{
"id": "h2uiciRa1D3ntSTT",
"meta": {
"instanceId": "ddfdf733df99a65c801a91865dba5b7c087c95cc22a459ff3647e6deddf2aee6"
},
"name": "My workflow",
"tags": [],
"nodes": [
{
"id": "4b885b7d-0976-4dd3-bc1c-091ab0dff437",
"name": "Split Topics into Items",
"type": "n8n-nodes-base.code",
"position": [
420,
420
],
"parameters": {
"jsCode": "\/\/ Input data (from $json.Topics)\nconst topicsString = $json.Topics;\n\n\/\/ Split the string by newlines and trim whitespace\nconst topicsArray = topicsString.split('\\n').map(topic => topic.trim());\n\n\/\/ Create an array of items for each topic\nconst items = topicsArray.map(topic => {\n return { json: { Topic: topic } };\n});\n\n\/\/ Output the new array of items\nreturn items;\n"
},
"typeVersion": 2
},
{
"id": "935d0266-feda-48cb-b441-b4da19d8b163",
"name": "Search Posts",
"type": "n8n-nodes-base.reddit",
"position": [
620,
420
],
"parameters": {
"keyword": "meta",
"location": "allReddit",
"operation": "search",
"returnAll": true,
"additionalFields": {
"sort": "hot"
}
},
"typeVersion": 1
},
{
"id": "cea577c8-c025-4132-926a-74d6946d81b8",
"name": "Upvotes Requirement Filtering",
"type": "n8n-nodes-base.if",
"position": [
800,
420
],
"parameters": {
"options": [],
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "f767f7a8-a2e8-4566-be80-bd735249e069",
"operator": {
"type": "number",
"operation": "gt"
},
"leftValue": "={{ $json.ups }}",
"rightValue": 100
},
{
"id": "3af82bef-5a78-4e6e-91ef-a5bd0141c87f",
"operator": {
"name": "filter.operator.equals",
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $json.post_hint }}",
"rightValue": "link"
},
{
"id": "980a84ed-d640-47a7-b49a-bf638e811f20",
"operator": {
"type": "string",
"operation": "notContains"
},
"leftValue": "={{ $json.url }}",
"rightValue": "bsky.app"
}
]
}
},
"typeVersion": 2.20000000000000017763568394002504646778106689453125
},
{
"id": "eec2d833-9a63-4cf6-a6bd-56b300ede5e0",
"name": "Set Reddit Posts",
"type": "n8n-nodes-base.set",
"position": [
1040,
420
],
"parameters": {
"options": [],
"assignments": {
"assignments": [
{
"id": "8d5ae4fa-2f54-48d7-8f61-766f4ecf9d96",
"name": "Title",
"type": "string",
"value": "={{ $json.title }}"
},
{
"id": "8eb33a06-d8e7-4eea-bcd3-f956e20e06e6",
"name": "Subreddit",
"type": "string",
"value": "={{ $json.subreddit }}"
},
{
"id": "5ff8c76e-a8d5-4f76-a7d0-faa69b7960e4",
"name": "Upvotes",
"type": "string",
"value": "={{ $json.ups }}"
},
{
"id": "05a2b453-0e29-4a81-8f10-5934ae721f64",
"name": "Comments",
"type": "string",
"value": "={{ $json.num_comments }}"
},
{
"id": "78f73e89-19a7-4dd5-9db0-ead55dfd5606",
"name": "Reddit URL",
"type": "string",
"value": "=https:\/\/www.reddit.com{{ $json.permalink }}"
},
{
"id": "6f92bce7-2dc5-4dfd-b216-efc12c5411bb",
"name": "URL",
"type": "string",
"value": "={{ $json.url }}"
},
{
"id": "0b20d78c-1d6b-4c84-99ef-978ee39fd35e",
"name": "Is_URL",
"type": "string",
"value": "={{ $json.post_hint }}"
},
{
"id": "489807f6-25ef-47d5-bd47-711ca75dedea",
"name": "Date",
"type": "string",
"value": "={{ new Date($json.created * 1000).toISOString().split('T')[0] }}"
},
{
"id": "0a9fb817-bfb7-4ea7-9182-1eddc404035f",
"name": "Post ID",
"type": "string",
"value": "={{ $json.id }}"
}
]
}
},
"typeVersion": 3.399999999999999911182158029987476766109466552734375
},
{
"id": "9b45abb0-866a-47f4-b2b3-03e4cf41c988",
"name": "Remove Duplicates",
"type": "n8n-nodes-base.code",
"position": [
1220,
420
],
"parameters": {
"jsCode": "\/\/ Get all input items\nconst inputItems = $input.all();\n\n\/\/ Create a Map to store the most upvoted item for each URL\nconst uniqueItemsMap = new Map();\n\nfor (const item of inputItems) {\n const url = item.json.URL;\n \n \/\/ Skip items where URL contains \"redd.it\"\n if (url && url.includes(\"redd.it\")) {\n continue;\n }\n \n const upvotes = parseInt(item.json.Upvotes, 10) || 0; \/\/ Ensure upvotes is a number\n\n if (!uniqueItemsMap.has(url)) {\n \/\/ Add the first occurrence of the URL\n uniqueItemsMap.set(url, item);\n } else {\n \/\/ Compare upvotes and keep the item with the most upvotes\n const existingItem = uniqueItemsMap.get(url);\n const existingUpvotes = parseInt(existingItem.json.Upvotes, 10) || 0;\n if (upvotes > existingUpvotes) {\n uniqueItemsMap.set(url, item);\n }\n }\n}\n\n\/\/ Extract all unique items\nconst uniqueItems = Array.from(uniqueItemsMap.values());\n\n\/\/ Return each unique item as a separate output\nreturn uniqueItems;"
},
"typeVersion": 2
},
{
"id": "39672fd4-3f8c-4cdb-acd5-bb862ae5eddd",
"name": "Loop Over Items",
"type": "n8n-nodes-base.splitInBatches",
"position": [
40,
660
],
"parameters": {
"options": []
},
"typeVersion": 3
},
{
"id": "ad70aec7-a610-42f8-b87c-0d3dbee00e7b",
"name": "Get Comments",
"type": "n8n-nodes-base.reddit",
"position": [
480,
640
],
"parameters": {
"postId": "={{ $json[\"Post ID\"] }}",
"resource": "postComment",
"operation": "getAll",
"subreddit": "={{ $json.Subreddit }}"
},
"typeVersion": 1
},
{
"id": "af7f0b35-4250-49e5-afa7-608155df0fd5",
"name": "Extract Top Comments",
"type": "n8n-nodes-base.code",
"position": [
660,
640
],
"parameters": {
"jsCode": "\/**\n * n8n Code Node for filtering top 30 Reddit-style comments by score\/ups\n * and ensuring replies are included in the comment tree.\n * Excludes deleted comments.\n *\/\n\n\/\/ Get all input items\nconst inputItems = $input.all();\nconst commentsArray = inputItems.flatMap(item => item.json);\n\n\/**\n * Checks if a comment is deleted.\n * @param {Object} commentObj - The comment to check.\n * @returns {boolean} - True if the comment is deleted, false otherwise.\n *\/\nfunction isDeletedComment(commentObj) {\n return commentObj.author === \"[deleted]\" && commentObj.body === \"[removed]\";\n}\n\n\/\/ Function to recursively flatten a comment and its replies\nfunction flattenCommentTree(commentObj) {\n \/\/ Skip deleted comments\n if (isDeletedComment(commentObj)) {\n return null;\n }\n\n const { body, ups, score, replies, author } = commentObj;\n\n \/\/ Calculate score\n const finalScore = typeof ups === 'number' ? ups : (score || 0);\n\n \/\/ Process comment\n const flatComment = {\n body: body || '',\n score: finalScore,\n author: author || 'Unknown',\n replies: [],\n };\n\n \/\/ Process replies\n if (\n replies &&\n replies.data &&\n Array.isArray(replies.data.children)\n ) {\n flatComment.replies = replies.data.children\n .filter(child => child.kind === 't1' && child.data)\n .map(child => flattenCommentTree(child.data)) \/\/ Recursively flatten replies\n .filter(reply => reply !== null); \/\/ Filter out null replies (deleted comments)\n }\n\n return flatComment;\n}\n\n\/\/ Flatten all comments, preserving hierarchy\nconst allComments = commentsArray\n .map(flattenCommentTree)\n .filter(comment => comment !== null); \/\/ Filter out null comments (deleted comments)\n\n\/\/ Flatten the hierarchy to a list for scoring and filtering\nfunction flattenForScoring(tree) {\n const result = [];\n tree.forEach(comment => {\n result.push(comment); \/\/ Add current comment\n if (comment.replies && comment.replies.length > 0) {\n result.push(...flattenForScoring(comment.replies)); \/\/ Add replies recursively\n }\n });\n return result;\n}\n\n\/\/ Flatten the hierarchy and sort by score\nconst flatList = flattenForScoring(allComments);\nflatList.sort((a, b) => b.score - a.score);\n\n\/\/ Select the top 30 comments\nconst top30 = flatList.slice(0, 30);\n\n\/\/ Rebuild the hierarchy from the top 30\nfunction filterHierarchy(tree, allowedBodies) {\n return tree\n .filter(comment => allowedBodies.has(comment.body))\n .map(comment => ({\n ...comment,\n replies: filterHierarchy(comment.replies || [], allowedBodies), \/\/ Recurse for replies\n }));\n}\n\nconst allowedBodies = new Set(top30.map(comment => comment.body));\nconst filteredHierarchy = filterHierarchy(allComments, allowedBodies);\n\n\/\/ Return in n8n format\nreturn [\n {\n json: {\n comments: filteredHierarchy,\n },\n },\n];"
},
"executeOnce": true,
"typeVersion": 2
},
{
"id": "e709d131-b8fa-42d5-bc66-479cb13574e6",
"name": "Format Comments",
"type": "n8n-nodes-base.code",
"position": [
840,
640
],
"parameters": {
"jsCode": "\/**\n * Convert comments data into Markdown format with accurate hierarchy visualization.\n * Excludes deleted comments.\n *\/\n\n\/\/ Input data (replace this with your actual comments data)\nconst data = $input.all()[0].json.comments;\n\n\/**\n * Checks if a comment is deleted.\n * @param {Object} comment - The comment to check.\n * @returns {boolean} - True if the comment is deleted, false otherwise.\n *\/\nfunction isDeletedComment(comment) {\n return comment.author === \"[deleted]\" && comment.body === \"[removed]\";\n}\n\n\/**\n * Filters out deleted comments and their replies.\n * @param {Array} comments - Array of comments.\n * @returns {Array} - Filtered array of comments.\n *\/\nfunction filterDeletedComments(comments) {\n if (!comments || !comments.length) return [];\n \n return comments\n .filter(comment => !isDeletedComment(comment))\n .map(comment => {\n if (comment.replies && comment.replies.length > 0) {\n comment.replies = filterDeletedComments(comment.replies);\n }\n return comment;\n });\n}\n\n\/**\n * Recursive function to format comments and replies into Markdown.\n * @param {Array} comments - Array of comments.\n * @param {number} level - Current level of the comment hierarchy for indentation.\n * @returns {string} - Formatted Markdown string.\n *\/\nfunction formatCommentsToMarkdown(comments, level = 0) {\n let markdown = '';\n const indent = ' '.repeat(level); \/\/ Indentation for replies\n\n for (const comment of comments) {\n \/\/ Format the main comment\n markdown += `${indent}- **Author**: ${comment.author}\\n`;\n markdown += `${indent} **Score**: ${comment.score}\\n`;\n markdown += `${indent} **Comment**:\\n\\n`;\n markdown += `${indent} > ${comment.body.replace(\/\\n\/g, `\\n${indent} > `)}\\n\\n`;\n\n \/\/ Process replies if they exist\n if (comment.replies && comment.replies.length > 0) {\n markdown += `${indent} **Replies:**\\n\\n`;\n markdown += formatCommentsToMarkdown(comment.replies, level + 1);\n }\n }\n\n return markdown;\n}\n\n\/\/ Filter out deleted comments first\nconst filteredData = filterDeletedComments(data);\n\n\/\/ Generate the Markdown\nconst markdownOutput = formatCommentsToMarkdown(filteredData);\n\n\/\/ Return the Markdown as an output for n8n\nreturn [\n {\n json: {\n markdown: markdownOutput,\n },\n },\n];"
},
"typeVersion": 2
},
{
"id": "284d511b-7d80-46ba-add0-6ff59aff176c",
"name": "Set for Loop",
"type": "n8n-nodes-base.set",
"position": [
280,
640
],
"parameters": {
"options": [],
"assignments": {
"assignments": [
{
"id": "ac7c257d-544f-44e5-abc6-d0436f12517f",
"name": "Title",
"type": "string",
"value": "={{ $json.Title }}"
},
{
"id": "fb22c6a5-a809-4588-9f6e-49c3e11f5ed2",
"name": "Subreddit",
"type": "string",
"value": "={{ $json.Subreddit }}"
},
{
"id": "4bfcc849-539b-48cd-856f-1b7f3be113ed",
"name": "Upvotes",
"type": "string",
"value": "={{ $json.Upvotes }}"
},
{
"id": "9a3a3a2a-8f43-4419-9203-bc83f5b0c0bc",
"name": "Comments",
"type": "string",
"value": "={{ $json.Comments }}"
},
{
"id": "2d31f321-fbdc-43d3-8a92-a78f418f112f",
"name": "Reddit URL",
"type": "string",
"value": "={{ $json[\"Reddit URL\"] }}"
},
{
"id": "f224323a-79ef-4f66-ae10-d77c8fddbccd",
"name": "URL",
"type": "string",
"value": "={{ $json.URL }}"
},
{
"id": "dbbc5a98-b5e2-45bb-bc18-2c438522d683",
"name": "Date",
"type": "string",
"value": "={{ $json.Date }}"
},
{
"id": "837cae4e-858a-48ba-bab9-bb66a2e51837",
"name": "Post ID",
"type": "string",
"value": "={{ $json[\"Post ID\"] }}"
}
]
}
},
"typeVersion": 3.399999999999999911182158029987476766109466552734375
},
{
"id": "b88fad49-edc4-4749-8984-a8e81f6a2899",
"name": "Get News Content",
"type": "n8n-nodes-base.httpRequest",
"maxTries": 5,
"position": [
1360,
640
],
"parameters": {
"url": "=https:\/\/r.jina.ai\/{{ $('Set for Loop').first().json.URL }}",
"options": [],
"sendHeaders": true,
"headerParameters": {
"parameters": [
{
"name": "Accept",
"value": "text\/event-stream"
},
{
"name": "Authorization",
"value": "=Bearer {{ $('Set Data').first().json['Jina API Key'] }}"
},
{
"name": "X-Retain-Images",
"value": "none"
},
{
"name": "X-Respond-With",
"value": "readerlm-v2"
},
{
"name": "X-Remove-Selector",
"value": "header, footer, sidebar"
}
]
}
},
"retryOnFail": true,
"typeVersion": 4.20000000000000017763568394002504646778106689453125,
"waitBetweenTries": 5000
},
{
"id": "26a8906c-2966-4ebf-8465-18a48b359f7d",
"name": "Set Final Report",
"type": "n8n-nodes-base.set",
"position": [
2400,
640
],
"parameters": {
"options": [],
"assignments": {
"assignments": [
{
"id": "0782b9a6-d659-4695-8696-6ff0e574f77a",
"name": "Final Report",
"type": "string",
"value": "=\/\/ Reddit Metrics:\nPost Link: {{ $('Set for Loop').first().json['Reddit URL'] }}\nUpvotes: {{ $('Set for Loop').first().json.Upvotes }}\nComments: {{ $('Set for Loop').first().json.Comments }}\n\n# FINAL REPORT\n{{ $json.text.replace(\/[\\s\\S]*<new_stories_report>\/, '').replace(\/<\\\/new_stories_report>[\\s\\S]*\/, '') }}\n\n# RAW ANALYSIS DATA (FOR FURTHER ANALYSIS)\n\n## NEWS CONTENT ANALYSIS\n{{ $('News Analysis').item.json.text.replace(\/[\\s\\S]*<news_analysis>\/, '').replace(\/<\\\/news_analysis>[\\s\\S]*\/, '') }}\n\n## REDDIT COMMENTS ANALYSIS\n{{ $('Comments Analysis').first().json.text.replace(\/[\\s\\S]*<comments_analysis>\/, '').replace(\/<\\\/comments_analysis>[\\s\\S]*\/, '') }}"
}
]
}
},
"typeVersion": 3.399999999999999911182158029987476766109466552734375
},
{
"id": "219ccb20-1b36-4c70-866a-0fded9c9b9fd",
"name": "Convert to File",
"type": "n8n-nodes-base.convertToFile",
"position": [
2580,
640
],
"parameters": {
"options": {
"encoding": "utf8",
"fileName": "={{ $json[\"Final Report\"].match(\/Headline:\\s*[\"\u201c](.*?)[\"\u201d]\/i)?.[1] }}.txt"
},
"operation": "toText",
"sourceProperty": "Final Report"
},
"typeVersion": 1.100000000000000088817841970012523233890533447265625
},
{
"id": "427d5a2d-6927-4427-9902-e033736410ca",
"name": "Compress files",
"type": "n8n-nodes-base.compression",
"position": [
600,
940
],
"parameters": {
"fileName": "=Trending_Stories_{{$now.format(\"yyyy_MM_dd\")}}_{{Math.floor(Math.random() * 10000).toString().padStart(4, '0')}}.zip",
"operation": "compress",
"outputFormat": "zip",
"binaryPropertyName": "={{ $json[\"binary_keys\"] }}",
"binaryPropertyOutput": "files_combined"
},
"typeVersion": 1
},
{
"id": "7f6ef656-0f76-433f-95a8-782de21caa53",
"name": "Merge Binary Files",
"type": "n8n-nodes-base.code",
"position": [
420,
940
],
"parameters": {
"jsCode": "\/\/ Get the first (and only) item since you're using Aggregate\nconst item = items[0];\nlet binary_keys = [];\n\n\/\/ Generate the list of binary keys from your aggregated item\nfor (let key in item.binary) {\n binary_keys.push(key);\n}\n\nreturn [{\n json: {\n binary_keys: binary_keys.join(',')\n },\n binary: item.binary \/\/ Keep the original binary data\n}];"
},
"executeOnce": true,
"typeVersion": 2
},
{
"id": "20411444-5ce8-452b-869c-97928200b205",
"name": "Google Drive6",
"type": "n8n-nodes-base.googleDrive",
"position": [
780,
940
],
"parameters": {
"driveId": {
"__rl": true,
"mode": "list",
"value": "My Drive",
"cachedResultUrl": "https:\/\/drive.google.com\/drive\/my-drive",
"cachedResultName": "My Drive"
},
"options": [],
"folderId": {
"__rl": true,
"mode": "id",
"value": "1HCTq5YupRHcgRd7FIlSeUMMjqqOZ4Q9x"
},
"inputDataFieldName": "files_combined"
},
"typeVersion": 3
},
{
"id": "2eb8112a-8655-4f06-998f-a9ffef74d72a",
"name": "Google Drive7",
"type": "n8n-nodes-base.googleDrive",
"position": [
960,
940
],
"parameters": {
"fileId": {
"__rl": true,
"mode": "id",
"value": "={{ $json.id }}"
},
"options": [],
"operation": "share",
"permissionsUi": {
"permissionsValues": {
"role": "reader",
"type": "anyone"
}
}
},
"typeVersion": 3
},
{
"id": "7f4e5e0c-49cc-4024-b62b-f7e099d4867d",
"name": "Send files to Mattermost3",
"type": "n8n-nodes-base.httpRequest",
"position": [
1140,
940
],
"parameters": {
"url": "https:\/\/team.YOUR_DOMAIN.com\/hooks\/REPLACE_THIS_WITH_YOUR_HOOK_ID",
"method": "POST",
"options": [],
"jsonBody": "={\n \"channel\": \"digital-pr\",\n \"username\": \"NotifyBot\",\n \"icon_url\": \"https:\/\/team.YOUR_DOMAIN.com\/api\/v4\/users\/YOUR_USER_ID\/image?_=0\",\n \"text\": \"@channel New trending stories have been generated \ud83c\udf89\\n\\n\\n You can download it here: https:\/\/drive.google.com\/file\/d\/{{ $('Google Drive6').item.json.id }}\/view?usp=drive_link\"\n}",
"sendBody": true,
"specifyBody": "json"
},
"typeVersion": 4.20000000000000017763568394002504646778106689453125
},
{
"id": "3c47f58d-8006-4565-b220-033d71239126",
"name": "Aggregate",
"type": "n8n-nodes-base.aggregate",
"position": [
260,
940
],
"parameters": {
"options": {
"includeBinaries": true
},
"aggregate": "aggregateAllItemData"
},
"executeOnce": false,
"typeVersion": 1
},
{
"id": "5611cdce-91ae-4037-9479-3b513eb07b77",
"name": "Schedule Trigger",
"type": "n8n-nodes-base.scheduleTrigger",
"position": [
40,
420
],
"parameters": {
"rule": {
"interval": [
{
"field": "weeks",
"triggerAtDay": [
1
],
"triggerAtHour": 6
}
]
}
},
"typeVersion": 1.1999999999999999555910790149937383830547332763671875
},
{
"id": "5cfeb9ea-45b6-4a0a-8702-34539738f280",
"name": "Anthropic Chat Model",
"type": "@n8n\/n8n-nodes-langchain.lmChatAnthropic",
"position": [
960,
800
],
"parameters": {
"model": "=claude-3-7-sonnet-20250219",
"options": {
"temperature": 0.5,
"maxTokensToSample": 8096
}
},
"typeVersion": 1.1999999999999999555910790149937383830547332763671875
},
{
"id": "b11b2fa6-f92a-4791-b255-51ce1b07181b",
"name": "Anthropic Chat Model1",
"type": "@n8n\/n8n-nodes-langchain.lmChatAnthropic",
"position": [
1640,
800
],
"parameters": {
"model": "=claude-3-7-sonnet-20250219",
"options": {
"temperature": 0.5,
"maxTokensToSample": 8096
}
},
"typeVersion": 1.1999999999999999555910790149937383830547332763671875
},
{
"id": "ffa45242-1dd4-46be-bacc-55bde63d0227",
"name": "Keep Last",
"type": "n8n-nodes-base.code",
"position": [
1540,
640
],
"parameters": {
"jsCode": "\/\/ Extract input data from n8n\nconst inputData = $json.data;\n\n\/\/ Ensure input is valid\nif (!inputData || typeof inputData !== 'string') {\n return [{ error: \"Invalid input data\" }];\n}\n\n\/\/ Split the data into lines\nlet lines = inputData.split(\"\\n\");\n\n\/\/ Extract only JSON entries\nlet jsonEntries = lines\n .map(line => line.trim()) \/\/ Remove spaces\n .filter(line => line.startsWith('data: {')) \/\/ Keep valid JSON objects\n .map(line => line.replace('data: ', '')); \/\/ Remove the prefix\n\n\/\/ Ensure there are entries\nif (jsonEntries.length === 0) {\n return [{ error: \"No valid JSON entries found\" }];\n}\n\n\/\/ Get only the LAST entry\nlet lastEntry = jsonEntries[jsonEntries.length - 1];\n\ntry {\n \/\/ Parse the last entry as JSON\n let jsonObject = JSON.parse(lastEntry);\n\n \/\/ Extract title and content\n return [{\n title: jsonObject.title || \"No Title\",\n content: jsonObject.content || \"No Content\"\n }];\n} catch (error) {\n return [{ error: \"JSON parsing failed\", raw: lastEntry }];\n}"
},
"typeVersion": 2
},
{
"id": "956672cc-8ceb-4a2c-93e8-bad2b9497043",
"name": "Anthropic Chat Model2",
"type": "@n8n\/n8n-nodes-langchain.lmChatAnthropic",
"position": [
1980,
800
],
"parameters": {
"model": "=claude-3-7-sonnet-20250219",
"options": {
"temperature": 0.5,
"maxTokensToSample": 8096
}
},
"typeVersion": 1.1999999999999999555910790149937383830547332763671875
},
{
"id": "b55df80f-dbdf-4d8d-8b62-93533d1fb6ef",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
0,
0
],
"parameters": {
"width": 1020,
"height": 340,
"content": "## Automatic Weekly Digital PR Stories Suggestions\nA weekly automated system that identifies trending news on Reddit, evaluates public sentiment through comment analysis, extracts key information from source articles, and generates strategic angles for potential digital PR campaigns. This workflow delivers curated, sentiment-analyzed news opportunities based on current social media trends. The final comprehensive report is automatically uploaded to Google Drive for storage and simultaneously shared with team members via a dedicated Mattermost channel for immediate collaboration.\n\n### Set up instructions:\n1. Add a new credential \"Reddit OAuth2 API\" by following this [guide](https:\/\/docs.n8n.io\/integrations\/builtin\/credentials\/reddit\/). Assign your Reddit OAuth2 account to the Reddit nodes.\n2. Add a new credential \"Anthropic Account\" by following this [guide]\n(https:\/\/docs.n8n.io\/integrations\/builtin\/credentials\/anthropic\/). Assign your Anthropic account to the nodes \"Anthropic Chat Model\".\n3. Add a new credential \"Google Drive OAuth2 API\" by following this [guide](https:\/\/docs.n8n.io\/integrations\/builtin\/credentials\/google\/oauth-single-service\/). Assign your Google Drive OAuth2 account to the node \"Gmail Drive\" nodes.\n4. Set your interested topics (one per line) and Jina API key in the \"Set Data\" node. You can obtain your Jina API key [here](https:\/\/jina.ai\/api-dashboard\/key-manager).\n5. Update your Mattermost information (Mattermost instance URL, Webhook ID and Channel) in the Mattermost node. You can follow this [guide](https:\/\/developers.mattermost.com\/integrate\/webhooks\/incoming\/).\n6. You can adjust the cron if needed. It currently run every Monday at 6am."
},
"typeVersion": 1
},
{
"id": "07f1e0ff-892c-4aaf-ad77-e636138570a1",
"name": "Comments Analysis",
"type": "@n8n\/n8n-nodes-langchain.chainLlm",
"position": [
1020,
640
],
"parameters": {
"text": "=Please analyze the following Reddit post and its comments:\n\nCONTEXT:\n<Reddit_Post_Info>\nPost Title: {{ $('Set for Loop').first().json.Title.replace(\/\\\"\/g, '\\\\\\\"') }}\nPost Date: {{ $('Set for Loop').first().json.Date }}\nShared URL: {{ $('Set for Loop').first().json.URL }}\nTotal Upvotes: {{ $('Set for Loop').first().json.Upvotes }}\nTotal Comments: {{ $('Set for Loop').first().json.Comments }}\n<\/Reddit_Post_Info>\n\nComment Thread Data:\n<Reddit_Post_Top_Comments>\n{{ $json.markdown.replace(\/\\\"\/g, '\\\\\\\"') }}\n<\/Reddit_Post_Top_Comments>\n\nAnalyze this discussion through these dimensions:\n\n1. CONTENT CONTEXT:\n \u2022 Main topic\/subject matter\n \u2022 Why this is trending (based on engagement metrics)\n \u2022 News cycle timing implications\n \u2022 Relationship to broader industry\/market trends\n\n2. SENTIMENT ANALYSIS:\n \u2022 Overall sentiment score (Scale: -5 to +5)\n \u2022 Primary emotional undertones\n \u2022 Sentiment progression in discussion threads\n \u2022 Consensus vs. controversial viewpoints\n \u2022 Changes in sentiment based on comment depth\n\n3. ENGAGEMENT INSIGHTS:\n \u2022 Most upvoted perspectives (with exact scores)\n \u2022 Controversial discussion points\n \u2022 Comment chains with deepest engagement\n \u2022 Types of responses generating most interaction\n\n4. NARRATIVE MAPPING:\n \u2022 Dominant narratives\n \u2022 Counter-narratives\n \u2022 Emerging sub-themes\n \u2022 Unexplored angles\n \u2022 Missing perspectives\n\nOutput Format (Place inside XML tags <comments_analysis>):\n\nPOST OVERVIEW:\nTitle: [Original title]\nEngagement Metrics:\n\u2022 Upvotes: [count]\n\u2022 Comments: [count]\n\u2022 Virality Assessment: [analysis of why this gained traction]\n\nSENTIMENT ANALYSIS:\n\u2022 Overall Score: [numerical score with explanation]\n\u2022 Sentiment Distribution: [percentage breakdown]\n\u2022 Key Emotional Drivers:\n - Primary: [emotion]\n - Secondary: [emotion]\n - Notable Shifts: [pattern analysis]\n\nTOP NARRATIVES:\n[List 3-5 dominant narratives]\nFor each narrative:\n\u2022 Key Points\n\u2022 Supporting Comments [with scores]\n\u2022 Counter-Arguments\n\u2022 Engagement Level\n\nAUDIENCE INSIGHTS:\n\u2022 Knowledge Level: [assessment]\n\u2022 Pain Points: [list key concerns]\n\u2022 Misconceptions: [list with evidence]\n\u2022 Information Gaps: [identified missing information]\n\nPR IMPLICATIONS:\n1. Story Opportunities:\n \u2022 [List potential angles]\n \u2022 [Supporting evidence from comments]\n\n2. Risk Factors:\n \u2022 [List potential PR risks]\n \u2022 [Supporting evidence from comments]\n\n3. Narrative Recommendations:\n \u2022 [Strategic guidance for messaging]\n \u2022 [Areas to address\/avoid]\n\nNEXT STEPS CONSIDERATIONS:\n\u2022 Key data points for content analysis\n\u2022 Suggested focus areas for PR story development\n\u2022 Critical elements to address in messaging\n\u2022 Potential expert perspectives needed\n\nMETA INSIGHTS:\n\u2022 Pattern connections to similar discussions\n\u2022 Unique aspects of this conversation\n\u2022 Viral elements to note\n\u2022 Community-specific nuances\n\nFocus on extracting insights that will:\n1. Inform the subsequent content analysis step\n2. Guide PR story development\n3. Identify unique angles and opportunities\n4. Highlight potential risks and challenges\n5. Suggest effective narrative approaches\n\nNote: Prioritize insights that will be valuable for the following workflow steps of content analysis and PR story development. Flag any particularly unique or compelling elements that could inform breakthrough story angles.",
"messages": {
"messageValues": [
{
"message": "=You are an expert Social Media Intelligence Analyst specialized in Reddit discourse analysis. Your task is to analyze Reddit posts and comments to extract meaningful patterns, sentiments, and insights for PR strategy development."
}
]
},
"promptType": "define"
},
"typeVersion": 1.5
},
{
"id": "4cdc4e49-6aae-4e6a-844e-c3c339638950",
"name": "News Analysis",
"type": "@n8n\/n8n-nodes-langchain.chainLlm",
"position": [
1720,
640
],
"parameters": {
"text": "=CONTEXT IMPORTANCE:\nReddit data is used as a critical indicator of news story potential because:\n\u2022 High upvotes indicate strong public interest\n\u2022 Comment volume shows discussion engagement\n\u2022 Comment sentiment reveals public perception\n\u2022 Discussion threads expose knowledge gaps and controversies\n\u2022 Community reaction predicts potential viral spread\n\u2022 Sub-discussions highlight unexplored angles\n\u2022 Engagement patterns suggest story longevity\n\nINPUT CONTEXT:\nNews URL: {{ $('Set for Loop').first().json.URL }}\nNews Content:\n<News_Content>\n{{ $json.content }}\n<\/News_Content>\nReddit Metrics:\n\u2022 Post Title (Understanding how the story was shared): {{ $('Set for Loop').first().json.Title }}\n\u2022 Upvotes (Indicator of initial interest): {{ $('Set for Loop').first().json.Upvotes }}\n\u2022 Total Comments (Engagement level): {{ $('Set for Loop').first().json.Comments }}\nReddit Sentiment Analysis:\n<Sentiment_Analysis>\n{{ $('Comments Analysis').first().json.text.replace(\/[\\s\\S]*<comments_analysis>\/, '').replace(\/<\\\/comments_analysis>[\\s\\S]*\/, '') }}\n<\/Sentiment_Analysis>\n\nFor each story, analyze through these dimensions:\n\n1. POPULARITY ASSESSMENT:\n A. Reddit Performance:\n \u2022 Upvote ratio and volume\n \u2022 Comment engagement rate\n \u2022 Discussion quality metrics\n \u2022 Viral spread indicators\n \n B. Audience Reception:\n \u2022 Initial reaction patterns\n \u2022 Discussion evolution\n \u2022 Community consensus vs. debate\n \u2022 Information seeking behavior\n\n1. CONTENT ANALYSIS:\n A. Core Story Elements:\n \u2022 Central narrative\n \u2022 Key stakeholders\n \u2022 Market implications\n \u2022 Industry impact\n \n B. Technical Analysis:\n \u2022 Writing style\n \u2022 Data presentation\n \u2022 Expert citations\n \u2022 Supporting evidence\n\n2. SOCIAL PROOF INTEGRATION:\n A. Engagement Metrics:\n \u2022 Reddit performance metrics\n \u2022 Discussion quality indicators\n \u2022 Viral spread patterns\n \n B. Sentiment Patterns:\n \u2022 Primary audience reactions\n \u2022 Controversial elements\n \u2022 Support vs. criticism ratio\n \u2022 Knowledge gaps identified\n\n3. NARRATIVE OPPORTUNITY MAPPING:\n A. Current Coverage:\n \u2022 Main angles covered\n \u2022 Supporting arguments\n \u2022 Counter-arguments\n \u2022 Expert perspectives\n \n B. Gap Analysis:\n \u2022 Unexplored perspectives\n \u2022 Missing stakeholder voices\n \u2022 Underutilized data points\n \u2022 Potential counter-narratives\n\nOUTPUT FORMAT (Place inside XML tags <news_analysis>):\n\nSTORY OVERVIEW:\nTitle: [Most compelling angle]\nURL: [Source]\nCategory: [Industry\/Topic]\n\nCONTENT SUMMARY:\nTLDR: [3-5 sentences emphasizing viral potential]\nCore Message: [One-line essence]\n\nKEY POINTS:\n\u2022 [Strategic point 1]\n\u2022 [Strategic point 2]\n\u2022 [Continue as needed]\n\nSOCIAL PROOF ANALYSIS:\nEngagement Metrics:\n\u2022 Reddit Performance: [Metrics + Interpretation]\n\u2022 Discussion Quality: [Analysis of conversation depth]\n\u2022 Sentiment Distribution: [From sentiment analysis]\n\nVIRAL ELEMENTS:\n1. Current Drivers:\n \u2022 [What's making it spread]\n \u2022 [Why people are engaging]\n \u2022 [Emotional triggers identified]\n\n2. Potential Amplifiers:\n \u2022 [Untapped viral elements]\n \u2022 [Engagement opportunities]\n \u2022 [Emotional hooks not yet used]\n\nNARRATIVE OPPORTUNITIES:\n1. Unexplored Angles:\n \u2022 [Angle 1 + Why it matters]\n \u2022 [Angle 2 + Why it matters]\n \u2022 [Angle 3 + Why it matters]\n\n2. Content Gaps:\n \u2022 [Missing perspectives]\n \u2022 [Underutilized data]\n \u2022 [Stakeholder voices needed]\n\n3. Controversy Points:\n \u2022 [Debate opportunities]\n \u2022 [Conflicting viewpoints]\n \u2022 [Areas of misconception]\n\nSTRATEGIC RECOMMENDATIONS:\n1. Immediate Opportunities:\n \u2022 [Quick-win suggestions]\n \u2022 [Timing considerations]\n\n2. Development Needs:\n \u2022 [Required research]\n \u2022 [Expert input needed]\n \u2022 [Data gaps to fill]\n\nPR POTENTIAL SCORE: [1-10 scale with explanation]\n\nFocus on elements that:\n\u2022 Show strong viral potential\n\u2022 Address identified audience concerns\n\u2022 Fill gaps in current coverage\n\u2022 Leverage positive sentiment patterns\n\u2022 Address or utilize controversial elements\n\u2022 Can be developed into unique angles\n\nNote: Prioritize insights that:\n1. Build on identified sentiment patterns\n2. Address audience knowledge gaps\n3. Leverage existing engagement drivers\n4. Can create breakthrough narratives\n5. Have immediate PR potential",
"messages": {
"messageValues": [
{
"message": "=You are an expert PR Content Analyst specialized in identifying viral potential in news stories. Your mission is to analyze news content while leveraging Reddit engagement metrics and sentiment data to evaluate news popularity and potential PR opportunities."
}
]
},
"promptType": "define"
},
"typeVersion": 1.5
},
{
"id": "c4905ed1-324a-4b08-a1f4-f5465229b56c",
"name": "Stories Report",
"type": "@n8n\/n8n-nodes-langchain.chainLlm",
"position": [
2060,
640
],
"parameters": {
"text": "=INPUT CONTEXT:\nNews Analysis: \n<News_Analysis>\n{{ $json.text.replace(\/[\\s\\S]*<news_analysis>\/, '').replace(\/<\\\/news_analysis>[\\s\\S]*\/, '') }}\n<\/News_Analysis>\nReddit Metrics:\n\u2022 Post Title (Understanding how the story was shared): {{ $('Set for Loop').first().json.Title }}\n\u2022 Upvotes (Indicator of initial interest): {{ $('Set for Loop').first().json.Upvotes }}\n\u2022 Total Comments (Engagement level): {{ $('Set for Loop').first().json.Comments }}\nReddit Sentiment Analysis:\n<Sentiment_Analysis>\n{{ $('Comments Analysis').first().json.text.replace(\/[\\s\\S]*<comments_analysis>\/, '').replace(\/<\\\/comments_analysis>[\\s\\S]*\/, '') }}\n<\/Sentiment_Analysis>\n\nOUTPUT FORMAT (Place inside XML tags <new_stories_report>):\n\nTREND ANALYSIS SUMMARY:\nTopic: [News topic\/category]\nCurrent Coverage Status: [Overview of existing coverage]\nAudience Reception: [From Reddit\/sentiment analysis]\nMarket Timing: [Why now is relevant]\n\nSTORY OPPORTUNITIES:\n\n1. FIRST-MOVER STORIES:\n[For each story idea (2-3)]\n\nStory #1:\n\u2022 Headline: [Compelling title]\n\u2022 Hook: [One-line grabber]\n\u2022 Story Summary: [2-3 sentences]\n\u2022 Why It Works:\n - Audience Evidence: [From Reddit data]\n - Market Gap: [From news analysis]\n - Timing Advantage: [Why now]\n\u2022 Development Needs:\n - Research Required: [List]\n - Expert Input: [Specific needs]\n - Supporting Data: [What's needed]\n\u2022 Media Strategy:\n - Primary Targets: [Publications]\n - Secondary Targets: [Publications]\n - Exclusive Potential: [Yes\/No + Rationale]\n\u2022 Success Metrics:\n - Coverage Goals: [Specific targets]\n - Engagement Expectations: [Based on Reddit data]\n\n2. TREND-AMPLIFIER STORIES:\n[Same format as above for 2-3 stories]\n\nPRIORITY RANKING:\n1. [Story Title] - Score: [X\/10]\n \u2022 Impact Potential: [Score + Rationale]\n \u2022 Resource Requirements: [High\/Medium\/Low]\n \u2022 Timeline: [Immediate\/Short-term\/Long-term]\n \n2. [Continue for all stories]\n\nEXECUTION ROADMAP:\n\u2022 Immediate Actions (24-48 hours)\n\u2022 Week 1 Priorities\n\u2022 Risk Management\n\u2022 Contingency Plans\n\nSTRATEGIC RECOMMENDATIONS:\n\u2022 Core Strategy\n\u2022 Alternative Angles\n\u2022 Resource Requirements\n\u2022 Timeline Considerations\n\nANALYTICAL FRAMEWORK:\n\n1. TREND VALIDATION:\n A. Story Performance Indicators:\n \u2022 Reddit engagement metrics\n \u2022 Public sentiment patterns\n \u2022 Discussion quality\n \u2022 Viral elements identified\n\n B. Current Narrative Landscape:\n \u2022 Dominant themes from news analysis\n \u2022 Public perception gaps\n \u2022 Controversial elements\n \u2022 Underserved perspectives\n\n2. OPPORTUNITY MAPPING:\n A. Content Gap Analysis:\n \u2022 Unexplored angles from news analysis\n \u2022 Audience questions from comments\n \u2022 Missing expert perspectives\n \u2022 Data\/research opportunities\n\n B. Timing Assessment:\n \u2022 News cycle position\n \u2022 Trend trajectory\n \u2022 Optimal launch window\n \u2022 Competition consideration\n\nPR STORY OPPORTUNITIES:\nGenerate 4-6 high-potential story ideas, categorized as:\n\nA. \\\"FIRST-MOVER\\\" OPPORTUNITIES (2-3 ideas):\nFor each idea:\n\n1. Story Concept:\n \u2022 Headline\n \u2022 Sub-headline\n \u2022 Key message\n \u2022 Unique selling point\n\n2. Why It Works:\n \u2022 Gap in current coverage\n \u2022 Evidence from Reddit discussions\n \u2022 Sentiment analysis support\n \u2022 Market timing rationale\n\n3. Development Requirements:\n \u2022 Required data\/research\n \u2022 Expert perspectives needed\n \u2022 Supporting elements\n \u2022 Potential challenges\n\n4. Media Strategy:\n \u2022 Target publications\n \u2022 Journalist appeal factors\n \u2022 Exclusive potential\n \u2022 Supporting assets needed\n\nB. \\\"TREND-AMPLIFIER\\\" OPPORTUNITIES (2-3 ideas):\n[Same structure as above, but focused on enhancing existing narratives]\n\nSTORY PRIORITIZATION MATRIX:\nFor each story idea:\n1. Impact Potential (1-10):\n \u2022 Audience interest indicators\n \u2022 Media appeal factors\n \u2022 Viral potential\n \u2022 Business value\n\n2. Resource Requirements:\n \u2022 Time to develop\n \u2022 Research needs\n \u2022 Expert input\n \u2022 Asset creation\n\n3. Risk Assessment:\n \u2022 Competition factors\n \u2022 Timing risks\n \u2022 Narrative challenges\n \u2022 Mitigation strategies\n\nEXECUTION ROADMAP:\n1. Immediate Actions (Next 24-48 hours):\n \u2022 Priority research needs\n \u2022 Expert outreach\n \u2022 Data gathering\n \u2022 Asset development\n\n2. Development Timeline:\n \u2022 Story development sequence\n \u2022 Key milestones\n \u2022 Decision points\n \u2022 Launch windows\n\n3. Success Metrics:\n \u2022 Coverage targets\n \u2022 Engagement goals\n \u2022 Share of voice objectives\n \u2022 Impact measurements\n\nSTRATEGIC RECOMMENDATIONS:\n1. Primary Strategy:\n \u2022 Core approach\n \u2022 Key differentiators\n \u2022 Critical success factors\n \u2022 Risk mitigation\n\n2. Alternative Approaches:\n \u2022 Backup angles\n \u2022 Pivot opportunities\n \u2022 Alternative narratives\n \u2022 Contingency plans\n\nFocus on creating stories that:\n\u2022 Address identified audience interests (from Reddit data)\n\u2022 Fill gaps in current coverage\n\u2022 Leverage positive sentiment patterns\n\u2022 Solve for identified pain points\n\u2022 Offer unique, data-backed perspectives\n\u2022 Present clear competitive advantages\n\nBased on the provided news analysis, Reddit metrics, and sentiment analysis, please generate a comprehensive PR strategy report following the format above.",
"messages": {
"messageValues": [
{
"message": "=You are an elite PR Strategy Consultant specialized in crafting breakthrough story angles that capture media attention. Your mission is to analyze trending story patterns and develop high-impact PR opportunities based on comprehensive data analysis.\n\nCONTEXT IMPORTANCE:\nThis analysis combines three critical data sources:\n1. Reddit Engagement Data:\n \u2022 Indicates public interest levels\n \u2022 Shows organic discussion patterns\n \u2022 Reveals audience sentiment\n \u2022 Highlights knowledge gaps\n \u2022 Demonstrates viral potential\n\n2. News Content Analysis:\n \u2022 Provides core story elements\n \u2022 Shows current media angles\n \u2022 Identifies market implications\n \u2022 Reveals coverage gaps\n \u2022 Maps expert perspectives\n\n3. Sentiment Analysis:\n \u2022 Reveals public perception\n \u2022 Identifies controversy points\n \u2022 Shows emotional triggers\n \u2022 Highlights audience concerns\n \u2022 Indicates story longevity\n\nThis combined data helps us:\n\u2022 Validate story potential\n\u2022 Identify unexplored angles\n\u2022 Understand audience needs\n\u2022 Predict media interest\n\u2022 Craft compelling narratives"
}
]
},
"promptType": "define"
},
"typeVersion": 1.5
},
{
"id": "1379c60b-387c-4eba-a7c2-2bcb1cda48fd",
"name": "Set Data",
"type": "n8n-nodes-base.set",
"position": [
240,
420
],
"parameters": {
"options": [],
"assignments": {
"assignments": [
{
"id": "b4da0605-b5e1-47e1-8e7e-00158ecaba33",
"name": "Topics",
"type": "string",
"value": "=Donald Trump\nPolitics"
},
{
"id": "d7602355-7082-4e98-a0b5-a400fade6dbc",
"name": "Jina API Key",
"type": "string",
"value": "YOUR_API_KEY"
}
]
}
},
"typeVersion": 3.399999999999999911182158029987476766109466552734375
}
],
"active": false,
"pinData": [],
"settings": {
"executionOrder": "v1"
},
"versionId": "dad1fb7a-599f-4b98-9461-8b27baa774d9",
"connections": {
"Set Data": {
"main": [
[
{
"node": "Split Topics into Items",
"type": "main",
"index": 0
}
]
]
},
"Aggregate": {
"main": [
[
{
"node": "Merge Binary Files",
"type": "main",
"index": 0
}
]
]
},
"Keep Last": {
"main": [
[
{
"node": "News Analysis",
"type": "main",
"index": 0
}
]
]
},
"Get Comments": {
"main": [
[
{
"node": "Extract Top Comments",
"type": "main",
"index": 0
}
]
]
},
"Search Posts": {
"main": [
[
{
"node": "Upvotes Requirement Filtering",
"type": "main",
"index": 0
}
]
]
},
"Set for Loop": {
"main": [
[
{
"node": "Get Comments",
"type": "main",
"index": 0
}
]
]
},
"Google Drive6": {
"main": [
[
{
"node": "Google Drive7",
"type": "main",
"index": 0
}
]
]
},
"Google Drive7": {
"main": [
[
{
"node": "Send files to Mattermost3",
"type": "main",
"index": 0
}
]
]
},
"News Analysis": {
"main": [
[
{
"node": "Stories Report",
"type": "main",
"index": 0
}
]
]
},
"Compress files": {
"main": [
[
{
"node": "Google Drive6",
"type": "main",
"index": 0
}
]
]
},
"Stories Report": {
"main": [
[
{
"node": "Set Final Report",
"type": "main",
"index": 0
}
]
]
},
"Convert to File": {
"main": [
[
{
"node": "Loop Over Items",
"type": "main",
"index": 0
}
]
]
},
"Format Comments": {
"main": [
[
{
"node": "Comments Analysis",
"type": "main",
"index": 0
}
]
]
},
"Loop Over Items": {
"main": [
[
{
"node": "Aggregate",
"type": "main",
"index": 0
}
],
[
{
"node": "Set for Loop",
"type": "main",
"index": 0
}
]
]
},
"Get News Content": {
"main": [
[
{
"node": "Keep Last",
"type": "main",
"index": 0
}
]
]
},
"Schedule Trigger": {
"main": [
[
{
"node": "Set Data",
"type": "main",
"index": 0
}
]
]
},
"Set Final Report": {
"main": [
[
{
"node": "Convert to File",
"type": "main",
"index": 0
}
]
]
},
"Set Reddit Posts": {
"main": [
[
{
"node": "Remove Duplicates",
"type": "main",
"index": 0
}
]
]
},
"Comments Analysis": {
"main": [
[
{
"node": "Get News Content",
"type": "main",
"index": 0
}
]
]
},
"Remove Duplicates": {
"main": [
[
{
"node": "Loop Over Items",
"type": "main",
"index": 0
}
]
]
},
"Merge Binary Files": {
"main": [
[
{
"node": "Compress files",
"type": "main",
"index": 0
}
]
]
},
"Anthropic Chat Model": {
"ai_languageModel": [
[
{
"node": "Comments Analysis",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Extract Top Comments": {
"main": [
[
{
"node": "Format Comments",
"type": "main",
"index": 0
}
]
]
},
"Anthropic Chat Model1": {
"ai_languageModel": [
[
{
"node": "News Analysis",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Anthropic Chat Model2": {
"ai_languageModel": [
[
{
"node": "Stories Report",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Split Topics into Items": {
"main": [
[
{
"node": "Search Posts",
"type": "main",
"index": 0
}
]
]
},
"Upvotes Requirement Filtering": {
"main": [
[
{
"node": "Set Reddit Posts",
"type": "main",
"index": 0
}
]
]
}
}
}
