{ "nodes": [ { "parameters": { "functionCode": "if (!$json.items || !Array.isArray($json.items)) {\n return [];\n}\n\nconst questions = $json.items.slice(0, 5).map(q => ({\n timestamp: new Date(q.creation_date * 1000).toISOString(),\n platform: 'stackoverflow',\n author: q.owner ? q.owner.display_name : 'unknown',\n content: q.title,\n url: q.link,\n score: q.score || 0\n}));\n\nreturn questions.map(q => ({json: q}));" }, "id": "0d63d94a-1479-464d-8e06-291a7dba08ba", "name": "Process StackOverflow", "type": "n8n-nodes-base.function", "typeVersion": 1, "position": [ 208, 48 ] }, { "parameters": { "url": "https://api.stackexchange.com/2.3/questions?order=desc&sort=creation&tagged=artificial-intelligence&site=stackoverflow&pagesize=10", "options": {} }, "id": "e202f38a-c8cb-4444-aab0-c727d6bd1c73", "name": "Get StackOverflow", "type": "n8n-nodes-base.httpRequest", "typeVersion": 4.1, "position": [ -16, 48 ] }, { "parameters": {}, "id": "520abc3b-556f-4f76-8345-d061ee8526a8", "name": "Manual Trigger", "type": "n8n-nodes-base.manualTrigger", "typeVersion": 1, "position": [ -240, 240 ] }, { "parameters": { "functionCode": "// Combine all platform data from 3 sources\nconst allData = [];\n\nfor (const inputData of $input.all()) {\n if (inputData.json && inputData.json.timestamp) {\n allData.push(inputData.json);\n }\n}\n\nconsole.log(`Combined ${allData.length} items from 3 platforms: StackOverflow, GitHub, Reddit ML`);\nreturn allData.map(item => ({json: item}));" }, "id": "fd3e970b-5599-466c-839e-3769dc79719b", "name": "Combine All Data", "type": "n8n-nodes-base.function", "typeVersion": 1, "position": [ 528, 240 ] }, { "parameters": { "functionCode": "// Process Reddit ML JSON\nif (!$json.data || !$json.data.children) {\n return [];\n}\n\nconst posts = $json.data.children.slice(0, 3).map(child => ({\n timestamp: new Date(child.data.created_utc * 1000).toISOString(),\n platform: 'reddit_ml',\n author: child.data.author,\n content: child.data.title,\n url: 'https://reddit.com' + child.data.permalink,\n score: child.data.score || 0\n}));\n\nreturn posts.map(p => ({json: p}));" }, "id": "decf7a1e-cf6e-48d9-8582-c72fbd049f39", "name": "Process Reddit ML", "type": "n8n-nodes-base.function", "typeVersion": 1, "position": [ 208, 288 ] }, { "parameters": { "url": "https://www.reddit.com/r/MachineLearning/.json", "options": {} }, "id": "235ba23e-5ed9-4d22-bd60-de2b23c6e6ee", "name": "Get Reddit ML", "type": "n8n-nodes-base.httpRequest", "typeVersion": 4.1, "position": [ -16, 288 ] }, { "parameters": { "functionCode": "if (!$json.items || !Array.isArray($json.items)) {\n return [];\n}\n\nconst repos = $json.items.slice(0, 3).map(repo => ({\n timestamp: new Date(repo.updated_at).toISOString(),\n platform: 'github',\n author: repo.owner ? repo.owner.login : 'unknown',\n content: repo.name + ' - ' + (repo.description || 'No description'),\n url: repo.html_url,\n score: repo.stargazers_count || 0\n}));\n\nreturn repos.map(r => ({json: r}));" }, "id": "99e7d249-c8d8-4d88-80e5-5370ee35a549", "name": "Process GitHub", "type": "n8n-nodes-base.function", "typeVersion": 1, "position": [ 208, 160 ] }, { "parameters": { "url": "https://api.github.com/search/repositories?q=artificial+intelligence&sort=updated&per_page=5", "options": {} }, "id": "8f093f54-c5c1-4a33-ad8d-de2e14679660", "name": "Get GitHub", "type": "n8n-nodes-base.httpRequest", "typeVersion": 4.1, "position": [ -16, 160 ] }, { "parameters": { "method": "POST", "url": "https://api.groq.com/openai/v1/chat/completions", "authentication": "predefinedCredentialType", "nodeCredentialType": "groqApi", "sendBody": true, "specifyBody": "json", "jsonBody": "={\n \"model\": \"llama3-8b-8192\",\n \"messages\": [\n {\n \"role\": \"user\", \n \"content\": \"Analyze the sentiment of this text and respond with only: positive, negative, or neutral. Text: {{$json.content}}\"\n }\n ],\n \"max_tokens\": 10\n}", "options": {} }, "id": "ed94ea81-0575-48d8-9b5e-4156c3a4e818", "name": "Groq LLM Sentiment", "type": "n8n-nodes-base.httpRequest", "typeVersion": 4.1, "position": [ 752, 240 ] }, { "parameters": { "functionCode": "// Process Groq LLM sentiment response\nconst originalData = $('Combine All Data').all();\nconst sentimentResults = $input.all();\n\nconst processedItems = originalData.map((item, index) => {\n const sentimentResult = sentimentResults[index]?.json;\n \n let sentiment_score = 0;\n let sentiment_label = 'neutral';\n let confidence = 0.8;\n \n if (sentimentResult && sentimentResult.choices && sentimentResult.choices[0]) {\n const llmResponse = sentimentResult.choices[0].message.content.toLowerCase().trim();\n \n if (llmResponse.includes('positive')) {\n sentiment_label = 'positive';\n sentiment_score = 0.7;\n } else if (llmResponse.includes('negative')) {\n sentiment_label = 'negative';\n sentiment_score = -0.7;\n } else {\n sentiment_label = 'neutral';\n sentiment_score = 0;\n }\n } else {\n // Fallback if API fails\n const words = item.json.content.toLowerCase().split(/\\W+/);\n const pos = words.filter(w => ['good', 'great', 'excellent', 'solved'].includes(w)).length;\n const neg = words.filter(w => ['error', 'problem', 'issue', 'bad'].includes(w)).length;\n sentiment_score = (pos - neg) / Math.max(words.length, 1);\n sentiment_label = sentiment_score > 0.01 ? 'positive' : sentiment_score < -0.01 ? 'negative' : 'neutral';\n confidence = 0.3;\n }\n \n return {\n timestamp: item.json.timestamp,\n platform: item.json.platform,\n author: item.json.author,\n content: item.json.content.substring(0, 200),\n url: item.json.url,\n score: item.json.score,\n sentiment_score: Math.round(sentiment_score * 1000) / 1000,\n sentiment_label: sentiment_label,\n llm_confidence: confidence,\n processed_at: new Date().toISOString()\n };\n});\n\nconsole.log('Processed', processedItems.length, 'items with sentiment analysis');\n\nreturn processedItems.map(item => ({json: item}));" }, "id": "2034b92a-14dd-4794-a14c-64fc7fd0d5dc", "name": "Process Groq Sentiment", "type": "n8n-nodes-base.function", "typeVersion": 1, "position": [ 976, 240 ] }, { "parameters": { "method": "POST", "url": "https://api.groq.com/openai/v1/chat/completions", "authentication": "predefinedCredentialType", "nodeCredentialType": "groqApi", "sendBody": true, "specifyBody": "json", "jsonBody": "={\n \"model\": \"llama3-8b-8192\",\n \"messages\": [\n {\n \"role\": \"user\", \n \"content\": \"You are a Social Sentiment Agent for the Mycroft AI investment framework. Classify this content into ONE category and assess its discussion type. Respond with format: CATEGORY|TYPE where CATEGORY is one of: LLM_DEVELOPMENT, AI_HARDWARE, INVESTMENT_DISCUSSION, TECHNICAL_ISSUES, RESEARCH, BUSINESS_STRATEGY and TYPE is either TECHNICAL or GENERAL. Content: {{$json.content}}\"\n }\n ],\n \"max_tokens\": 15\n}", "options": {} }, "id": "a23bcf2a-c3d6-43a9-a86a-5adfa1c80cea", "name": "Groq Topic Classification", "type": "n8n-nodes-base.httpRequest", "typeVersion": 4.1, "position": [ 1200, 240 ] }, { "parameters": { "functionCode": "// Advanced Social Sentiment Agent - Topic Clustering & Technical Discussion Interpretation\nconst sentimentData = $('Process Groq Sentiment').all();\nconst topicResults = $input.all();\n\nconst enhancedItems = sentimentData.map((item, index) => {\n const topicResult = topicResults[index]?.json;\n \n // Parse LLM response for category and technical classification\n let topic_category = 'GENERAL';\n let discussion_type = 'GENERAL';\n \n if (topicResult && topicResult.choices && topicResult.choices[0]) {\n const llmResponse = topicResult.choices[0].message.content.toUpperCase().trim();\n const parts = llmResponse.split('|');\n \n // Extract category\n const validTopics = ['LLM_DEVELOPMENT', 'AI_HARDWARE', 'INVESTMENT_DISCUSSION', 'TECHNICAL_ISSUES', 'RESEARCH', 'BUSINESS_STRATEGY'];\n topic_category = validTopics.find(t => llmResponse.includes(t)) || 'GENERAL';\n \n // Extract discussion type\n discussion_type = llmResponse.includes('TECHNICAL') ? 'TECHNICAL' : 'GENERAL';\n }\n \n // Enhanced technical discussion interpretation\n const content = item.json.content.toLowerCase();\n const technicalKeywords = {\n development: ['api', 'sdk', 'framework', 'library', 'code', 'programming', 'development'],\n models: ['model', 'training', 'fine-tuning', 'inference', 'embedding', 'transformer', 'neural'],\n infrastructure: ['gpu', 'cuda', 'cloud', 'scaling', 'deployment', 'kubernetes', 'docker'],\n research: ['paper', 'research', 'arxiv', 'algorithm', 'methodology', 'experiment'],\n business: ['funding', 'valuation', 'ipo', 'revenue', 'market', 'competition', 'strategy']\n };\n \n // Calculate technical depth across categories\n let technical_categories = {};\n let total_technical_score = 0;\n \n Object.keys(technicalKeywords).forEach(category => {\n const score = technicalKeywords[category].filter(keyword => content.includes(keyword)).length;\n technical_categories[category] = score;\n total_technical_score += score;\n });\n \n const is_technical = total_technical_score >= 2 || discussion_type === 'TECHNICAL';\n \n // Developer community sentiment tracking\n const community_indicators = {\n developer_engagement: content.match(/(github|stackoverflow|developer|coding|programming)/g)?.length || 0,\n problem_solving: content.match(/(error|bug|issue|problem|fix|solve)/g)?.length || 0,\n innovation_excitement: content.match(/(new|breakthrough|amazing|revolutionary|game-changing)/g)?.length || 0,\n skepticism: content.match(/(overhype|bubble|concern|risk|limitation)/g)?.length || 0\n };\n \n // Enhanced engagement calculation by platform\n let engagement_score = item.json.score || 0;\n let platform_weight = 1;\n \n switch(item.json.platform) {\n case 'stackoverflow': \n platform_weight = 1.5; // High-quality technical discussions\n engagement_score = Math.max(engagement_score, 1);\n break;\n case 'github': \n platform_weight = 1.3; // Developer-focused\n engagement_score = engagement_score / 100; // Scale down star count\n break;\n case 'reddit_ml': \n platform_weight = 1.2; // ML community expertise\n break;\n }\n \n const weighted_engagement = engagement_score * platform_weight;\n \n return {\n ...item.json,\n // Topic clustering\n topic_category: topic_category,\n discussion_type: discussion_type,\n \n // Technical discussion interpretation\n is_technical: is_technical,\n technical_score: total_technical_score,\n technical_categories: technical_categories,\n \n // Developer community sentiment tracking\n community_indicators: community_indicators,\n developer_sentiment: community_indicators.developer_engagement > 0 ? 'engaged' : 'neutral',\n \n // Enhanced engagement metrics\n engagement_score: Math.round(weighted_engagement * 100) / 100,\n platform_weight: platform_weight\n };\n});\n\nconsole.log('Enhanced', enhancedItems.length, 'items with Social Sentiment Agent capabilities');\nreturn enhancedItems.map(item => ({json: item}));" }, "id": "600a2548-0298-44b9-9f8a-cd9bf5130ed3", "name": "Topic Clustering Engine", "type": "n8n-nodes-base.function", "typeVersion": 1, "position": [ 1424, 240 ] }, { "parameters": { "functionCode": "// Advanced Signal-to-Noise Filtering for Social Sentiment Analysis (3-Source Version)\nconst allItems = $input.all().map(item => item.json);\n\n// Multi-dimensional quality scoring for social sentiment\nconst scoredItems = allItems.map(item => {\n let signal_score = 0;\n let noise_factors = [];\n \n // Content substance scoring\n const contentLength = item.content.length;\n if (contentLength > 150) {\n signal_score += 3;\n } else if (contentLength > 75) {\n signal_score += 2;\n } else if (contentLength < 30) {\n noise_factors.push('too_short');\n signal_score -= 1;\n }\n \n // Platform credibility and community value (Updated for 3 sources)\n switch(item.platform) {\n case 'stackoverflow': \n signal_score += 4; // High-quality technical Q&A\n break;\n case 'github': \n signal_score += 3; // Developer activity\n break;\n case 'reddit_ml': \n signal_score += 3; // ML community expertise\n break;\n }\n \n // Technical discussion value\n if (item.is_technical) {\n signal_score += 3;\n if (item.technical_score > 5) {\n signal_score += 2; // Highly technical content\n }\n }\n \n // Developer community engagement bonus\n if (item.community_indicators) {\n const communityValue = item.community_indicators.developer_engagement + \n item.community_indicators.problem_solving;\n if (communityValue > 2) signal_score += 2;\n else if (communityValue > 0) signal_score += 1;\n }\n \n // Engagement and interaction quality\n const engagementThreshold = item.platform === 'github' ? 5 : 10;\n if (item.engagement_score > engagementThreshold * 2) {\n signal_score += 3;\n } else if (item.engagement_score > engagementThreshold) {\n signal_score += 2;\n } else if (item.engagement_score === 0) {\n noise_factors.push('no_engagement');\n signal_score -= 1;\n }\n \n // Sentiment clarity and confidence\n if (item.llm_confidence > 0.8) {\n signal_score += 2;\n } else if (item.llm_confidence < 0.5) {\n noise_factors.push('low_confidence');\n signal_score -= 1;\n }\n \n // Topic relevance for investment intelligence\n const highValueTopics = ['INVESTMENT_DISCUSSION', 'BUSINESS_STRATEGY', 'RESEARCH'];\n const mediumValueTopics = ['LLM_DEVELOPMENT', 'AI_HARDWARE'];\n \n if (highValueTopics.includes(item.topic_category)) {\n signal_score += 3;\n } else if (mediumValueTopics.includes(item.topic_category)) {\n signal_score += 2;\n }\n \n // Calculate signal-to-noise ratio\n const maxPossibleScore = 20; // Theoretical maximum\n const signal_to_noise_ratio = Math.round((signal_score / maxPossibleScore) * 100) / 100;\n \n // Determine if item passes quality threshold\n const is_high_signal = signal_score >= 8 && noise_factors.length <= 1;\n const is_medium_signal = signal_score >= 5 && noise_factors.length <= 2;\n \n let signal_quality = 'low';\n if (is_high_signal) signal_quality = 'high';\n else if (is_medium_signal) signal_quality = 'medium';\n \n return {\n ...item,\n signal_score: signal_score,\n signal_quality: signal_quality,\n signal_to_noise_ratio: signal_to_noise_ratio,\n noise_factors: noise_factors,\n passes_filter: is_medium_signal || is_high_signal\n };\n});\n\n// Analytics for signal quality distribution\nconst signalAnalytics = {\n high_signal: scoredItems.filter(item => item.signal_quality === 'high').length,\n medium_signal: scoredItems.filter(item => item.signal_quality === 'medium').length,\n low_signal: scoredItems.filter(item => item.signal_quality === 'low').length,\n avg_signal_score: Math.round((scoredItems.reduce((sum, item) => sum + item.signal_score, 0) / scoredItems.length) * 100) / 100\n};\n\nconsole.log('Signal-to-Noise Analysis (3-Source):', signalAnalytics);\nconsole.log(`Filtered: ${scoredItems.filter(item => item.passes_filter).length}/${scoredItems.length} items pass quality threshold`);\n\nreturn scoredItems.map(item => ({json: item}));" }, "id": "b9062fad-8c8d-47c2-a200-b57605d86168", "name": "Signal-to-Noise Filter", "type": "n8n-nodes-base.function", "typeVersion": 1, "position": [ 1648, 240 ] }, { "parameters": { "functionCode": "// Mycroft Social Sentiment Agent - Final Intelligence Output (3-Source Version)\nconst processedItems = $input.all().map(item => item.json);\n\n// Core Social Sentiment Agent Analytics\nconst analytics = {\n mycroft_agent_type: 'Social_Sentiment_Agent',\n framework_version: '1.0.0',\n execution_timestamp: new Date().toISOString(),\n data_sources: ['stackoverflow', 'github', 'reddit_ml'], // Updated for 3 sources\n \n // Data processing summary\n total_items_processed: processedItems.length,\n platforms_analyzed: {\n stackoverflow: processedItems.filter(item => item.platform === 'stackoverflow').length,\n github: processedItems.filter(item => item.platform === 'github').length,\n reddit_ml: processedItems.filter(item => item.platform === 'reddit_ml').length\n },\n \n // Contextual sentiment analysis results\n sentiment_distribution: {\n positive: processedItems.filter(item => item.sentiment_label === 'positive').length,\n negative: processedItems.filter(item => item.sentiment_label === 'negative').length,\n neutral: processedItems.filter(item => item.sentiment_label === 'neutral').length,\n avg_sentiment_score: Math.round((processedItems.reduce((sum, item) => sum + item.sentiment_score, 0) / processedItems.length) * 1000) / 1000\n },\n \n // Topic clustering and trend identification\n topic_clusters: {},\n trending_topics: [],\n \n // Technical discussion interpretation\n technical_analysis: {\n technical_discussions: processedItems.filter(item => item.is_technical).length,\n general_discussions: processedItems.filter(item => !item.is_technical).length,\n technical_percentage: Math.round((processedItems.filter(item => item.is_technical).length / processedItems.length) * 100),\n avg_technical_score: Math.round((processedItems.reduce((sum, item) => sum + (item.technical_score || 0), 0) / processedItems.length) * 100) / 100\n },\n \n // Developer community sentiment tracking\n community_sentiment: {\n developer_engaged_posts: processedItems.filter(item => item.developer_sentiment === 'engaged').length,\n avg_developer_engagement: 0,\n problem_solving_activity: 0,\n innovation_excitement_level: 0\n },\n \n // Signal-to-noise ratio optimization\n signal_quality: {\n high_signal_items: processedItems.filter(item => item.signal_quality === 'high').length,\n medium_signal_items: processedItems.filter(item => item.signal_quality === 'medium').length,\n low_signal_items: processedItems.filter(item => item.signal_quality === 'low').length,\n avg_signal_score: Math.round((processedItems.reduce((sum, item) => sum + (item.signal_score || 0), 0) / processedItems.length) * 100) / 100,\n items_passing_filter: processedItems.filter(item => item.passes_filter).length\n }\n};\n\n// Calculate topic distribution and trends\nprocessedItems.forEach(item => {\n analytics.topic_clusters[item.topic_category] = (analytics.topic_clusters[item.topic_category] || 0) + 1;\n});\n\n// Identify trending topics (most discussed with high engagement)\nanalytics.trending_topics = Object.entries(analytics.topic_clusters)\n .sort(([,a], [,b]) => b - a)\n .slice(0, 3)\n .map(([topic, count]) => ({topic, count}));\n\n// Calculate community sentiment metrics\nif (processedItems.length > 0) {\n analytics.community_sentiment.avg_developer_engagement = Math.round(\n (processedItems.reduce((sum, item) => sum + (item.community_indicators?.developer_engagement || 0), 0) / processedItems.length) * 100\n ) / 100;\n \n analytics.community_sentiment.problem_solving_activity = processedItems.reduce(\n (sum, item) => sum + (item.community_indicators?.problem_solving || 0), 0\n );\n \n analytics.community_sentiment.innovation_excitement_level = processedItems.reduce(\n (sum, item) => sum + (item.community_indicators?.innovation_excitement || 0), 0\n );\n}\n\n// Generate intelligence insights for Mycroft framework\nconst intelligence_insights = {\n market_sentiment_direction: analytics.sentiment_distribution.positive > analytics.sentiment_distribution.negative ? 'bullish' : 'bearish',\n technical_community_activity: analytics.technical_analysis.technical_percentage > 60 ? 'high' : 'moderate',\n developer_engagement_level: analytics.community_sentiment.avg_developer_engagement > 1 ? 'active' : 'quiet',\n signal_quality_assessment: analytics.signal_quality.high_signal_items > analytics.signal_quality.low_signal_items ? 'strong' : 'noisy',\n \n // Top investment-relevant signals\n investment_signals: processedItems\n .filter(item => item.passes_filter && ['INVESTMENT_DISCUSSION', 'BUSINESS_STRATEGY', 'RESEARCH'].includes(item.topic_category))\n .sort((a, b) => b.signal_score - a.signal_score)\n .slice(0, 3)\n .map(item => ({\n platform: item.platform,\n topic: item.topic_category,\n sentiment: item.sentiment_label,\n signal_strength: item.signal_score,\n content_preview: item.content.substring(0, 100) + '...',\n url: item.url\n }))\n};\n\n// Create comprehensive Social Sentiment Agent report\nconst social_sentiment_report = {\n mycroft_intelligence_type: 'Social_Sentiment_Agent',\n capabilities_demonstrated: [\n 'contextual_sentiment_analysis',\n 'technical_discussion_interpretation', \n 'topic_clustering_and_trend_identification',\n 'developer_community_sentiment_tracking',\n 'signal_to_noise_ratio_optimization'\n ],\n analytics: analytics,\n intelligence_insights: intelligence_insights,\n execution_metadata: {\n run_id: Date.now().toString(),\n processing_time: new Date().toISOString(),\n data_sources: ['stackoverflow', 'github', 'reddit_ml'], // Updated for 3 sources\n ai_model_used: 'llama3-8b-8192'\n }\n};\n\nconsole.log('=== MYCROFT SOCIAL SENTIMENT AGENT COMPLETE (3-SOURCE) ===');\nconsole.log('Market Sentiment:', intelligence_insights.market_sentiment_direction.toUpperCase());\nconsole.log('Technical Activity:', intelligence_insights.technical_community_activity.toUpperCase());\nconsole.log('High-Signal Items:', analytics.signal_quality.high_signal_items);\nconsole.log('Investment Signals Found:', intelligence_insights.investment_signals.length);\n\n// Return comprehensive report + detailed data\nreturn [\n {json: {type: 'social_sentiment_report', data: social_sentiment_report}},\n ...processedItems.filter(item => item.passes_filter).map(item => ({json: {type: 'high_quality_signal', data: item}}))\n];" }, "id": "a934af0f-7e8a-48c3-9314-2f8a41777128", "name": "Social Sentiment Output", "type": "n8n-nodes-base.function", "typeVersion": 1, "position": [ 1872, 240 ] }, { "parameters": { "functionCode": "// Enhanced Mycroft Data Export & Analytics (3-Source Version)\nconst allData = $input.all().map(item => item.json);\n\n// Extract executive summary and detailed signals\nconst summary = allData.find(item => item.type === 'social_sentiment_report');\nconst detailedSignals = allData.filter(item => item.type === 'high_quality_signal').map(item => item.data);\n\n// Enhanced executive dashboard\nif (summary) {\n const report = summary.data;\n console.log('\\nšŸŽÆ === MYCROFT SOCIAL SENTIMENT AGENT DASHBOARD (3-SOURCE) ===');\n console.log('šŸ“Š Market Intelligence Summary:');\n console.log(' ā€¢ Market Sentiment Direction:', report.intelligence_insights?.market_sentiment_direction?.toUpperCase() || 'UNKNOWN');\n console.log(' ā€¢ Technical Community Activity:', report.intelligence_insights?.technical_community_activity?.toUpperCase() || 'UNKNOWN');\n console.log(' ā€¢ Developer Engagement Level:', report.intelligence_insights?.developer_engagement_level?.toUpperCase() || 'UNKNOWN');\n console.log(' ā€¢ Signal Quality Assessment:', report.intelligence_insights?.signal_quality_assessment?.toUpperCase() || 'UNKNOWN');\n \n console.log('\\nšŸ“ˆ Processing Analytics:');\n console.log(' ā€¢ Total Items Processed:', report.analytics?.total_items_processed || 0);\n console.log(' ā€¢ High Signal Items:', report.analytics?.signal_quality?.high_signal_items || 0);\n console.log(' ā€¢ Technical Discussions:', report.analytics?.technical_analysis?.technical_percentage || 0, '%');\n console.log(' ā€¢ Investment Signals Found:', report.intelligence_insights?.investment_signals?.length || 0);\n \n console.log('\\nšŸŒ Platform Distribution (3 Sources):');\n const platforms = report.analytics?.platforms_analyzed || {};\n Object.entries(platforms).forEach(([platform, count]) => {\n console.log(` ā€¢ ${platform}: ${count} items`);\n });\n \n console.log('\\nšŸ’­ Sentiment Analysis:');\n const sentiment = report.analytics?.sentiment_distribution || {};\n console.log(' ā€¢ Positive:', sentiment.positive || 0);\n console.log(' ā€¢ Negative:', sentiment.negative || 0);\n console.log(' ā€¢ Neutral:', sentiment.neutral || 0);\n console.log(' ā€¢ Average Score:', sentiment.avg_sentiment_score || 0);\n \n console.log('\\nšŸ”„ Trending Topics:');\n const topics = report.analytics?.trending_topics || [];\n topics.forEach((topic, index) => {\n console.log(` ${index + 1}. ${topic.topic}: ${topic.count} discussions`);\n });\n \n if (report.intelligence_insights?.investment_signals?.length > 0) {\n console.log('\\nšŸ’¼ Top Investment Signals:');\n report.intelligence_insights.investment_signals.forEach((signal, index) => {\n console.log(` ${index + 1}. [${signal.platform}] ${signal.topic} - ${signal.sentiment} (Score: ${signal.signal_strength})`);\n console.log(` Preview: ${signal.content_preview}`);\n });\n }\n}\n\n// Export structured data for database/storage\nconst structuredExport = {\n timestamp: new Date().toISOString(),\n mycroft_run_id: Date.now().toString(),\n agent_type: 'Social_Sentiment_Agent',\n data_sources: ['stackoverflow', 'github', 'reddit_ml'], // Updated for 3 sources\n executive_summary: summary?.data || null,\n high_quality_signals: detailedSignals,\n processing_stats: {\n total_input_sources: 3, // Updated from 4 to 3\n total_output_items: allData.length,\n high_signal_items: detailedSignals.length,\n executive_reports: summary ? 1 : 0\n },\n export_metadata: {\n framework: 'Mycroft',\n version: '1.0.0',\n export_time: new Date().toISOString(),\n data_ready_for_storage: true\n }\n};\n\nconsole.log('\\nšŸ“¦ === STRUCTURED DATA EXPORT (3-SOURCE) ===');\nconsole.log('Ready for database storage or file export');\nconsole.log('High-quality signals ready:', detailedSignals.length);\nconsole.log('Executive summary ready:', summary ? 'Yes' : 'No');\n\n// Return the structured export\nreturn [{json: structuredExport}];" }, "id": "a46aa295-78e0-4281-a808-966cb142884d", "name": "Enhanced Data Export", "type": "n8n-nodes-base.function", "typeVersion": 1, "position": [ 2096, 240 ] }, { "parameters": { "functionCode": "// Prepare High-Quality Signals for Google Sheets Storage (3-Source Version)\nconst structuredData = $input.all()[0].json;\nconst highQualitySignals = structuredData.high_quality_signals || [];\n\n// Prepare each signal for Google Sheets (flatten complex objects)\nconst sheetsReadyData = highQualitySignals.map(signal => ({\n // Core identification\n run_id: structuredData.mycroft_run_id,\n timestamp: signal.timestamp,\n platform: signal.platform,\n author: signal.author || 'unknown',\n \n // Content data\n content: signal.content || '',\n url: signal.url || '',\n \n // Sentiment analysis\n sentiment_score: signal.sentiment_score || 0,\n sentiment_label: signal.sentiment_label || 'neutral',\n llm_confidence: signal.llm_confidence || 0,\n \n // Topic classification\n topic_category: signal.topic_category || 'GENERAL',\n discussion_type: signal.discussion_type || 'GENERAL',\n \n // Technical analysis\n is_technical: signal.is_technical ? 'TRUE' : 'FALSE',\n technical_score: signal.technical_score || 0,\n \n // Community metrics\n developer_sentiment: signal.developer_sentiment || 'neutral',\n developer_engagement: signal.community_indicators?.developer_engagement || 0,\n problem_solving: signal.community_indicators?.problem_solving || 0,\n innovation_excitement: signal.community_indicators?.innovation_excitement || 0,\n \n // Quality scoring\n engagement_score: signal.engagement_score || 0,\n signal_quality: signal.signal_quality || 'low',\n signal_score: signal.signal_score || 0,\n signal_to_noise_ratio: signal.signal_to_noise_ratio || 0,\n \n // Metadata\n processed_at: signal.processed_at || new Date().toISOString(),\n framework: 'Mycroft',\n agent_type: 'Social_Sentiment_Agent',\n data_sources: 'stackoverflow,github,reddit_ml' // Updated for 3 sources\n}));\n\nconsole.log(`Prepared ${sheetsReadyData.length} high-quality signals for Google Sheets (3-Source Version)`);\nconsole.log('Sheet columns will be:', Object.keys(sheetsReadyData[0] || {}));\n\nreturn sheetsReadyData.map(row => ({json: row}));" }, "id": "cf83ff6f-3b7a-438f-825d-acb7c34b7576", "name": "Prepare for Google Sheets", "type": "n8n-nodes-base.function", "typeVersion": 1, "position": [ 2320, 240 ] }, { "parameters": { "operation": "appendOrUpdate", "documentId": { "__rl": true, "value": "REPLACE_WITH_RESOURCE", "mode": "id" }, "sheetName": { "__rl": true, "value": "REPLACE_WITH_RESOURCE", "mode": "name" }, "columns": { "mappingMode": "autoMapInputData", "value": {}, "matchingColumns": [ "run_id" ], "schema": [ { "id": "run_id", "displayName": "run_id", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": true, "removed": false }, { "id": "timestamp", "displayName": "timestamp", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": true }, { "id": "platform", "displayName": "platform", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": true }, { "id": "author", "displayName": "author", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": true }, { "id": "content", "displayName": "content", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": true }, { "id": "url", "displayName": "url", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": true }, { "id": "sentiment_score", "displayName": "sentiment_score", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": true }, { "id": "sentiment_label", "displayName": "sentiment_label", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": true }, { "id": "topic_category", "displayName": "topic_category", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": true }, { "id": "is_technical", "displayName": "is_technical", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": true }, { "id": "technical_score", "displayName": "technical_score", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": true }, { "id": "developer_sentiment", "displayName": "developer_sentiment", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": true }, { "id": "engagement_score", "displayName": "engagement_score", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": true }, { "id": "signal_quality", "displayName": "signal_quality", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": true }, { "id": "signal_score", "displayName": "signal_score", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": true }, { "id": "processed_at", "displayName": "processed_at", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": true }, { "id": "framework", "displayName": "framework", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": true } ], "attemptToConvertTypes": false, "convertFieldsToString": false }, "options": {} }, "id": "90fa4624-4f72-4191-8c00-e1e52b8be1f7", "name": "Store in Google Sheets", "type": "n8n-nodes-base.googleSheets", "typeVersion": 4, "position": [ 2544, 240 ] }, { "id": "59117bc6-33e9-5c30-84c9-591d594b1172", "name": "Publication Ā· Overview and setup", "type": "n8n-nodes-base.stickyNote", "typeVersion": 1, "position": [ -1040, 0 ], "parameters": { "content": "# http request and get reddit ml and groq llm sentiment\n\n## Purpose and structure\nThis folder contains the **http request and get reddit ml and groq llm sentiment** workflow. Its executable nodes use `function`, `googleSheets`, `httpRequest`, `manualTrigger`. The table and connection map below are derived from the JSON, not an inferred guarantee of behavior.\n\n## Setup\nImport inactive. Configure your own credentials and replace REPLACE_WITH_* values. Review external destinations and Code nodes before running.\n\n## Entry points\nManual Trigger\n\n## Verification\nSanitized source candidate; not runtime-tested. This overview is generated from node metadata. Read the folder README for the connection map, credential types, and license.\n\n## Attribution\nNik Bear Brown; rights confirmed 2026-10-19\nMIT; preserve source notices.", "width": 700, "height": 800, "color": 1 } } ], "connections": { "Process StackOverflow": { "main": [ [ { "node": "Combine All Data", "type": "main", "index": 0 } ] ] }, "Get StackOverflow": { "main": [ [ { "node": "Process StackOverflow", "type": "main", "index": 0 } ] ] }, "Manual Trigger": { "main": [ [ { "node": "Get StackOverflow", "type": "main", "index": 0 }, { "node": "Get Reddit ML", "type": "main", "index": 0 }, { "node": "Get GitHub", "type": "main", "index": 0 } ] ] }, "Combine All Data": { "main": [ [ { "node": "Groq LLM Sentiment", "type": "main", "index": 0 } ] ] }, "Process Reddit ML": { "main": [ [ { "node": "Combine All Data", "type": "main", "index": 0 } ] ] }, "Get Reddit ML": { "main": [ [ { "node": "Process Reddit ML", "type": "main", "index": 0 } ] ] }, "Process GitHub": { "main": [ [ { "node": "Combine All Data", "type": "main", "index": 0 } ] ] }, "Get GitHub": { "main": [ [ { "node": "Process GitHub", "type": "main", "index": 0 } ] ] }, "Groq LLM Sentiment": { "main": [ [ { "node": "Process Groq Sentiment", "type": "main", "index": 0 } ] ] }, "Process Groq Sentiment": { "main": [ [ { "node": "Groq Topic Classification", "type": "main", "index": 0 } ] ] }, "Groq Topic Classification": { "main": [ [ { "node": "Topic Clustering Engine", "type": "main", "index": 0 } ] ] }, "Topic Clustering Engine": { "main": [ [ { "node": "Signal-to-Noise Filter", "type": "main", "index": 0 } ] ] }, "Signal-to-Noise Filter": { "main": [ [ { "node": "Social Sentiment Output", "type": "main", "index": 0 } ] ] }, "Social Sentiment Output": { "main": [ [ { "node": "Enhanced Data Export", "type": "main", "index": 0 } ] ] }, "Enhanced Data Export": { "main": [ [ { "node": "Prepare for Google Sheets", "type": "main", "index": 0 } ] ] }, "Prepare for Google Sheets": { "main": [ [ { "node": "Store in Google Sheets", "type": "main", "index": 0 } ] ] } }, "active": false }