slack channel.
  No Incidents: If no new incidents are found, the workflow does nothing.

  Setup

  ServiceNow API Credentials: Configure ServiceNow Basic Authentication in the workflow to connect to your ServiceNow instance.
  Slack API Credentials: Add your Slack API credentials to enable message posting.
  Slack Channel Configuration:
      Define the Slack channel where notifications should be sent.
      Ensure the channel ID is correctly set in the Slack node.
  Adjust the Schedule: Modify the schedule in the Schedule Trigger node to suit your requirements.

  How to customize this workflow to your needs

  Notification Format:
      Customize the Slack message format to include additional or fewer details.
      Update the Blocks section in the Slack node for personalized messages.
  Incident Query Parameters:
      Adjust the sysparm_query parameter in the ServiceNow node to filter incidents based on specific criteria.
  Error Handling:
      Modify the error message in the Slack node for more detailed troubleshooting information.

  Features

  Real-Time Notifications**: Immediate updates on new ServiceNow incidents.
  Error Handling**: Alerts in Slack if the workflow encounters issues connecting to ServiceNow.
  Customizable Notifications**: Flexibility to modify incident details sent to Slack.

  This workflow streamlines incident management and fosters collaboration by delivering actionable updates directly to your team.

nodes_used:
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name: Chat with GitHub API Documentation: RAG-Powered Chatbot with Pinecone & OpenAI
author_name: Mihai Farcas
author_username: mihailtd
categories: []
created_at: 2025-01-07T20:05:27.294Z
updated_at: 2025-01-07T20:05:27.294Z
url: https://n8n.io/workflows/2705
scraped_at: 2025-12-24 13:02:43
description:
  This workflow demonstrates a Retrieval Augmented Generation (RAG) chatbot that lets you chat with the GitHub API Specification (documentation) using natural language. Built with n8n, OpenAI's LLMs and the Pinecone vector database, it provides accurate and context-aware responses to your questions about how to use the GitHub API.
  You could adapt this to any OpenAPI specification for any public or private API, thus creating a documentation chatbout that anyone in your company can use.

  How it works:

  Data Ingestion: The workflow fetches the complete GitHub API OpenAPI 3 specification directly from the GitHub repository.
  Chunking and Embeddings: It splits the large API spec into smaller, manageable chunks. OpenAI's embedding models then generate vector embeddings for each chunk, capturing their semantic meaning.
  Vector Database Storage: These embeddings, along with the corresponding text chunks, are stored in a Pinecone vector database.
  Chat Interface and Query Processing: The workflow provides a simple chat interface. When you ask a question, it generates an embedding for your query using the same OpenAI model.
  Semantic Search and Retrieval: Pinecone is queried to find the most relevant text chunks from the API spec based on the query embedding.
  Response Generation: The retrieved chunks and your original question are fed to OpenAI's gpt-4o-mini LLM, which generates a concise, informative, and contextually relevant answer, including code snippets when applicable.

  Set up steps:

  Create accounts: You'll need accounts with OpenAI and Pinecone.
  API keys: Obtain API keys for both services.
  Configure credentials: In your n8n environment, configure credentials for OpenAI and Pinecone using your API keys.
  Import the workflow: Import this workflow into your n8n instance.
  Pinecone Index: Ensure you have a Pinecone index named "n8n-demo" or adjust the workflow accordingly. The workflow is set up to work with this index out of the box.

  Setup Time: Approximately 15-20 minutes.

  Why use this workflow?

  Learn RAG in Action: This is a practical, hands-on example of how to build a RAG-powered chatbot.
  Adaptable Template: Easily modify this workflow to create chatbots for other APIs or knowledge bases.
  n8n Made Easy: See how n8n simplifies complex integrations between data sources, vector databases, and LLMs.

nodes_used:
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name: Auto-Categorize blog posts in wordpress using A.I.
author_name: Keith Rumjahn
author_username: rumjahn
categories: []
created_at: 2025-01-08T06:30:40.394Z
updated_at: 2025-01-08T06:30:40.394Z
url: https://n8n.io/workflows/2706
scraped_at: 2025-12-24 13:02:46
description:
  WordPress Post Auto-Categorization Workflow



  📺 Click here to watch youtube tutorial 

  🎯 Purpose
  Automatically categorize WordPress blog posts using AI, saving hours of manual work. This workflow analyzes your post titles and assigns them to predefined categories using artificial intelligence.

  🔄 What This Workflow Does
  • Connects to your WordPress site
  • Retrieves all uncategorized posts
  • Uses AI to analyze post titles
  • Automatically assigns appropriate category IDs
  • Updates posts with new categories
  • Processes dozens of posts in minutes

  ⚙️ Setup Requirements
  WordPress site with admin access
  Predefined categories in WordPress
  OpenAI API credentials (or your preferred AI provider)
  n8n with WordPress credentials

  🛠️ Configuration Steps
  Add your WordPress categories (manually in WordPress)
  Note down category IDs
  Update the AI prompt with your category IDs
  Configure WordPress credentials in n8n
  Set up AI API connection

  🔧 Customization Options
  • Modify AI prompts for different categorization criteria
  • Adjust for multiple category assignments
  • Add tag generation functionality
  • Customize for different content types
  • Add additional metadata updates

  ⚠️ Important Notes
  • Backup your WordPress database before running
  • Test with a few posts first
  • Review AI categorization results initially
  • Categories must be created manually first

  🎁 Bonus Features
  • Can be modified for tag generation
  • Works with scheduled posts
  • Handles bulk processing
  • Maintains categorization consistency

  Perfect for content managers, bloggers, and website administrators looking to organize their WordPress content efficiently.

  #n8n #WordPress #ContentManagement #Automation #AI

  Created by rumjahn

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.wordpress","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.wordpress","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.agent"]

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name: Food Delivery Notifications and Easy Expense Tracking
author_name: darrell_tw
author_username: darrelltw
categories: []
created_at: 2025-01-08T07:41:52.038Z
updated_at: 2025-01-08T07:41:52.038Z
url: https://n8n.io/workflows/2707
scraped_at: 2025-12-24 13:02:46
description:
  Workflow Description
  This workflow automates the process of retrieving emails from a food delivery platform, extracting key order details, and sending notifications to a Slack channel. Additionally, the Slack message includes a Moze accounting app URL scheme link for quick expense tracking.

  Key Features
  Manual Trigger: Allows the workflow to be executed manually for immediate testing.
  Gmail Integration: Retrieves emails containing specific keywords in the subject line (e.g., "透過 Uber Eats 系統送出的訂單"). (You can adjust the keywords to fit your language.)
  Data Extraction: Parses the email content to extract key details such as:
     Order price
     Shop name
     Order date and time
  Slack Notification: Sends a notification to a specified Slack channel using a structured block format, including a link to record the expense in the Moze accounting app.

  Node Configurations

  1. Manual Trigger
  Purpose**: Starts the workflow manually for testing or immediate execution.
  Configuration**: No setup needed.

  2. Gmail Trigger
  Purpose**: Automatically polls Gmail for new emails matching specific subject keywords.
  Configuration**:
    Filters:
      q: subject:透過 Uber Eats 系統送出的訂單 (You can adjust the keywords to fit your language.)
    Polling Frequency: Every hour at 30 minutes past the hour.
    Credentials: Linked Gmail account.

  3. Extract Price, Shop, Date, Time
  Purpose**: Extracts key information from the email content using regular expressions.
  Extracted Data**:
    price: Order price (e.g., $200).
    shop: Shop name (e.g., "店名").
    date: Order date (e.g., 2024.01.01).
    time: Order time converted to 24-hour format (e.g., 14:30).

  4. Slack Notification
  Purpose**: Sends a formatted message to a Slack channel with extracted order details.
  Message Content**:
    Text:
          Ubereat 訂餐資訊:
      商家: {{ shop }}
      金額: {{ price }}
      日期: {{ date }}
        Moze App Link: Includes a clickable button in the Slack message with a pre-filled Moze app URL scheme:
          moze3://expense?amount={{ price }}&account=信用卡&subcategory=外送&store={{ shop }}&date={{ date }}&time={{ time }}&project=生活開銷
        Channel: Slack channel ID associated with food delivery notifications.

  Additional Notes
  Customization**: Adjust the email subject filter (subject) to match other types of food delivery platforms or services.
  Error Handling**: Ensure regular expressions for data extraction match the email format. Test with sample emails before deployment.
  Moze URL Scheme Reference**: Learn more about Moze app URL schemes for customization by visiting the Moze Documentation.

  This workflow is ideal for automating expense tracking and centralizing notifications for food delivery orders, streamlining personal or team expense management.

  Image:
  UberEat Gmail with order information


  Slack text with button


  Click the button will call moze url scheme

  工作流程描述
  此工作流程自動化從外送平台獲取郵件，提取關鍵訂單詳細資訊，並將通知發送到指定的 Slack 頻道。此外，Slack 消息中包含一個 Moze 記帳 App URL Scheme 的連結，方便快速記帳。

  主要功能
  Manual Trigger：允許手動執行工作流程，方便測試。
  Gmail Integration：從 Gmail 中提取包含特定關鍵字（例如：「透過 Uber Eats 系統送出的訂單」）的郵件。
  資料提取：解析郵件內容，提取以下關鍵資訊：
     訂單金額
     商家名稱
     訂單日期與時間
  Slack 通知：將結構化的通知發送到指定的 Slack 頻道，並包含一個連結供用戶快速記帳。

  節點設定

  1. Manual Trigger
  用途**：手動啟動工作流程以進行測試或即時執行。
  設定**：無需額外設定。

  2. Gmail Trigger
  用途**：自動檢查 Gmail 中是否有符合特定主題關鍵字的新郵件。
  設定**：
    篩選條件：
      q: subject:透過 Uber Eats 系統送出的訂單
    檢查頻率：每小時的 30 分。
    憑證：已連結的 Gmail 帳號。

  3. Extract Price, Shop, Date, Time
  用途**：使用正則表達式從郵件內容中提取關鍵資訊。
  提取的資料**：
    price：訂單金額（例如：$200）。
    shop：商家名稱（例如：「店名」）。
    date：訂單日期（例如：2024.01.01）。
    time：訂單時間（24 小時制，例如：14:30）。

  4. Slack 通知
  用途**：將訂單詳細資訊以格式化消息發送到 Slack。
  消息內容**：
    文字：
          Ubereat 訂餐資訊:
      商家: {{ shop }}
      金額: {{ price }}
      日期: {{ date }}
        Moze App 連結：Slack 消息中包含一個可點擊按鈕，預填 Moze App URL Scheme：
          moze3://expense?amount={{ price }}&account=信用卡&subcategory=外送&store={{ shop }}&date={{ date }}&time={{ time }}&project=生活開銷
        頻道：與外送通知相關的 Slack 頻道。

  補充說明
  自訂化**：可調整郵件主題篩選條件（subject），以匹配其他外送平台或服務。
  錯誤處理**：確保正則表達式匹配郵件格式。在部署前使用樣本郵件進行測試。
  Moze URL Scheme 參考**：了解更多關於 Moze App URL Scheme 的客製化資訊，請參閱 Moze 官方文件。

  此工作流程適合自動化費用記帳以及集中管理外送訂單通知，提升個人或團隊的費用管理效率。

nodes_used:
  ["n8n-nodes-base.splitInBatches","n8n-nodes-base.manualTrigger","n8n-nodes-base.gmail","n8n-nodes-base.gmailTrigger","n8n-nodes-base.set","n8n-nodes-base.slack"]

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name: Send Jira task notifications to Telegram user via Bot
author_name: Abolfazl Akbarzadeh
author_username: abolfazlakbarzadeh
categories: []
created_at: 2025-01-08T19:48:54.739Z
updated_at: 2025-01-08T19:48:54.739Z
url: https://n8n.io/workflows/2708
scraped_at: 2025-12-24 13:02:46
description:
  What we wanna do?

  Let's look at the concern. In my experience, some developers don't check their Jira board to find out whether there are new updates on the issues or not or if some Issues need to be addressed as soon as possible. So, the developer or anyone else in other fields needs to be informed about the task as soon as possible, too. One way to send this immediate notification is through the Telegram Bot.

  Setup Guide

  so, first of all, you need to register a Telegram Bot in your account and obtain its token, so that we'll be able to send Telegram messages by using this token through our bot;
  after getting your telegram bot token
  go to the workflow and click on one of the telegram nodes
  select the telegram credential or create one through the Credential to connect with field
  and put the token in the token in the Access Token field.

  Ok, you're done with the Telegram Side setup.

  then you need the Jira accounts (team users) accountId and also their telegram chatId for the telegram account node so that it can find the corresponding telegram user from the assignee of the issue, put this data as following guide comments in the telegram account node.

  Now we go for the Jira side setup, you need to setup some automation rules as your needs.
  go to the Jira settings and Global automation section, click on the Create Rule button
  select the Issue Created trigger type in the When step
  add a Send webhook request action, after selecting it you'll see its settings
  go back to workflow and from the jira-webhook node copy the Production URL 
  paste it in the Web request URL field in the Jira action setting
  then set the HTTP method field on POST 
  set Web request body on Issue Data (Automation format)
  in the header section, add a new header with the name type and value created for the creation event.

  OK, the Jira side also is done! Now It's time to test!

  If you've put your Jira accountId and telegram chatId in the telegram account node and of course started the telegram bot, after creating an Issue that is assigned to you, the creation notif will send to you in telegram!

nodes_used:
  ["n8n-nodes-base.code","n8n-nodes-base.if","n8n-nodes-base.switch","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.if","n8n-nodes-base.webhook"]

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name: Automated Email Optin Form with n8n and Hunter io for verification
author_name: Keith Rumjahn
author_username: rumjahn
categories: []
created_at: 2025-01-09T05:40:32.489Z
updated_at: 2025-01-09T05:40:32.489Z
url: https://n8n.io/workflows/2709
scraped_at: 2025-12-24 13:02:47
description:
  Case Study 📧
  Want to collect email subscribers without paying expensive monthly fees? 💰 This workflow creates a free email collection system with built-in email verification to ensure you only collect legitimate email addresses! ✨

  Learn more:
  📺 Watch the tutorial: 

  What this workflow does 🛠️
  Creates a customizable email collection form that can be embedded on your website 🌐
  Verifies email addresses using Hunter.io to filter out fake or invalid emails ✅
  Stores verified email addresses in SendGrid for your email marketing needs 📊
  Completely free solution (except for Hunter.io's 50 free monthly credits) 🆓

  Setup ⚙️
  Set up a free Hunter.io account for email verification
  Configure your SendGrid account credentials
  Customize the email collection form fields
  Get the embedded form code for your website

  How to adjust it to your needs 🔧
  Add additional form fields beyond just email collection
  Customize the form's appearance and labels
  Modify the verification logic based on your requirements
  Connect to different email marketing platforms instead of SendGrid
  Add additional automation steps after email verification

  Benefits 🌟
  No monthly subscription fees for email collection 💸
  Built-in email verification prevents fake signups 🛡️
  Scalable solution that won't lock you into expensive plans 📈
  Clean email list with only verified addresses ✨
  Simple setup and customization 🎯

  This workflow is perfect for bloggers, small businesses, and anyone looking to build an email list without getting locked into expensive email marketing platforms as their subscriber count grows! 🚀

  Built by rumjahn

nodes_used:
  ["n8n-nodes-base.noOp","n8n-nodes-base.if","n8n-nodes-base.hunter","n8n-nodes-base.formTrigger","n8n-nodes-base.sendGrid","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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name: Form with Dynamic Dropdown Field
author_name: Obsidi8n
author_username: derruttie
categories: []
created_at: 2025-01-09T13:29:08.624Z
updated_at: 2025-01-09T13:29:08.624Z
url: https://n8n.io/workflows/2712
scraped_at: 2025-12-24 13:02:46
description:
  This workflow creates a customizable form with a dynamic dropdown field that automatically updates its options from an external data source. 

  How it works

  The workflow polls an external data source (Google Sheets in this example) at regular intervals
  New values are processed and formatted for the dropdown
  The form automatically updates with the new dropdown options

  Set up steps

  Configure your data source:

  Default setup uses Google Sheets 
  	replace with credentials for your sheet
  	set up the update frequency
  Or modify to use any other data source (API, database, etc.)

  Adjust the form configuration:

  Customize the form title and description
  Add or modify form fields as needed
  The template includes the dropdown field by default

  Connect form submissions:

  Use the "Execute Workflow" node to process form submissions

  This template provides a foundation for creating dynamic forms that stay synchronized with your data sources, making it ideal for situations where dropdown options need to reflect current data.

nodes_used:
  ["n8n-nodes-base.n8n","n8n-nodes-base.n8n","n8n-nodes-base.formTrigger","n8n-nodes-base.googleSheetsTrigger","n8n-nodes-base.executeWorkflow","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.googleSheets","n8n-nodes-base.set"]

workflow_json_compressed:
  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


name: Using External Workflows as Tools in n8n
author_name: Alfred Nutile
author_username: dailyaistudio
categories: []
created_at: 2025-01-09T16:45:57.560Z
updated_at: 2025-01-09T16:45:57.560Z
url: https://n8n.io/workflows/2713
scraped_at: 2025-12-24 13:02:46
description:
  This guide will show you how to use a workflow as a reusable tool in n8n, such as integrating an AI Agent or other specialized processes into your workflows.

  By the end of this example, you'll have a simple, reusable workflow that can be easily plugged into larger projects, making your automations more efficient and scalable.



  With this approach, you can create reusable workflows like "Scrape a Page," "Search Brave," or "Generate an Image," which you can then call whenever needed.

  While n8n makes it easy to build these workflows from scratch, setting them up as reusable components saves time as your automations grow in complexity.

  Setup

  Add the "Execute Workflow Trigger" node

  Add the node(s) to perform the desired tasks in the workflow

  Add a final "Set" or "Edit Fields" node at the end to ensure all external workflows return a consistent output format

  Details

  In this example, the "Execute Workflow Trigger" expects input in the following JSON format:

  [
    {
      "query": {
        "url": "https://en.wikipedia.org/wiki/some_info"
      }
    }
  ]

  Once your external workflow is ready, you can instruct the AI Agent to use this tool by connecting it to the external workflow. Set up the schema type to "Generate from JSON Example" using this structure:

  {
    "url": "URL_TO_GET"
  }


  Finally, ensure your external workflow includes a "Set" or "Edit Fields" node at the end to define the response format. This helps keep the outputs of your reusable workflows consistent and predictable.

nodes_used:
  ["n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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name: Get Meta Ads insights and save them into Google Sheets
author_name: Solomon
author_username: solomon
categories: []
created_at: 2025-01-09T21:44:06.381Z
updated_at: 2025-01-09T21:44:06.381Z
url: https://n8n.io/workflows/2714
scraped_at: 2025-12-24 13:02:46
description:
  Pull data using the Facebook Graph API and get your Meta Ads insights into your Google Sheets for analysis.

  Get metrics like impressions, spend, reach, conversions, CTR, and CPC.

  Automate your data collection.

  Who is this template for?
  Marketing professionals looking to analyze Meta Ads data.

  You can also replace the Google Sheets node for a database node like MySQL or Postgres, and then use your BI tool (Power BI, Looker Studio, Tableau) to analyze the data.

  How it works
  Every day the flow gets Meta Ads insights from the previous day and saves them to Google Sheets.

  How to set up
  All information you need to set up this workflow is already inside the template.

  Check out my other templates
  👉 https://n8n.io/creators/solomon/

nodes_used:
  ["n8n-nodes-base.splitOut","n8n-nodes-base.splitOut","n8n-nodes-base.manualTrigger","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.facebookGraphApi","n8n-nodes-base.stickyNote","n8n-nodes-base.facebookGraphApi","n8n-nodes-base.splitOut","n8n-nodes-base.filter","n8n-nodes-base.filter","n8n-nodes-base.filter","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets"]

workflow_json_compressed:
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name: Store Form Submission in Airtable
author_name: WeblineIndia
author_username: weblineindia
categories: []
created_at: 2025-01-10T15:22:18.979Z
updated_at: 2025-01-10T15:22:18.979Z
url: https://n8n.io/workflows/2716
scraped_at: 2025-12-24 13:02:47
description:
  This workflow, developed by our AI developers at WeblineIndia, is designed to automate the process of capturing form submissions and storing them in Airtable. By leveraging automation, it eliminates manual data entry, ensuring a smooth and efficient way to handle form data. The purpose of creating this workflow is to streamline data management, helping businesses save time, reduce errors, and maintain an organized, structured database for easy access and future use.

  Steps:

  Trigger on Form Submission (Form Node)**
  	What It Does: Activates the workflow whenever a form is submitted.
  	How to Set It Up: Use the Form Submission Trigger node to detect new form submissions. This ensures the workflow starts automatically when a user fills out the form.

  Store Data in Airtable (Airtable Node)**
  	What It Does: Transfers the form data into an Airtable base.
  	How to Set It Up: Use the Airtable Node to map form fields to corresponding columns in your Airtable table, storing the data accurately.

  Finalize and Activate**
  	What It Does: Completes the setup to automate data storage upon form submission.
  	How to Set It Up: Save and activate the workflow. Once active, it will automatically record all new form submissions in Airtable.

nodes_used:
  ["n8n-nodes-base.formTrigger","n8n-nodes-base.airtable","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  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


name: AI agent for Instagram DM/inbox. Manychat + Open AI integration
author_name: Alex Hi no code
author_username: alex-nocode
categories: []
created_at: 2025-01-12T12:45:33.424Z
updated_at: 2025-01-12T12:45:33.424Z
url: https://n8n.io/workflows/2718
scraped_at: 2025-12-24 13:02:42
description:
  Automate Instagram DMs with OpenAI GPT and ManyChat

  How It Works:  
  Once connected, GPT will automatically initiate conversations with messages from new recipients in Intagram.

  Who Is This For?
  This workflow is ideal for
  marketers,
  business owners
  content creators

  who want to automatically respond to Instagram direct messages using OpenAI GPT. 

  By integrating ManyChat, you can manage conversations, nurture leads, and provide instant replies at scale.

  What This Workflow Does
  Captures** incoming Instagram DMs through ManyChat’s integration.  
  Processes** messages with GPT to generate a relevant response.  
  Delivers** instant replies back to Instagram users, creating efficient, AI-driven communication.  

  Setup
  Import the Template: Copy the n8n workflow into your workspace.  
  OpenAI Credentials: Add your OpenAI API key in n8n so GPT can generate responses.  
  ManyChat Account: Create (or log in to) your ManyChat account.  
  Connect Instagram: Link your Instagram profile as a channel in ManyChat.  
  ManyChat Custom Field: Create a custom field for storing user input or conversation context.  
  Configure Default Reply: In ManyChat, set up the default Instagram reply flow to point to your n8n webhook.  
  Add External Request: Create an external request step in ManyChat to send messages to n8n.  
  Test the Flow: Send yourself a DM on Instagram to confirm the workflow triggers and GPT responds correctly.  

  Instructions and links:

  Notion instruction

  Register in ManyChat

nodes_used:
  ["n8n-nodes-base.set","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.respondToWebhook","n8n-nodes-base.stickyNote","n8n-nodes-base.webhook","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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name: Retry on fail except for known error
author_name: Mario
author_username: octionic
categories: []
created_at: 2025-01-12T13:17:18.184Z
updated_at: 2025-01-12T13:17:18.184Z
url: https://n8n.io/workflows/2719
scraped_at: 2025-12-24 13:02:47
description:
  Purpose

  This workflow snippet allows for advanced error catching during retry attempts.

  There are cases, where you want to check if an item exists first, so you can determine the following actions. Some API’s do not support an endpoint (e.g. Todoist: completed tasks) to do so, which is why you would work with the error branch, only that this does not work well in combination with the retry functionality.

  How it works

  Instead of the builtin retry function of a Node a custom loop is used, to get more granular control in between the iterations
  If the main executed node fails, the error can be filtered for an expected error, which can trigger a separate action
  The retries only happen, if an unexpected error happened
  The workflow only stops, if the defined amount of retries exceeded

  Setup

  Copy the nodes into your existing workflow
  Replace the “Replace me” placeholder with the Node you want to apply the retry logic on
  Follow the sticky notes for more instructions and optional settings

nodes_used:
  ["n8n-nodes-base.stopAndError","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.wait","n8n-nodes-base.if","n8n-nodes-base.noOp","n8n-nodes-base.noOp","n8n-nodes-base.noOp","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.manualTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.if","n8n-nodes-base.stickyNote"]

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name: Email Summary Agent
author_name: Vishal Kumar
author_username: vishalquantana
categories: []
created_at: 2025-01-13T17:24:39.260Z
updated_at: 2025-01-13T17:24:39.260Z
url: https://n8n.io/workflows/2722
scraped_at: 2025-12-24 13:02:42
description:
  Problem
  Teams often struggle with email overload, leading to missed actions and inefficient meeting preparation.

  Solution
  This workflow automates email management using n8n and AI. It fetches emails, summarizes key points and actions, and sends two concise updates—one in the morning and one at night.

  How It Works

  Triggers at 7 AM and 9 PM: Automates the process to summarize emails received during specific time blocks.

  Fetches Emails: Retrieves emails from the last 24 hours or after a specific time.

  Summarizes with AI: Uses OpenAI to process the email content into actionable summaries.
  Sends Team Updates: Compiles the summaries into a concise, formatted email and sends it to the team.

  Expected Results
  Significant reduction in missed actions and follow-ups. 

  Customizations
  Adjust timings, filters, and recipients to suit your team’s needs.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.gmail","n8n-nodes-base.aggregate","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.gmail","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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name: Display ServiceNow Incident Details in Slack using Slash Commands
author_name: Angel Menendez
author_username: djangelic
categories: []
created_at: 2025-01-14T20:55:38.962Z
updated_at: 2025-01-14T20:55:38.962Z
url: https://n8n.io/workflows/2727
scraped_at: 2025-12-24 13:02:47
description:
  Who is this for?
  This workflow is designed for teams using Slack for communication and ServiceNow for incident management. It simplifies incident lookup by enabling team members to fetch incident details directly within Slack via a Slash Command.

  What problem is this workflow solving?
  Manually switching between Slack and ServiceNow to retrieve incident details can be time-consuming and disrupt workflow efficiency. This workflow bridges the two platforms, providing instant access to critical incident information in Slack, saving time, and improving response efficiency.

  What this workflow does?
  The workflow listens for a Slash Command in Slack that includes an incident ID, extracts the ID from the incoming payload, queries ServiceNow for the corresponding incident details, and sends a formatted response back to Slack. Depending on the query result, it can:
  Display incident details (e.g., ID, description, severity, and priority).
  Notify the user if no matching incident is found.
  Alert the user if there’s an issue connecting to ServiceNow.

  Setup
  Slack Setup:
     Create a Slash Command in Slack with the appropriate endpoint URL.
     Configure the command to send a POST request to the webhook endpoint of this workflow.
     For details on how to setup the Slack app using Slash commands and n8n, check out this video. 

  ServiceNow Setup:
     Create or use an existing account with the necessary permissions to access incident data.
     Configure the ServiceNow node with your ServiceNow credentials.

  n8n Workflow Activation:
     Deploy and activate the workflow in your n8n instance.
     Ensure all nodes are properly configured and connected.

  How to customize this workflow to your needs
  Modify Incident Query Parameters:** Adjust the query logic in the Search For Incident in ServiceNow node to include additional filters or data points based on your organization’s needs.
  Slack Response Customization:** Customize the Slack response template to display additional incident details or to match your team’s tone and style.
  Error Handling:** Enhance the error handling nodes to include more detailed logs or send alerts to a dedicated Slack channel.

nodes_used:
  ["n8n-nodes-base.webhook","n8n-nodes-base.set","n8n-nodes-base.serviceNow","n8n-nodes-base.respondToWebhook","n8n-nodes-base.respondToWebhook","n8n-nodes-base.respondToWebhook","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.switch"]

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name: List recent ServiceNow Incidents in Slack Using Pop Up Modal
author_name: Angel Menendez
author_username: djangelic
categories: []
created_at: 2025-01-15T04:47:19.413Z
updated_at: 2025-01-15T04:47:19.413Z
url: https://n8n.io/workflows/2728
scraped_at: 2025-12-24 13:02:47
description:
  Who is this for?  
  This workflow is designed for IT teams, service desk personnel, and incident management professionals who need a streamlined way to monitor and report on recent ServiceNow incidents directly within Slack.  

  What problem is this workflow solving? / Use Case  
  Manually monitoring incidents in ServiceNow can be time-consuming, and keeping teams updated about new or specific incidents often involves additional manual effort. This workflow automates the process of querying recent incidents from ServiceNow based on user-defined parameters and delivering formatted results directly to Slack. It ensures faster response times and improved incident visibility.  

  What this workflow does  
  This workflow integrates Slack and ServiceNow to provide an automated system for retrieving and presenting incident details.  
  Slack User Interaction: Users initiate the workflow via a Slack modal form, selecting incident parameters like priority and state.  
  ServiceNow Query: The workflow queries ServiceNow for incidents matching the selected criteria.  
  Results Delivery: Incident results are sent back to Slack as a message formatted using Block Kit. If no results are found, the workflow notifies the user with a detailed message, either in a Slack channel or via direct message.  
  Error Handling: If no channel is selected or any issues arise, the workflow ensures graceful fallback with appropriate notifications.  

  Setup Instructions  
  Slack Setup:  
     Integrate Slack with n8n using a Slack app.  
     Configure the modal form to accept parameters like priority and state.  
     Check out this video for setting up a modal slack app on YouTube. 
  ServiceNow Integration:  
     Use ServiceNow credentials to connect with n8n.  
     Ensure appropriate permissions for querying incidents.  
  n8n Workflow Configuration:  
     Import this workflow into n8n.  
     Verify all node configurations, particularly those for ServiceNow API queries and Slack outputs.  
     Set up webhook URLs for Slack event handling.  
  Testing:  
     Trigger the workflow from Slack to test modal inputs and incident queries.  
     Confirm the output is correctly formatted and delivered to the intended Slack channel or user.  

  How to Customize this Workflow to Your Needs  
  Modify the ServiceNow query logic to include additional filters or fields.  
  Adjust the Slack Block Kit formatting to match your organization’s preferred notification style.  
  Use conditional logic to add more advanced handling for specific priorities or states.  
  Expand the workflow to include escalation steps, such as notifying a specific team or creating follow-up tasks.  

  Workflow Highlights  
  Slack Modal Form**: Allows users to specify search criteria for incidents interactively.  
  Dynamic Results Delivery**: Automatically sends results to a Slack channel or direct message based on user input.  
  Error Handling**: Provides fallback notifications when no incidents are found or user inputs are incomplete.  
  Customizable Integration**: Easily adaptable to fit different organizational needs, including advanced filtering and formatting options.

nodes_used:
  ["n8n-nodes-base.set","n8n-nodes-base.respondToWebhook","n8n-nodes-base.switch","n8n-nodes-base.httpRequest","n8n-nodes-base.webhook","n8n-nodes-base.respondToWebhook","n8n-nodes-base.serviceNow","n8n-nodes-base.slack","n8n-nodes-base.slack","n8n-nodes-base.if","n8n-nodes-base.if","n8n-nodes-base.sort","n8n-nodes-base.limit","n8n-nodes-base.splitInBatches","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.summarize","n8n-nodes-base.slack","n8n-nodes-base.slack","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.if","n8n-nodes-base.stickyNote","n8n-nodes-base.respondToWebhook"]

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name: 🔐🦙🤖 Private & Local Ollama Self-Hosted AI Assistant
author_name: Joseph LePage
author_username: joe
categories: []
created_at: 2025-01-15T05:39:06.856Z
updated_at: 2025-01-15T05:39:06.856Z
url: https://n8n.io/workflows/2729
scraped_at: 2025-12-24 13:02:42
description:
  Transform your local N8N instance into a powerful chat interface using any local & private Ollama model, with zero cloud dependencies ☁️. This workflow creates a structured chat experience that processes messages locally through a language model chain and returns formatted responses 💬.

  How it works 🔄
  💭 Chat messages trigger the workflow
  🧠 Messages are processed through Llama 3.2 via Ollama (or any other Ollama compatible model)
  📊 Responses are formatted as structured JSON
  ⚡ Error handling ensures robust operation

  Set up steps 🛠️
  📥 Install N8N and Ollama
  ⚙️ Download Ollama 3.2 model (or other model)
  🔑 Configure Ollama API credentials
  ✨ Import and activate workflow

  This template provides a foundation for building AI-powered chat applications while maintaining full control over your data and infrastructure 🚀.

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.chainLlm","@n8n/n8n-nodes-langchain.lmOllama","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.set"]

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name: Get Daily Weather and Save It in Airtable
author_name: WeblineIndia
author_username: weblineindia
categories: []
created_at: 2025-01-15T08:26:21.852Z
updated_at: 2025-01-15T08:26:21.852Z
url: https://n8n.io/workflows/2730
scraped_at: 2025-12-24 13:02:47
description:
  This smart automation workflow created by the AI development team at WeblineIndia, helps with the daily collection and storage of weather data. Using the OpenWeatherMap API and Airtable, this solution gathers vital weather details such as temperature, humidity, and wind speed. The automation ensures daily updates, creating a dependable historical record of weather patterns for future reference and analysis.

  Steps:

  Set Schedule Trigger
  Configure a Cron node to trigger the workflow daily, for example, at 7 AM.

  Fetch Weather Data (HTTP Request)

  Use the HTTP Request node to retrieve weather data from the OpenWeatherMap API.
  Include your API key and query parameters (e.g., q=London, unit=metric) to specify the city and desired units.

  Parse Weather Data

  Utilize a JSON Parse node to extract key weather details, such as temperature, humidity, and wind speed, from the API response.

  Store Data in Airtable

  Use the Airtable node to insert the parsed data into the designated Airtable table. Ensure proper mapping of fields like temperature, humidity, and wind speed.

  Save and Execute

  Save the workflow and activate it to ensure weather data is fetched and stored automatically every day.

  Outcome
  This robust solution, developed by WeblineIndia, reliably collects and archives daily weather data, providing businesses and individuals with an accessible record of weather trends for analysis and decision-making.

  About WeblineIndia
  We specialize in creating custom automation solutions and innovative software workflows to help businesses streamline operations and achieve efficiency. This weather data fetcher is just one example of our expertise in delivering value through technology.

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.airtable","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  H4sIAAAAAAAAE7VXWW/bOBD+K4Kw2CfL0X0YKBZOtk3SNkcbp91eKChyZHMrU4pIOXEC//cdSrItJ+luG2yfbJHD75uLM8M7kzNzZJ4fld70VbH8kF3cqnSybw7MOShiju5MLqQigsKxlnN88GkUObYfuS5JnTgMvIRlQAIndsF3EgKUxh6LgyT0MnC8LGQQhy6NowDSjCCugnmZEwUHFTB5AaouDwpcAQVIoKoaVgNTkDkg27hWxRxFmfEn4fnSeA9EzaDCL0WMF6Co/iCCGReqqMgUNDqZSnP06a41q76g4kVyevvWO399vFxcmhvo8zrNuZwh58CkFWiSscJ113YDy/Ys15nY0ch1R2489BPnI4rVJftvsdUXpCgY9JSIIydjScYsBr5v+TTJLAKZb9k08hwahHGQOVvFLtAoVudgTCo+nUKlbVqWekfEwmqgrZRIGMpOcCtXFpIrXgik9u2BY9uoS0kqxFVQSR3LCuXbmOLKguSNkqoFGKujoq7MkWOvvqxWLes7PNcAOkN3NejssdPIdahLrNCzHcsndmwloe1aSWIHSczsLCJezx4MDexE7rsWEV4pkuY6jKJQ2oXd6evuNNNx58JopD6LeybHj9usofXv169V3iYY5jaSIjpmgEIQdEStP0lZvkLNspNS7R+/ulClzg2i3fwWZJ2rSw1g3ls8bc3sDGzs085r7PghVpXmWerJl7EPby7zDy/ZT7Cu6WiR13PRmNvB3pkTvGco8uzuzvjtb1mI4ZxwMdS3z1itEO6onnPG1fKhzKzbaeVeF5S0Lu7J6eC22xM+h9tCwM626hZbkfdcX9ES9AXvCV3j8lDqZS2GVuiMnpPtxdkwbxJGYq6KKX4zLrGILNeerWBeLDR8RnLZfBN2JvJlb+Gq5lVPogPo/NhjYpARdPAJweqykaZE7MOlBDYpuo22UHWKbnywzex6njZ38n9WtMf0JEV1Tvx6JVuWpyjYS5VfruYO11OU3dygX65qj+lnFMVaOMeihlfmpC09eJgL2Ie8uNYdXovi5sG6enz6grewKPVN0LWk+YBqffvbNtmUG9QUhOJIrivNum5Pim8gxiVvOgxri1bXBUzdUZqifixeaPbOMTtNxh062yYTuMSPqGMxJ4ws33OplXiZazlORBzquAkNek3zENSPtZiZUuVb9DU0JXjdZZpRAh1hIMBOs9ntMG74eIepmwqtoeVob4+UfIhuEx0OBmBYVNM9DbfnDoO9bv0PnIGeud7Qxqb5e16IZxFuBn5k3ouABMHe1FBtconUeBydv6nKUxBQcXqwCcpEmz5Yr+MQNZu0ztAqNlh6DSWu9P/zHVv6lmEd7vxbC65kr2ehBCKb7aBwLxs0yT6RLfEmFW6L64/VvpOcqrOln9+U29idoa+62I3PuU6vXTXXCGfv/jq6/Jtc3V7ePD84en74KMIJKY3mqNGc7cCO8Loheh9tmr/0PBIeBvnVbO4cvO8NLLOi5NnSOKxIOXvz2tB+/aH09XszUuAGmQeJa9HQtS0/wsxNkwiswKMpzkih59thf0bi9NvSOEWC7497jUwn0ktKe/AwIXEcKHCSCwfmNWfa6BBnI3MGfDrD4dVzbT0x4Ago1BOG7M8CJ68H8+HGcpplMUtT2/JCL7H8gIQWsUNmJSQNvdRlGYX4UcudJ5iObvxJ451d4yczLo3rovqWYVSNrLFW7s6brPFKLdf14V62jc+PG+9IPanKnQrCRVbg/dfa6ql13JXJoTFBoQYb6zbu0LxmYNC6qlApQ09ouujWFQyM9Sg2MPS0ZDTT0qDhKxqKCnJYEDy1URlfbTyXmgMtI21stQIgZN0qSNSDOmegbPe8MWChrw8jy61ZzEBDjKzWOiFlBqgoRe3KqligeugYtAOIXFqqsAilIKWB9HgUq1SOJ2hRocbZDm9JFIZMyOEjyYRBJVTxBWw6nASlkKiJMNwArbVRZxVea4ziwtEYi/Z8806FLI58Hx9ovhNjFqZ4Ewm+SDEV3YwGcUDTSFdaTAQBdF1tH7YRXNMTMeaaroVtD/23B00jPMDnFYMbc4QPKd1QH7zoHkP9fgd7BHO1+gfMG8ditw8AAA==


name: Daily Birthday Reminders from Google Contacts to Slack
author_name: WeblineIndia
author_username: weblineindia
categories: []
created_at: 2025-01-15T08:32:01.026Z
updated_at: 2025-01-15T08:32:01.026Z
url: https://n8n.io/workflows/2731
scraped_at: 2025-12-24 13:02:46
description:
  Ensure you never miss a birthday with this automated workflow designed by WeblineIndia. It retrieves your Google Contacts, identifies birthdays happening today, and sends personalized reminders directly to a designated Slack channel. This daily automation keeps your team informed and makes birthday celebrations effortless.

  Steps

  Set Daily Schedule (Cron Node)
  Configure a Cron node to trigger the workflow daily at a specific time (e.g., 8 AM).
  This ensures the workflow runs consistently every day to check for birthdays.

  Retrieve Contacts (Google Contacts - Get Contact Node)
  Use the Google Contacts (Get Contact) node to fetch your contact list.
  Ensure your contacts have birthday details stored for accurate filtering.

  Filter Birthdays (IF Node)
  Add an IF Node to compare the current date with each contact’s birthday.
  Only contacts whose birthdays match today’s date will move to the next step.

  Send Birthday Notifications to Slack (Slack - Send Message Node)
  Use the Slack node to send a personalized birthday message to your chosen Slack channel (e.g., #general or #birthdays).
  Customize the message to include the contact’s name, e.g., "🎉 Today is John Doe's birthday! Let’s celebrate!"
  Configure the node to target a specific Slack channel for seamless notifications.

  Activate Workflow
  Save and activate the workflow.

  From now on, the workflow will automatically check for birthdays daily and send timely reminders to your Slack team.

  Outcome
  This hassle-free automation keeps your team engaged and ensures no birthday goes unnoticed. Celebrate special days of your contacts effortlessly and maintain meaningful connections.

  About WeblineIndia
  This workflow showcases our commitment to delivering innovative automation solutions that enhance productivity and foster better relationships. Let us help you build the AI automation tools that make a difference.

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.googleContacts","n8n-nodes-base.if","n8n-nodes-base.slack","n8n-nodes-base.filter","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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name: CSV to JSON Converter with Error Handling and Slack Notifications
author_name: n8n Team
author_username: n8n-team
categories: []
created_at: 2025-01-15T11:45:23.218Z
updated_at: 2025-01-15T11:45:23.218Z
url: https://n8n.io/workflows/2732
scraped_at: 2025-12-24 13:02:46
description:
  Who this template is for
  This template is for developers or teams who need to convert CSV data into JSON format through an API endpoint, with support for both file uploads and raw CSV text input.

  Use case
  Converting CSV files or raw CSV text data into JSON format via a webhook endpoint, with error handling and notifications. This is particularly useful when you need to transform CSV data into JSON as part of a larger automation or integration process.

  How this workflow works

  Receives POST requests through a webhook endpoint at 
  /tool/csv-to-json
  Uses a Switch node to handle different input types:
      File uploads (binary data)
      Plain text CSV data
      JSON format data
  Processes the CSV data:
      For files: Uses the Extract From File node
      For raw text: Converts the text to CSV using a custom Code node that handles both comma and semicolon delimiters
  Aggregates the processed data and returns:
      Success response (200): Converted JSON data
      Error response (500): Error message with details
  In case of errors, sends notifications to a Slack error channel with execution details and a link to debug

  Set up steps

  Configure the webhook endpoint by deploying the workflow
  Set up Slack integration for error notifications:
      Update the Slack channel ID (currently set to "C0832GBAEN4")
      Configure OAuth2 authentication for Slack
  Test the endpoint using either:
      CURL for file uploads:
          
                  bash
          Copy
          curl -X POST "https://yoururl.com/webhook-test/tool/csv-to-json" \
               -H "Content-Type: text/csv" \
               --data-binary @path/to/your/file.csv
          
                  
      Or send raw CSV data as text/plain content type

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.extractFromFile","n8n-nodes-base.respondToWebhook","n8n-nodes-base.respondToWebhook","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.switch","n8n-nodes-base.slack","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.if","n8n-nodes-base.httpRequest","n8n-nodes-base.webhook","n8n-nodes-base.aggregate","n8n-nodes-base.aggregate","n8n-nodes-base.respondToWebhook"]

workflow_json_compressed:
  [encoded data, 4212 characters]


name: Line Message API : Push Message & Reply
author_name: darrell_tw
author_username: darrelltw
categories: []
created_at: 2025-01-15T17:08:04.179Z
updated_at: 2025-01-15T17:08:04.179Z
url: https://n8n.io/workflows/2733
scraped_at: 2025-12-24 13:02:46
description:
  Workflow Description
  This workflow demonstrates how to use the LINE Messaging API to handle two scenarios:
  Replying to a user's message using a reply token.
  Sending a push message to a specific LINE user using their user ID.

  Key Features
  Webhook Integration: Receives and processes incoming messages from LINE using a webhook.
  Conditional Logic: Checks if the received event type is a message and handles it accordingly.
  Reply Message: Automatically responds to the user's message using the LINE reply token.
  Push Message: Sends a test message to a specific LINE user using their unique user ID.

  Pre-Configuration
  To simplify the setup process, create a Header Auth credential in n8n:
  Name**: Authorization
  Value**: Bearer {line token}  
  This will authenticate all API requests to the LINE Messaging API.
  Node Configurations

  1.1. Webhook from LINE Message
  Purpose**: Captures incoming events from the LINE Messaging API.
  Configuration**:
    HTTP Method: POST
    Path: {n8n-webhook-page}

  1.2. If Condition
  Purpose**: Checks if the received event type is message.
  Configuration**:
    Condition:
      {{ $json.body.events[0].type }} equals "message"

  1.3. Line: Reply with Token
  Purpose**: Replies to the user's message using the LINE reply token.
  Configuration**:
    Method: POST
    URL: https://api.line.me/v2/bot/message/reply
    JSON Body:
          {
        "replyToken": "{{ $('Webhook from Line Message').item.json.body.events[0].replyToken }}",
        "messages": [
          {
            "type": "text",
            "text": "收到您的訊息 : {{ $('Webhook from Line Message').item.json.body.events[0].message.text }}"
          }
        ]
      }
  2.1. Manual Trigger: Test Workflow
  Purpose**: Triggers the workflow for testing the push message functionality.
  Configuration**: No additional setup required.

  2.2. Edit Fields
  Purpose**: Prepares the unique LINE user ID for the push message.
  Configuration**:
    Field:
      line_uid: Uxxxxxxxxxxxx

  2.3. Line: Push Message
  Purpose**: Sends a test message to a specific LINE user.
  Configuration**:
    Method: POST
    URL: https://api.line.me/v2/bot/message/push
    JSON Body:
          {
        "to": "{{ $json.line_uid }}",
        "messages": [
          {
            "type": "text",
            "text": "推播測試"
          }
        ]
      }
  工作流程描述
  此工作流程展示如何使用 LINE Messaging API 處理兩種情境：
  使用 reply token 回應使用者的訊息。
  使用使用者的 user ID 發送 推播訊息。

  主要功能
  Webhook 整合：透過 Webhook 接收並處理來自 LINE 的訊息。
  條件邏輯：檢查接收到的事件類型是否為訊息並進行處理。
  回應訊息：使用 LINE 的 reply token 自動回覆使用者的訊息。
  推播訊息：使用 LINE User ID 向指定用戶發送測試訊息。

  預先設定
  為簡化設定流程，請在 n8n 中建立 Header Auth 憑證：
  名稱**：Authorization
  值**：Bearer {line token}  
  此設定將用於認證所有 LINE Messaging API 的請求。
  節點設定

  1.1. Webhook from LINE Message
  用途**：接收來自 LINE Messaging API 的事件。
  設定**：
    HTTP 方法：POST
    路徑：{n8n-webhook-page}

  1.2. If 條件判斷
  用途**：檢查接收到的事件類型是否為 message。
  設定**：
    條件：
      {{ $json.body.events[0].type }} 等於 "message"

  1.3. Line: Reply with Token
  用途**：使用 LINE reply token 回應使用者訊息。
  設定**：
    方法：POST
    URL：https://api.line.me/v2/bot/message/reply
    JSON 主體：
          {
        "replyToken": "{{ $('Webhook from Line Message').item.json.body.events[0].replyToken }}",
        "messages": [
          {
            "type": "text",
            "text": "收到您的訊息 : {{ $('Webhook from Line Message').item.json.body.events[0].message.text }}"
          }
        ]
      }
  2.1. 手動觸發：測試工作流程
  用途**：測試推播訊息功能。
  設定**：無需額外設定。

  2.2. Edit Fields
  用途**：準備推播訊息所需的 LINE 使用者 ID。
  設定**：
    欄位：
      line_uid：Uxxxxxxxxxxxx

  2.3. Line: 推播訊息
  用途**：向特定 LINE 使用者發送測試訊息。
  設定**：
    方法：POST
    URL：https://api.line.me/v2/bot/message/push
    JSON 主體：
          {
        "to": "{{ $json.line_uid }}",
        "messages": [
          {
            "type": "text",
            "text": "推播測試"
          }
        ]
      }
      

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.webhook","n8n-nodes-base.stickyNote","n8n-nodes-base.if","n8n-nodes-base.stickyNote","n8n-nodes-base.manualTrigger","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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name: Stock Technical Analysis with Google Gemini
author_name: Udit Rawat
author_username: ailistmaster
categories: []
created_at: 2025-01-16T08:21:40.374Z
updated_at: 2025-01-16T08:21:40.374Z
url: https://n8n.io/workflows/2735
scraped_at: 2025-12-24 13:02:46
description:
  The purpose of this workflow, "Sell: Stock Vision," is to create an AI-powered technical analysis agent capable of analyzing financial charts for equity stocks and cryptocurrencies. This workflow provides users with actionable insights into market trends, price movements, candlestick patterns, and technical indicators to support informed trading decisions.

  How It Works

  Integration with TradingView:** The workflow uses the Chart-Img.com API to fetch detailed financial charts for the specified stock or cryptocurrency.
  AI-Powered Analysis:** The workflow employs advanced AI models, including Google's Gemini Chat Model, to analyze the retrieved charts for candlestick patterns, support/resistance levels, and technical indicators like MACD and RSI.
  News and Sentiment Analysis:** By integrating with SerpAPI, the workflow gathers relevant news about the stock or cryptocurrency to evaluate its potential impact on market movements.
  Comprehensive Insights:** It provides detailed trading strategies, including buy/sell recommendations, stop-loss levels, and risk-reward evaluations.
  Continuous Memory:** The AI agent uses buffer memory to retain context for enhanced analysis and continuity.

  Use Case
  This workflow is perfect for traders and analysts who need reliable and AI-powered market analysis to make informed trading decisions efficiently.

  Tutorial

  Obtain API keys for Chart-Img.com and SerpAPI.
  Configure the workflow in your n8n instance by inputting the required API keys and connecting the nodes.
  Trigger the workflow by providing the stock or cryptocurrency symbol, and let the agent do the rest!

  https://youtu.be/9fR4qNMT5LM

nodes_used:
  ["n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.chatTrigger","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.toolSerpApi"]

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name: Summarize YouTube Videos from Transcript
author_name: Ahmed Alnaqa
author_username: ahmedalnaqa
categories: []
created_at: 2025-01-16T12:37:51.928Z
updated_at: 2025-01-16T12:37:51.928Z
url: https://n8n.io/workflows/2736
scraped_at: 2025-12-24 13:02:43
description:
  Who is this template for?  
  This workflow template is designed for content creators, researchers, educators, and professionals who need quick, accurate summaries of YouTube videos. It’s ideal for those looking to save time, extract key insights, or repurpose video content into concise formats for reports, studies, or social media.  

  What does it do?  
  The workflow automates the process of summarizing YouTube videos by extracting the transcript, analyzing the content, and generating a concise summary. It leverages AI tools to ensure accuracy and relevance, making it easier to digest lengthy videos in seconds.  

  Why is it useful?  
  This template saves hours of manual effort by automating video summarization, enabling users to focus on analyzing or sharing insights rather than watching entire videos. It’s particularly useful for staying updated with trends, conducting research, or creating content efficiently.  

  How does it work?  
  The workflow integrates with YouTube’s Transcript API powered by Apify Actor to fetch video transcripts, process the text using AI-powered summarization tools, and deliver a clear, concise summary.

  Setup Instructions

  You need an Apify account and an API key to connect with the Actor. Follow the steps below:

  Create a Free Account.
  Choose the appropriate Actor from the Apify search.  
  Under the Integration tab, click on “Use API endpoints.”  
  Select the API that best suits your needs.  

nodes_used:
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name: Ultimate Content Generator for WordPress
author_name: Alex Kim
author_username: alexk1919
categories: []
created_at: 2025-01-17T03:25:32.265Z
updated_at: 2025-01-17T03:25:32.265Z
url: https://n8n.io/workflows/2737
scraped_at: 2025-12-24 13:02:45
description:
  Overview  
  This workflow automates the end-to-end process of creating, optimizing, and publishing content on WordPress. 

  It integrates AI-powered tools, Airtable, and WordPress plugins to generate high-quality, on-brand posts effortlessly. 

  Perfect for content creators, marketers, and business owners looking to save time and scale their content strategy.  

  Features  

  Content Creation:  
  AI-Powered Content:** Generates SEO-friendly blog posts with structured headings, relevant keywords, and meta descriptions.  
  Custom Prompts:** Tailor the AI-generated content to match your brand’s tone and voice.  

  SEO Optimization:  
  RankMath Plugin Integration:** Updates RankMath SEO with focus keywords and meta descriptions, ensuring your content is search-engine optimized.  

  Content Management:  
  Airtable Integration:** Organizes content ideas, drafts, and publishing schedules in one place. Easily scalable for teams or solo creators.  

  Visuals:  
  Branded Featured Images:** Automatically generates on-brand images for every post.  

  Publishing:  
  Effortless Formatting:** Adapts content to fit your WordPress theme and schedules it for publication.  

  Workflow Steps  
  Trigger:  
     Initiated manually or on a schedule.  
  Content Management:  
     Retrieves and organizes ideas from Airtable.  
  Content Generation:  
     Generates AI-driven blog content tailored to your audience.  
  SEO Optimization:  
     Automatically updates RankMath with SEO details.  
  Featured Image Creation:  
     Produces on-brand images for the post.  
  Publishing:  
     Formats and schedules the post on WordPress.  

  Prerequisites  

  API Keys:  
  OpenAI  
  Airtable  
  WordPress REST API  
  RankMath SEO Plugin  

  Custom Code:  
  Add a small update to your WordPress theme’s functions.php file to enable seamless automation.  

  Customization  
  Replace Airtable with another content management system if preferred.  
  Adjust AI prompts to reflect different tones, styles, or industries.  
  Add integrations for additional plugins, analytics, or storage services.  

  Usage  
  Import the workflow into your n8n instance.  
  Configure API credentials for WordPress, Airtable, OpenAI, and RankMath.  
  Update your functions.php file with the provided code snippet.  
  Customize prompts and Airtable structure for your content needs.  
  Trigger the workflow manually or set it on a schedule.  

  Notes  
  Experiment with Airtable views or add filters for more granular control over your content pipeline.  
  Extend the workflow to include social media posting or analytics tracking.  
  For questions, refer to n8n documentation or reach out to the creator.  

  Tools Used  
  Airtable  
  OpenAI GPT  
  WordPress REST API  
  RankMath SEO Plugin  

  Feel free to adapt and extend this workflow to meet your specific needs! 🎉  

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name: Transform Image to Lego Style Using Line and Dall-E
author_name: Boriwat Chanruang
author_username: boriwatopal
categories: []
created_at: 2025-01-17T03:33:28.196Z
updated_at: 2025-01-17T03:33:28.196Z
url: https://n8n.io/workflows/2738
scraped_at: 2025-12-24 13:02:46
description:
  Who is this for?
  This workflow is designed for:
  Content creators**, artists, or hobbyists looking to experiment with AI-generated art.
  Small business owners* or *marketers** using LEGO-style designs for branding or promotions.
  Developers* or *AI enthusiasts** wanting to automate image transformations through messaging platforms like LINE.

  What problem is this workflow solving?
  Simplifies the process of creating custom AI-generated LEGO-style images.
  Automates the manual effort of transforming user-uploaded images into AI-generated artwork.
  Bridges the gap between messaging platforms (LINE) and advanced AI tools (DALL·E).
  Provides a seamless system for users to upload an image and receive an AI-transformed output without technical expertise.

  What this workflow does
  Image Upload via LINE:
     Users send an image to the LINE chatbot.
  AI-Powered Prompt Creation:
     GPT generates a prompt to describe the uploaded image for LEGO-style conversion.
  AI Image Generation:
     DALL·E 3 processes the prompt and creates a LEGO-style isometric image.
  Image Delivery:
     The generated image is returned to the user in LINE.

  Setup

  Prerequisites
  LINE Developer Account** with API credentials.
  Access to OpenAI API with DALL·E and GPT-4 capabilities.
  A configured n8n instance to run this workflow.

  Steps
  Environment Setup:
     Add your LINE API Token and OpenAI credentials as environment variables (LINE_API_TOKEN, OPENAI_API_KEY) in n8n.
  Configure LINE Webhook:
     Point the LINE webhook to your n8n instance.
  Connect OpenAI:
     Set up OpenAI API credentials in the workflow nodes for GPT-4 and DALL·E.
  Test Workflow:
     Upload a sample image in LINE and ensure it returns the LEGO-style AI image.

  How to customize this workflow to your needs
  Localization**:
    Modify response messages in LINE to fit your audience's language and tone.
  Integration**:
    Add nodes to send notifications through other platforms like Slack or email.
  Image Style**:
    Replace the LEGO-style image prompt with other artistic styles or themes.

  Advanced Use Cases
  Art Contests:
     Users upload images and receive AI-enhanced outputs for community voting or branding.
  Marketing Campaigns:
     Quickly generate creative visual content for ads and promotions using customer-submitted photos.
  Education:
     Use the workflow to teach students about AI-generated art and automation through a hands-on approach.

  Tips for Optimization
  Error Handling**:
    Add fallback nodes to handle invalid images or API errors gracefully.
  Logging**:
    Implement a logging mechanism to track requests and outputs for debugging and analytics.
  Scalability**:
    Use queue-based systems or cloud scaling to handle large volumes of image requests.

  Enhancements
  Add sticky notes in n8n to provide inline instructions for configuring each node.
  Create a tutorial video or documentation for first-time users to set up and customize the workflow.
  Include advanced filters to allow users to select from multiple styles beyond LEGO (e.g., pixel art, watercolor).

  This workflow enables seamless interaction between messaging platforms and advanced AI capabilities, making it highly versatile for various creative and business applications.

nodes_used:
  ["n8n-nodes-base.webhook","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.httpRequest"]

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name: Convert Addresses to LatLong with Google Sheets and Google Maps API
author_name: Boriwat Chanruang
author_username: boriwatopal
categories: []
created_at: 2025-01-17T03:43:26.331Z
updated_at: 2025-01-17T03:43:26.331Z
url: https://n8n.io/workflows/2739
scraped_at: 2025-12-24 13:02:46
description:
  Template Detail
  This template automates the process of converting a list of addresses into their latitude and longitude (LatLong) coordinates using Google Sheets and the Google Maps API. It's designed for businesses, developers, and analysts who need accurate geolocation data for use cases like delivery routing, event planning, or market analysis.

  What the Template Does
  Fetch Address Data: Retrieves addresses from a Google Sheet.
  Google Maps API Integration: Sends each address to the Google Maps API and retrieves the corresponding LatLong coordinates.
  Update Google Sheets: Automatically updates the same Google Sheet with the LatLong data for each address.

  Enhancements
  Google Sheets Template: Provide a pre-configured Google Sheets template that users can copy. Example link: Google Sheets Template. 

  Columns required:
     Address: Column to input addresses.
     LatLong: Column for the latitude and longitude results.

  Updated Workflow Structure

  Trigger:
     A manual trigger node starts the workflow.
  Retrieve Data from Google Sheets:
     Fetch addresses from a Google Sheet.
  Send to Google Maps API:
     For each address, retrieve the LatLong coordinates directly via the Google Maps API.
  Update Google Sheets:
     Write the LatLong results back into the Google Sheet.

  Steps to Use
  Prepare Google Sheet:
     Copy the provided Google Sheets template and add your addresses to the Address column.
  Configure Google Cloud API:
     Enable the Maps API for your Google Cloud project.
     Generate an API key with the required permissions.
  Run the Workflow:
     Start the workflow in n8n; it will process the addresses automatically.
     Updated LatLong data will appear in the corresponding Google Sheet.
  Review the Results:
     Use the enriched LatLong data for mapping or analysis.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets"]

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name: Basic Automatic Gmail Email Labelling with OpenAI and Gmail API
author_name: Martijn Kerver
author_username: mkc
categories: []
created_at: 2025-01-17T08:51:32.434Z
updated_at: 2025-01-17T08:51:32.434Z
url: https://n8n.io/workflows/2740
scraped_at: 2025-12-24 13:02:44
description:
  Description
  This workflow automates email categorization in Gmail using the Gmail API and OpenAI's language model. It periodically checks for new emails, reads their content, and categorizes them based on existing Gmail labels. If no matching label is found, the workflow creates a new label and assigns it to the email.

  Key Features
  Polling for Emails**: The workflow triggers every 5 minutes to check for new emails using the Gmail Trigger node.
  Reading Labels**: Existing Gmail labels are fetched to determine the most relevant match for email categorization.
  Dynamic Labeling**: If no existing label matches, a new label is created dynamically based on the email's content.
  OpenAI Integration**: The workflow uses OpenAI's Chat model to analyze email content and suggest or create appropriate labels.
  Email Categorization**: Labels are applied to emails, ensuring they are organized in Gmail's structure. The workflow also removes less relevant emails (e.g., ads) from the inbox.

  Nodes in Use
  Gmail Trigger: Polls Gmail every 5 minutes for new emails.
  Gmail - Read Labels: Fetches all existing Gmail labels.
  Gmail - Get Message: Retrieves the full content of a specific email.
  Gmail - Add Label to Message: Assigns a chosen label to the email.
  Gmail - Create Label: Creates a new label if necessary.
  OpenAI Chat Model: Analyzes email content for categorization.
  Memory Buffer: Retains context for email analysis across multiple iterations.
  Wait Node: Adds a buffer period to manage email processing.

  Prerequisites
  Gmail API Setup**: Ensure Gmail OAuth2 credentials are configured in n8n.
  OpenAI API Key**: Configure OpenAI credentials for email analysis.
  Labeling Standards**: Maintain a consistent Gmail label structure for better organization.

  Instructions
  Add your Gmail API credentials to the Gmail nodes.
  Add your OpenAI API credentials to the OpenAI Chat Model node.
  Activate the workflow. It will start polling for new emails every 5 minutes.
  Monitor and refine the categorization logic if necessary to ensure alignment with Gmail's organizational needs.

  Use Case
  Ideal for individuals or teams handling high email volumes who want to maintain an organized inbox and automate repetitive categorization tasks.

  Note: You can improve the prompt to get better results from the agent by giving it more personal rules on how to categorize.

nodes_used:
  ["n8n-nodes-base.gmailTrigger","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.gmailTool","n8n-nodes-base.gmailTool","n8n-nodes-base.gmailTool","n8n-nodes-base.gmailTool","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.wait","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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name: AI-Powered RAG Workflow For Stock Earnings Report Analysis
author_name: Mihai Farcas
author_username: mihailtd
categories: []
created_at: 2025-01-17T09:45:18.164Z
updated_at: 2025-01-17T09:45:18.164Z
url: https://n8n.io/workflows/2741
scraped_at: 2025-12-24 13:02:43
description:
  This n8n workflow creates a financial analysis tool that generates reports on a company's quarterly earnings using the capabilities of OpenAI GPT-4o-mini, Google's Gemini AI and Pinecone's vector search. By analyzing PDFs of any company's earnings reports from their Investor Relations page, this workflow can answer complex financial questions and automatically compile findings into a structured Google Doc.

  How it works:
  Data loading and indexing
  Fetches links to PDF earnings document from a Google Sheet containing a list of file links.
  Downloads the PDFs from Google Drive.
  Parses the PDFs, splits the text into chunks, and generates embeddings using the Embeddings Google AI node (text-embedding-004 model).
  Stores the embeddings and corresponding text chunks in a Pinecone vector database for semantic search.

  Report generation with AI agent
  Utilizes an AI Agent node with a specifically crafted system prompt. The agent orchestrates the entire process.
  The agent uses a Vector Store Tool to access and retrieve information from the Pinecone database.
  Report delivery
  Saves the generated report as a Google Doc in a specified Google Drive location.

  Set up steps
  Google Cloud Project & Vertex AI API:
  	Create a Google Cloud project.
  	Enable the Vertex AI API for your project.
  Google AI API key:
  	Obtain a Google AI API key from Google AI Studio.
  Pinecone account and API key:
  	Create a free account on the Pinecone website.
  	Obtain your API key from your Pinecone dashboard.
  	Create an index named company-earnings in your Pinecone project.
  Google Drive - download and save financial documents:
  	Go to a company you want to analize and download their quarterly earnings PDFs
  	Save the PDFs in Google Drive
  	Create a Google Sheet that stores a list of file URLs pointing to the PDFs you downloaded and saved to Google Drive
  Configure credentials in your n8n environment for:
  	Google Sheets OAuth2
  	Google Drive OAuth2
  	Google Docs OAuth2
  	Google Gemini(PaLM) Api (using your Google AI API key)
  	Pinecone API (using your Pinecone API key)
  Import and configure the workflow:
  	Import this workflow into your n8n instance.
  	Update the List Of Files To Load (Google Sheets) node to point to your Google Sheet.
  	Update the Download File From Google Drive to point to the column where the file URLs are
  	Update the Save Report to Google Docs node to point to your Google Doc where you want the report saved.

nodes_used:
  ["@n8n/n8n-nodes-langchain.vectorStorePinecone","@n8n/n8n-nodes-langchain.embeddingsGoogleGemini","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter","n8n-nodes-base.splitInBatches","n8n-nodes-base.manualTrigger","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.toolVectorStore","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.vectorStorePinecone","n8n-nodes-base.googleDocs","@n8n/n8n-nodes-langchain.embeddingsGoogleGemini","n8n-nodes-base.googleSheets","n8n-nodes-base.googleDrive","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 5796 characters]


name: Post Hourly Crypto Market Summaries via Coingecko to X and to Email
author_name: Badr
author_username: b4dr
categories: []
created_at: 2025-01-17T11:18:56.845Z
updated_at: 2025-01-17T11:18:56.845Z
url: https://n8n.io/workflows/2746
scraped_at: 2025-12-24 13:02:47
description:
  Description  
  This workflow, delivers real-time cryptocurrency market updates (default: Bitcoin) by fetching data from the CoinGecko API. It formats the information into a visually engaging message and shares it on X (formerly Twitter) and via email. The workflow is set to trigger hourly but is fully customizable to suit different schedules or cryptocurrencies.

  Key Features  
  Real-Time Market Data Retrieval:** Fetches up-to-date cryptocurrency market data using CoinGecko API.  
  AI-Driven Message Formatting:** Converts raw data into a structured, engaging message.  
  Social Media Integration:** Posts updates to X with OAuth2 authentication.  
  Email Notifications:** Sends updates via email to specified recipients.  
  Flexible Customization:** Adaptable for tracking multiple cryptocurrencies or adding new notification channels.  

  Workflow Steps  
  Trigger Workflow: Default is hourly, but the interval can be customized.  
  Fetch Cryptocurrency Data: Retrieves market data using the CoinGecko API.  
  Format the Message: Converts the data into a readable and visually appealing format.  
  Post Update on X: Shares the message on X (formerly Twitter).  
  Send Email Notifications: Sends the update to a specified email address.  
  Optional Notifications: Expand to additional channels like Slack or Telegram.

nodes_used:
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name: 🎨 Interactive Image Editor with FLUX.1 Fill Tool for Inpainting
author_name: Eduard
author_username: eduard
categories: []
created_at: 2025-01-17T12:45:15.799Z
updated_at: 2025-01-17T12:45:15.799Z
url: https://n8n.io/workflows/2747
scraped_at: 2025-12-24 13:02:43
description:
  &gt; Like this template? Connect with Eduard via LinkedIn.

  This workflow is a prototype of an AI-powered image editing interface, similar to Photoshop's Generative Fill feature, but running entirely in the browser. It provides a web-based editor that allows users to:

  Select areas in images using an adjustable brush tool
  Input text prompts to guide the AI generation
  Compare original and generated images side by side
  Iterate on edits with different prompts and settings
  Save or reuse generated images

  &gt; 🎨 Perfect for product catalog management, seasonal content updates, and creative image editing tasks!

  📋 Requirements
  FLUX API Access: You'll need API credentials from FLUX to use this workflow.
  Configure the HTTP Header Auth credential in n8n with your FLUX API key


  🔧 Key Components
  FLUX Fill API for AI-powered image generation
  Konva.js for canvas manipulation
  img-comparison-slider for result visualization
  Custom CSS/JS for editor functionality


  Simple Editor Interface
     HTML page with an editor is served on the Webhook call
     Adjustable brush selection tool
     Provides several mock examples and allows uploading custom images
     Basic prompt and FLUX model parameter controls

  Image Processing Pipeline
     Handles image and mask separately
     Processes FLUX Fill API requests
     Delivers results back to the editor

  Result Viewer
     Split-screen comparison of original and generated images
     Interactive slider for before/after comparison
     Options to save or continue editing
     Support for multiple iteration cycles

  🎯 Use Cases
  This prototype is particularly useful for:

  Testing AI-powered image editing concepts
  Quick product visualization experiments
  Exploring creative image variations
  Demonstrating inpainting capabilities

  &gt; 💡 Pro Tip: Save masks for frequently edited areas to quickly generate variations with different prompts!

  The workflow can be extended to integrate with various data sources and can be customized for specific business needs.

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name: Proxmox AI Agent with n8n and Generative AI Integration
author_name: Amjid Ali
author_username: amjid
categories: []
created_at: 2025-01-18T14:13:14.250Z
updated_at: 2025-01-18T14:13:14.250Z
url: https://n8n.io/workflows/2749
scraped_at: 2025-12-24 13:02:42
description:
  Proxmox AI Agent with n8n and Generative AI Integration

  This template automates IT operations on a Proxmox Virtual Environment (VE) using an AI-powered conversational agent built with n8n. By integrating Proxmox APIs and generative AI models (e.g., Google Gemini), the workflow converts natural language commands into API calls, enabling seamless management of your Proxmox nodes, VMs, and clusters.
  Buy My Book:  
  Mastering n8n on Amazon

  Full Courses & Tutorials:  
  http://lms.syncbricks.com

  Watch Video on Youtube

  How It Works  
  Trigger Mechanism  
     The workflow can be triggered through multiple channels like chat (Telegram, email, or n8n's built-in chat).  
     Interact with the AI agent conversationally.

  AI-Powered Parsing  
     A connected AI model (Google Gemini or other compatible models like OpenAI or Claude) processes your natural language input to determine the required Proxmox API operation.

  API Call Generation  
     The AI parses the input and generates structured JSON output, which includes:  
       response_type: The HTTP method (GET, POST, PUT, DELETE).  
       url: The Proxmox API endpoint to execute.  
       details: Any required payload parameters for the API call.

  Proxmox API Execution  
     The structured output is used to make HTTP requests to the Proxmox VE API. The workflow supports various operations, such as:  
       Retrieving cluster or node information.  
       Creating, deleting, starting, or stopping VMs.  
       Migrating VMs between nodes.  
       Updating or resizing VM configurations.

  Response Formatting  
     The workflow formats API responses into a user-friendly summary. For example:  
       Success messages for operations (e.g., "VM started successfully").  
       Error messages with missing parameter details.

  Extensibility  
     You can enhance the workflow by connecting additional triggers, external services, or AI models. It supports:  
       Telegram/Slack integration for real-time notifications.  
       Backup and restore workflows.  
       Cloud monitoring extensions.

  Key Features  
  Multi-Channel Input**: Use chat, email, or custom triggers to communicate with the AI agent.  
  Low-Code Automation**: Easily customize the workflow to suit your Proxmox environment.  
  Generative AI Integration**: Supports advanced AI models for precise command interpretation.  
  Proxmox API Compatibility**: Fully adheres to Proxmox API specifications for secure and reliable operations.  
  Error Handling**: Detects and informs you of missing or invalid parameters in your requests.  

  Example Use Cases  
  Create a Virtual Machine  
     Input: "Create a VM with 4 cores, 8GB RAM, and 50GB disk on psb1."  
     Action: Sends a POST request to Proxmox to create the VM with specified configurations.

  Start a VM  
     Input: "Start VM 105 on node psb2."  
     Action: Executes a POST request to start the specified VM.

  Retrieve Node Details  
     Input: "Show the memory usage of psb3."  
     Action: Sends a GET request and returns the node's resource utilization.

  Migrate a VM  
     Input: "Migrate VM 202 from psb1 to psb3."  
     Action: Executes a POST request to move the VM with optional online migration.

  Pre-Requisites  
  Proxmox API Configuration  
     Enable the Proxmox API and generate API keys in the Proxmox Data Center.  
     Use the Authorization header with the format:  
       PVEAPIToken=&lt;user&gt;@&lt;realm&gt;!&lt;token-id&gt;=&lt;token-value&gt;

  n8n Setup  
     Add Proxmox API credentials in n8n using Header Auth.  
     Connect a generative AI model (e.g., Google Gemini) via the relevant credential type.

  Access the Workflow  
     Import this template into your n8n instance.  
     Replace placeholder credentials with your Proxmox and AI service details.

  Additional Notes  
  This template is designed for Proxmox 7.x and above.  
  For advanced features like backup, VM snapshots, and detailed node monitoring, you can extend this workflow.  
  Always test with a non-production Proxmox environment before deploying in live systems.

  Start with n8n  

  Learn n8n with Amjid   
  Get n8n Book 

  What is Proxmox

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name: 🤖 Telegram Messaging Agent for Text/Audio/Images
author_name: Joseph LePage
author_username: joe
categories: []
created_at: 2025-01-18T18:10:46.426Z
updated_at: 2025-01-18T18:10:46.426Z
url: https://n8n.io/workflows/2751
scraped_at: 2025-12-24 13:02:41
description:
  🤖 This n8n workflow creates an intelligent Telegram bot that processes multiple types of messages and provides automated responses using AI capabilities. The bot serves as a personal assistant that can handle text, voice messages, and images through a sophisticated processing pipeline.

  Core Components

  Message Reception and Validation 📥
  🔄 Implements webhook-based message reception for real-time processing
  🔐 Features a robust user validation system that verifies sender credentials
  🔀 Supports both testing and production webhook endpoints for development flexibility

  Message Processing Pipeline ⚡
  🔄 Uses a smart router to detect and categorize incoming message types
  📝 Processes three main message formats:
    💬 Text messages
    🎤 Voice recordings
    📸 Images with captions

  AI Integration 🧠
  🤖 Leverages OpenAI's GPT-4 for message classification and processing
  🗣️ Incorporates voice transcription capabilities for audio messages
  👁️ Features image analysis using GPT-4 Vision API for processing visual content

  Technical Architecture

  Webhook Management 🔌
  🌐 Maintains separate endpoints for testing and production environments
  📊 Implements automatic webhook status monitoring
  ⚡ Provides real-time webhook configuration updates

  Error Handling ⚠️
  🔍 Features comprehensive error detection and reporting
  🔄 Implements fallback mechanisms for unprocessable messages
  💬 Provides user feedback for failed operations

  Message Classification System 📋
  🏷️ Categorizes incoming messages into tasks and general conversation
  🔀 Implements separate processing paths for different message types
  🧩 Maintains context awareness across message processing

  Security Features

  User Authentication 🔒
  ✅ Validates user credentials against predefined parameters
  👤 Implements first name, last name, and user ID verification
  🚫 Restricts access to authorized users only

  Response System

  Intelligent Responses 💡
  🤖 Generates contextual responses based on message classification

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