{ "name": "Media Narrative Tracker — Mycroft Agent 3 (Groq)", "nodes": [ { "parameters": { "rule": { "interval": [ { "field": "hours", "hoursInterval": 6 } ] } }, "id": "a7fc736c-cb23-401a-bcb9-ab5227756da5", "name": "Cron Trigger", "type": "n8n-nodes-base.scheduleTrigger", "typeVersion": 1.2, "position": [ -2944, -48 ] }, { "parameters": { "path": "run-media-pipeline", "authentication": "headerAuth", "responseMode": "responseNode", "options": {} }, "id": "c744aef5-93aa-4bbb-8e03-0688a23b8873", "name": "Manual Pipeline Webhook", "type": "n8n-nodes-base.webhook", "typeVersion": 2, "position": [ -2928, 176 ] }, { "parameters": { "jsCode": "// Fetch Watchlist — hardcoded AI sector companies\nconst watchlist = [\n { ticker: \"NVDA\", companyName: \"NVIDIA\",\n aliases: [\"nvidia\", \"nvda\", \"jensen huang\"] },\n { ticker: \"AMD\", companyName: \"AMD\",\n aliases: [\"amd\", \"advanced micro devices\", \"lisa su\"] },\n { ticker: \"INTC\", companyName: \"Intel\",\n aliases: [\"intel\", \"intc\", \"pat gelsinger\"] },\n { ticker: \"GOOGL\", companyName: \"Google\",\n aliases: [\"google\", \"alphabet\", \"googl\", \"deepmind\"] },\n { ticker: \"AAPL\", companyName: \"Apple\",\n aliases: [\"apple\", \"aapl\", \"tim cook\"] },\n { ticker: \"MSFT\", companyName: \"Microsoft\",\n aliases: [\"microsoft\", \"msft\", \"azure\", \"satya nadella\"] },\n { ticker: \"META\", companyName: \"Meta\",\n aliases: [\"meta\", \"facebook\", \"zuckerberg\", \"llama\"] }\n];\n\nreturn [{ json: { watchlist } }];" }, "id": "99644b9f-cd02-42d4-a496-0e010239ffd8", "name": "Fetch Watchlist", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ -2720, -48 ] }, { "parameters": { "url": "https://www.ft.com/rss/home", "sendHeaders": true, "headerParameters": { "parameters": [ { "name": "User-Agent", "value": "Mozilla/5.0 (Mycroft/1.0)" } ] }, "options": { "response": { "response": { "responseFormat": "text" } } } }, "id": "e2d5956a-1e31-47b7-a5de-3b06ff7a2bc9", "name": "Fetch FT RSS", "type": "n8n-nodes-base.httpRequest", "typeVersion": 4.2, "position": [ -2400, 64 ] }, { "parameters": { "url": "https://feeds.reuters.com/reuters/businessNews", "sendHeaders": true, "headerParameters": { "parameters": [ { "name": "User-Agent", "value": "Mozilla/5.0 (Mycroft/1.0)" } ] }, "options": { "response": { "response": { "responseFormat": "text" } } } }, "id": "9eafdb32-19ea-44c9-8148-afcae9b05632", "name": "Fetch Reuters RSS", "type": "n8n-nodes-base.httpRequest", "typeVersion": 4.2, "position": [ -2400, 208 ] }, { "parameters": { "url": "https://seekingalpha.com/feed.xml", "sendHeaders": true, "headerParameters": { "parameters": [ { "name": "User-Agent", "value": "Mozilla/5.0 (Mycroft/1.0)" } ] }, "options": { "response": { "response": { "responseFormat": "text" } } } }, "id": "fb1e903f-9644-4bbf-bcdb-d4b1f0cca39b", "name": "Fetch Seeking Alpha RSS", "type": "n8n-nodes-base.httpRequest", "typeVersion": 4.2, "position": [ -2400, 352 ] }, { "parameters": { "url": "https://www.cnbc.com/id/10001147/device/rss/rss.html", "sendHeaders": true, "headerParameters": { "parameters": [ { "name": "User-Agent", "value": "Mozilla/5.0 (Mycroft/1.0)" } ] }, "options": { "response": { "response": { "responseFormat": "text" } } } }, "id": "7abb2344-4b0c-43e2-a672-289834392d19", "name": "Fetch CNBC RSS", "type": "n8n-nodes-base.httpRequest", "typeVersion": 4.2, "position": [ -2400, 496 ] }, { "parameters": { "url": "https://finance.yahoo.com/rss/topstories", "sendHeaders": true, "headerParameters": { "parameters": [ { "name": "User-Agent", "value": "Mozilla/5.0 (Mycroft/1.0)" } ] }, "options": { "response": { "response": { "responseFormat": "text" } } } }, "id": "0d3e4c41-f027-4483-8ea1-2ef7de214184", "name": "Fetch Yahoo Finance RSS", "type": "n8n-nodes-base.httpRequest", "typeVersion": 4.2, "position": [ -2400, 624 ] }, { "parameters": { "jsCode": "// Parse & Clean Articles — regex XML parser, no external libs\nconst feeds = [\n { node: \"Fetch FT RSS\", source: \"Financial Times\" },\n { node: \"Fetch Reuters RSS\", source: \"Reuters\" },\n { node: \"Fetch Seeking Alpha RSS\", source: \"Seeking Alpha\" },\n { node: \"Fetch CNBC RSS\", source: \"CNBC\" },\n { node: \"Fetch Yahoo Finance RSS\", source: \"Yahoo Finance\" }\n];\n\nfunction stripHtml(s) {\n return String(s || \"\")\n .replace(//g, \"$1\")\n .replace(/<[^>]*>/g, \" \")\n .replace(/ /gi, \" \")\n .replace(/&/gi, \"&\")\n .replace(/</gi, \"<\")\n .replace(/>/gi, \">\")\n .replace(/"/gi, '\"')\n .replace(/'/gi, \"'\")\n .replace(/&[a-z0-9#]+;/gi, \" \")\n .replace(/\\s+/g, \" \")\n .trim();\n}\n\nfunction unwrapCdata(s) {\n const m = String(s || \"\").match(//);\n return m ? m[1] : s;\n}\n\nfunction tagValue(block, tag) {\n const re = new RegExp(\"<\" + tag + \"[^>]*>([\\\\s\\\\S]*?)<\\\\/\" + tag + \">\", \"i\");\n const m = block.match(re);\n return m ? unwrapCdata(m[1]).trim() : \"\";\n}\n\nfunction toIso(d) {\n if (!d) return null;\n const t = Date.parse(d);\n return isNaN(t) ? null : new Date(t).toISOString();\n}\n\nfunction rawText(nodeName) {\n let item;\n try { item = $(nodeName).first(); } catch (e) { return \"\"; }\n if (!item || !item.json) return \"\";\n const j = item.json;\n return j.data || j.body || j.response || (typeof j === \"string\" ? j : \"\") || \"\";\n}\n\nconst articles = [];\nfor (const feed of feeds) {\n const xml = rawText(feed.node);\n if (!xml) continue;\n const items = xml.match(//gi)\n || xml.match(//gi)\n || [];\n for (const block of items) {\n const headline = stripHtml(tagValue(block, \"title\"));\n let link = tagValue(block, \"link\");\n if (!link) {\n const m = block.match(/]*href=[\"']([^\"']+)[\"']/i);\n link = m ? m[1] : \"\";\n }\n const article_url = (link || \"\").trim();\n const pub = tagValue(block, \"pubDate\") || tagValue(block, \"published\") || tagValue(block, \"updated\");\n let summary = stripHtml(tagValue(block, \"description\") || tagValue(block, \"summary\") || tagValue(block, \"content\"));\n if (summary.length > 500) summary = summary.slice(0, 500);\n if (!headline || !article_url) continue;\n articles.push({\n article_url,\n headline,\n summary,\n source: feed.source,\n published_at: toIso(pub) || new Date().toISOString()\n });\n }\n}\n\nconst watchlist = $('Fetch Watchlist').first().json.watchlist;\nreturn [{ json: { articles, watchlist, fetchedAt: new Date().toISOString() } }];" }, "id": "c3e6a083-9329-408e-add4-c047b1bfc0db", "name": "Parse & Clean Articles", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ -1920, 352 ] }, { "parameters": { "jsCode": "// Deduplicate Against Supabase — drop URLs seen in last 7 days\nconst articles = $json.articles || [];\nconst watchlist = $json.watchlist || [];\nconst fetchedAt = $json.fetchedAt;\n\nconst SUPABASE_URL = $env.SUPABASE_URL;\nconst SUPABASE_KEY = $env.SUPABASE_KEY;\nconst sevenDaysAgo = new Date(Date.now() - 7 * 24 * 60 * 60 * 1000).toISOString();\n\nlet knownUrls = new Set();\ntry {\n const url = SUPABASE_URL +\n \"/rest/v1/media_narratives_raw?select=article_url&created_at=gte.\" +\n encodeURIComponent(sevenDaysAgo);\n const resp = await this.helpers.httpRequest({\n method: \"GET\",\n url,\n headers: {\n apikey: SUPABASE_KEY,\n Authorization: \"Bearer \" + SUPABASE_KEY,\n \"Content-Type\": \"application/json\"\n },\n json: true\n });\n const rows = Array.isArray(resp) ? resp : [];\n knownUrls = new Set(rows.map(r => r.article_url));\n} catch (e) {\n console.log(\"Dedup read failed, continuing without dedup:\", e.message);\n}\n\nconst newArticles = articles.filter(a => !knownUrls.has(a.article_url));\nconst skippedCount = articles.length - newArticles.length;\n\nif (newArticles.length === 0) {\n return [{ json: { status: \"no_new_articles\", skippedCount, articles: [], watchlist } }];\n}\nreturn [{ json: { newArticles, skippedCount, watchlist, fetchedAt } }];" }, "id": "a7df55ae-3327-41ab-bd5f-2c284a2fe8bf", "name": "Deduplicate Against Supabase", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ -1696, 352 ] }, { "parameters": { "jsCode": "// Aggregate for Groq — same payload for all 3 Groq nodes\nconst newArticles = $json.newArticles || [];\nconst watchlist = $json.watchlist || [];\nreturn [{\n json: {\n articles: newArticles,\n watchlist,\n articleCount: newArticles.length\n }\n}];" }, "id": "026d9aea-3811-4293-a948-ccb76db307c0", "name": "Aggregate for Groq", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ -1472, 352 ] }, { "parameters": { "method": "POST", "url": "https://api.groq.com/openai/v1/chat/completions", "sendHeaders": true, "headerParameters": { "parameters": [ { "name": "Content-Type", "value": "application/json" }, { "name": "Authorization", "value": "=Bearer {{ $env.GROQ_API_KEY }}" } ] }, "sendBody": true, "specifyBody": "json", "jsonBody": "={{ JSON.stringify({ \"model\": \"llama-3.3-70b-versatile\", \"temperature\": 0.1, \"max_tokens\": 4000, \"messages\": [ { \"role\": \"system\", \"content\": \"You are a financial journalist analyst. Extract the single core claim from each article. Return valid JSON only. No markdown, no preamble, no explanation.\" }, { \"role\": \"user\", \"content\": \"For each article extract the single most important factual claim. Ignore editorial framing, opinion, and context.\\nReturn ONLY a JSON array:\\n[{\\n \\\"article_url\\\": \\\"\\\",\\n \\\"core_claim\\\": \\\"\\\",\\n \\\"claim_type\\\": \\\"\\\"\\n}]\\n\\nArticles:\\n\" + JSON.stringify($json.articles.map(a => ({ article_url: a.article_url, headline: a.headline, summary: a.summary }))) } ] }) }}", "options": {} }, "id": "a1748c32-66c3-46fc-a285-8b53095e240c", "name": "Groq 1: Core Claim Extractor", "type": "n8n-nodes-base.httpRequest", "typeVersion": 4.2, "position": [ -1216, 32 ] }, { "parameters": { "method": "POST", "url": "https://api.groq.com/openai/v1/chat/completions", "sendHeaders": true, "headerParameters": { "parameters": [ { "name": "Content-Type", "value": "application/json" }, { "name": "Authorization", "value": "=Bearer {{ $env.GROQ_API_KEY }}" } ] }, "sendBody": true, "specifyBody": "json", "jsonBody": "={{ JSON.stringify({ \"model\": \"llama-3.3-70b-versatile\", \"temperature\": 0.1, \"max_tokens\": 4000, \"messages\": [ { \"role\": \"system\", \"content\": \"You are an analyst who detects whether financial news is genuinely new or recycled narrative. Return valid JSON only. No markdown, no preamble.\" }, { \"role\": \"user\", \"content\": \"Score each article's information age — how fresh is the underlying claim?\\n\\nScoring guide:\\n 80-100: Genuinely new — first time this specific claim has been made, new data, new event\\n 50-79: Partially new — new development in ongoing story, adds something to existing narrative\\n 20-49: Likely recycled — same claim seen before, new framing but old underlying information\\n 0-19: Recycled — commentary, synthesis, or restatement of weeks-old news\\n\\nReturn ONLY a JSON array:\\n[{\\n \\\"article_url\\\": \\\"\\\",\\n \\\"information_age_score\\\": ,\\n \\\"is_recycled\\\": ,\\n \\\"narrative_label\\\": \\\"<3-5 word phrase naming the narrative e.g. AI chip export restrictions>\\\"\\n}]\\n\\nArticles:\\n\" + JSON.stringify($json.articles.map(a => ({ article_url: a.article_url, headline: a.headline, summary: a.summary, published_at: a.published_at, source: a.source }))) } ] }) }}", "options": {} }, "id": "dfb7a323-152c-4820-a447-96c16a86a7a5", "name": "Groq 2: Information Age Scorer", "type": "n8n-nodes-base.httpRequest", "typeVersion": 4.2, "position": [ -1216, 320 ] }, { "parameters": { "method": "POST", "url": "https://api.groq.com/openai/v1/chat/completions", "sendHeaders": true, "headerParameters": { "parameters": [ { "name": "Content-Type", "value": "application/json" }, { "name": "Authorization", "value": "=Bearer {{ $env.GROQ_API_KEY }}" } ] }, "sendBody": true, "specifyBody": "json", "jsonBody": "={{ JSON.stringify({ \"model\": \"llama-3.3-70b-versatile\", \"temperature\": 0.1, \"max_tokens\": 4000, \"messages\": [ { \"role\": \"system\", \"content\": \"You are a financial entity recognition system. Identify which watchlist companies appear in each article, including informal references via aliases. Return valid JSON only. No markdown, no preamble.\" }, { \"role\": \"user\", \"content\": \"For each article identify which companies from the watchlist are mentioned. Use aliases to catch informal references (e.g. \\\"Jensen\\\" or \\\"Jensen Huang\\\" -> NVIDIA).\\n\\nReturn ONLY a JSON array:\\n[{\\n \\\"article_url\\\": \\\"\\\",\\n \\\"companies_mentioned\\\": [\\n { \\\"ticker\\\": \\\"\\\", \\\"companyName\\\": \\\"\\\" }\\n ]\\n}]\\nReturn empty array for companies_mentioned if none match.\\n\\nWatchlist: \" + JSON.stringify($json.watchlist) + \"\\n\\nArticles:\\n\" + JSON.stringify($json.articles.map(a => ({ article_url: a.article_url, headline: a.headline, summary: a.summary }))) } ] }) }}", "options": {} }, "id": "0046ad09-2441-4f08-a7f1-2723f6373276", "name": "Groq 3: Company Mention Extractor", "type": "n8n-nodes-base.httpRequest", "typeVersion": 4.2, "position": [ -1216, 624 ] }, { "parameters": { "jsCode": "// Signal Assembler — merge 3 Groq outputs by article_url\nfunction genUuid() {\n return 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, function (c) {\n const r = Math.floor(Math.random() * 16);\n const v = c === 'x' ? r : (r & 0x3) | 0x8;\n return v.toString(16);\n });\n}\n\nfunction parseGroq(item) {\n if (!item || !item.json) return null;\n let content = null;\n const j = item.json;\n try {\n content = j.choices && j.choices[0] && j.choices[0].message\n ? j.choices[0].message.content : null;\n } catch (e) { content = null; }\n if (content == null) {\n // some HTTP nodes nest the body under .data / .body\n const body = j.data || j.body;\n if (body && body.choices) content = body.choices[0].message.content;\n }\n if (content == null) return null;\n let text = String(content).trim();\n text = text.replace(/^\\`\\`\\`(?:json)?/i, \"\").replace(/\\`\\`\\`$/, \"\").trim();\n const start = text.indexOf(\"[\");\n const end = text.lastIndexOf(\"]\");\n if (start !== -1 && end !== -1 && end > start) text = text.slice(start, end + 1);\n try { return JSON.parse(text); }\n catch (e) { console.log(\"Groq parse failure:\", e.message); return null; }\n}\n\nconst all = $input.all();\nconst c1 = parseGroq(all[0]) || []; // Core Claim Extractor\nconst c2 = parseGroq(all[1]) || []; // Information Age Scorer\nconst c3 = parseGroq(all[2]) || []; // Company Mention Extractor\n\nconst byUrl = {};\nconst ensure = (url) => (byUrl[url] = byUrl[url] || { article_url: url });\n\nfor (const r of (Array.isArray(c1) ? c1 : [])) {\n if (!r || !r.article_url) continue;\n const o = ensure(r.article_url);\n o.core_claim = r.core_claim != null ? r.core_claim : null;\n o.claim_type = r.claim_type != null ? r.claim_type : null;\n}\nfor (const r of (Array.isArray(c2) ? c2 : [])) {\n if (!r || !r.article_url) continue;\n const o = ensure(r.article_url);\n o.information_age_score = typeof r.information_age_score === \"number\" ? r.information_age_score : null;\n o.is_recycled = typeof r.is_recycled === \"boolean\" ? r.is_recycled : null;\n o.narrative_label = r.narrative_label != null ? r.narrative_label : null;\n}\nfor (const r of (Array.isArray(c3) ? c3 : [])) {\n if (!r || !r.article_url) continue;\n ensure(r.article_url).companies_mentioned = r.companies_mentioned || [];\n}\n\nlet sourceArticles = [];\ntry { sourceArticles = $('Aggregate for Groq').first().json.articles || []; } catch (e) {}\nconst metaByUrl = {};\nfor (const a of sourceArticles) metaByUrl[a.article_url] = a;\n\nconst runId = genUuid();\nconst now = new Date().toISOString();\n\nconst records = Object.values(byUrl).map(o => {\n const meta = metaByUrl[o.article_url] || {};\n return {\n article_url: o.article_url,\n headline: meta.headline || null,\n summary: meta.summary || null,\n source: meta.source || null,\n published_at: meta.published_at || null,\n core_claim: o.core_claim || null,\n claim_type: o.claim_type || null,\n information_age_score: o.information_age_score == null ? null : o.information_age_score,\n is_recycled: o.is_recycled == null ? null : o.is_recycled,\n narrative_label: o.narrative_label || null,\n companies_mentioned: o.companies_mentioned || [],\n first_appearance_date: now,\n appearance_count: 1,\n agent_run_id: runId,\n processed_at: now\n };\n});\n\n// One item per record so the Supabase upsert maps cleanly.\n// Run-level metadata is recomputed by Generate Report from these records.\nreturn records.map(r => ({ json: r }));" }, "id": "4590b555-cae2-4bb3-bdb6-fa11f1c9602f", "name": "Signal Assembler", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ -736, 352 ] }, { "parameters": { "operation": "upsert" }, "id": "4db6b5bb-f5fa-4e39-b5aa-95a1ed7ede07", "name": "Insert to Supabase", "type": "n8n-nodes-base.supabase", "typeVersion": 1, "position": [ -512, 352 ] }, { "parameters": { "jsCode": "// Generate Report — build the markdown signal report\nconst records = $('Signal Assembler').all().map(i => i.json);\nconst runId = records.length ? records[0].agent_run_id : \"n/a\";\nconst runTimestamp = new Date().toISOString();\n\nconst total = records.length;\nconst novel = records.filter(r => (r.information_age_score || 0) >= 60);\nconst recycled = records.filter(r => r.is_recycled === true);\nconst novelCount = novel.length;\nconst recycledCount = recycled.length;\n\nconst fmt = v => (v == null || v === \"\" ? \"—\" : v);\nconst companiesOf = r => {\n const cm = r.companies_mentioned || [];\n return cm.length ? cm.map(c => c.ticker || c.companyName).join(\", \") : \"None\";\n};\n\n// company coverage\nconst compStats = {};\nfor (const r of records) {\n for (const c of (r.companies_mentioned || [])) {\n const key = c.ticker || c.companyName;\n if (!key) continue;\n if (!compStats[key]) compStats[key] = { ticker: c.ticker || \"—\", name: c.companyName || key, count: 0, sum: 0 };\n compStats[key].count++;\n compStats[key].sum += (r.information_age_score || 0);\n }\n}\nconst companyList = Object.values(compStats).sort((a, b) => b.count - a.count);\n\n// narrative clusters\nconst clusters = {};\nfor (const r of records) {\n const label = r.narrative_label || \"Unlabeled\";\n if (!clusters[label]) clusters[label] = { count: 0, sum: 0, sources: new Set() };\n clusters[label].count++;\n clusters[label].sum += (r.information_age_score || 0);\n if (r.source) clusters[label].sources.add(r.source);\n}\nconst clusterList = Object.entries(clusters)\n .map(([label, c]) => ({ label, count: c.count, avg: Math.round(c.sum / c.count), sources: [...c.sources] }))\n .sort((a, b) => b.count - a.count);\nconst topNarrative = clusterList.length ? clusterList[0].label : \"—\";\n\n// classification buckets\nconst bucket = (lo, hi) => records.filter(r => (r.information_age_score || 0) >= lo && (r.information_age_score || 0) <= hi).length;\nconst b80 = bucket(80, 100), b50 = bucket(50, 79), b20 = bucket(20, 49), b0 = bucket(0, 19);\n\nconst L = [];\nL.push(\"# Media Narrative Tracker — Signal Report\");\nL.push(\"**Run ID:** \" + runId);\nL.push(\"**Generated:** \" + runTimestamp);\nL.push(\"**Period:** Last 6 hours\");\nL.push(\"\");\nL.push(\"---\");\nL.push(\"\");\nL.push(\"## Executive Summary\");\nL.push(\"\");\nL.push(\"| Metric | Value |\");\nL.push(\"|--------|-------|\");\nL.push(\"| Articles processed | \" + total + \" |\");\nL.push(\"| Novel signals (score ≥ 60) | \" + novelCount + \" |\");\nL.push(\"| Recycled narratives | \" + recycledCount + \" |\");\nL.push(\"| Companies mentioned | \" + (companyList.map(c => c.name).join(\", \") || \"None\") + \" |\");\nL.push(\"| Most active narrative | \" + topNarrative + \" |\");\nL.push(\"\");\nL.push(\"---\");\nL.push(\"\");\nL.push(\"## Novel Signals\");\nL.push(\"*Articles with information age score ≥ 60 — worth reading*\");\nL.push(\"\");\nif (!novel.length) {\n L.push(\"_No novel signals in this run._\");\n L.push(\"\");\n} else {\n for (const r of novel.sort((a, b) => (b.information_age_score || 0) - (a.information_age_score || 0))) {\n L.push(\"### \" + fmt(r.headline));\n L.push(\"**Source:** \" + fmt(r.source) + \" | **Score:** \" + fmt(r.information_age_score) + \"/100 | **Claim type:** \" + fmt(r.claim_type) + \" | **Companies:** \" + companiesOf(r));\n L.push(\"**Core claim:** \" + fmt(r.core_claim));\n L.push(\"**Narrative:** \" + fmt(r.narrative_label));\n L.push(\"**Published:** \" + fmt(r.published_at));\n L.push(\"**URL:** \" + fmt(r.article_url));\n L.push(\"\");\n }\n}\nL.push(\"---\");\nL.push(\"\");\nL.push(\"## Recycled Narratives\");\nL.push(\"*Articles with information age score < 40 — noise*\");\nL.push(\"\");\nconst recList = records.filter(r => (r.information_age_score || 0) < 40);\nif (!recList.length) {\n L.push(\"_No recycled narratives in this run._\");\n L.push(\"\");\n} else {\n L.push(\"| Headline | Source | Score | Narrative Label |\");\n L.push(\"|----------|--------|-------|-----------------|\");\n for (const r of recList.sort((a, b) => (a.information_age_score || 0) - (b.information_age_score || 0))) {\n L.push(\"| \" + fmt(r.headline) + \" | \" + fmt(r.source) + \" | \" + fmt(r.information_age_score) + \" | \" + fmt(r.narrative_label) + \" |\");\n }\n L.push(\"\");\n}\nL.push(\"---\");\nL.push(\"\");\nL.push(\"## Narrative Clusters\");\nL.push(\"*Articles grouped by narrative label*\");\nL.push(\"\");\nif (!clusterList.length) {\n L.push(\"_No narratives to cluster._\");\n L.push(\"\");\n} else {\n for (const c of clusterList) {\n L.push(\"**\" + c.label + \"** — \" + c.count + \" articles, avg score: \" + c.avg);\n L.push(\"Sources: \" + (c.sources.join(\", \") || \"—\"));\n L.push(\"\");\n }\n}\nL.push(\"---\");\nL.push(\"\");\nL.push(\"## Company Coverage\");\nL.push(\"*Which watchlist companies appeared in this run*\");\nL.push(\"\");\nL.push(\"| Company | Ticker | Mentions | Avg Age Score |\");\nL.push(\"|---------|--------|----------|---------------|\");\nif (!companyList.length) {\n L.push(\"| None | — | 0 | — |\");\n} else {\n for (const c of companyList) {\n L.push(\"| \" + c.name + \" | \" + c.ticker + \" | \" + c.count + \" | \" + Math.round(c.sum / c.count) + \" |\");\n }\n}\nL.push(\"\");\nL.push(\"---\");\nL.push(\"\");\nL.push(\"## Signal Classification Summary\");\nL.push(\"\");\nL.push(\"| Score Range | Label | Count |\");\nL.push(\"|-------------|-------|-------|\");\nL.push(\"| 80–100 | Genuinely New | \" + b80 + \" |\");\nL.push(\"| 50–79 | Partially New | \" + b50 + \" |\");\nL.push(\"| 20–49 | Likely Recycled | \" + b20 + \" |\");\nL.push(\"| 0–19 | Recycled | \" + b0 + \" |\");\nL.push(\"\");\nL.push(\"---\");\nL.push(\"\");\nL.push(\"## Known Limitations\");\nL.push(\"- RSS coverage limited to 5 outlets (FT, Reuters, Seeking Alpha, CNBC, Yahoo Finance). Trade press narratives may not appear until picked up here.\");\nL.push(\"- Information age scores are Groq estimates, not validated against a labeled ground truth dataset.\");\nL.push(\"- Narrative label consistency across runs is unvalidated. Same narrative may receive slightly different labels.\");\nL.push(\"\");\nL.push(\"---\");\nL.push(\"\");\nL.push(\"*Mycroft — Media Narrative Tracker | Agent 3*\");\nL.push(\"*Stack: n8n + Groq (llama-3.3-70b-versatile) + Supabase*\");\n\nconst report = L.join(\"\\n\");\n\nreturn [{\n json: {\n report,\n runId,\n totalProcessed: total,\n novelCount,\n recycledCount\n }\n}];" }, "id": "6e48fa80-fa63-415c-b35e-8e7e84763e89", "name": "Generate Report", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ -304, 352 ] }, { "parameters": { "operation": "insert" }, "id": "1e1931f6-d96f-42e7-94e4-7489c170291d", "name": "Store Report in Supabase", "type": "n8n-nodes-base.supabase", "typeVersion": 1, "position": [ -80, 352 ] }, { "parameters": { "respondWith": "text", "responseBody": "={{ $('Generate Report').first().json.report }}", "options": { "responseHeaders": { "entries": [ { "name": "Content-Type", "value": "text/markdown" } ] } } }, "id": "566e08e0-4949-40a0-bbfc-3c8ee8b3e31e", "name": "Pipeline Response", "type": "n8n-nodes-base.respondToWebhook", "typeVersion": 1.1, "position": [ 144, 352 ] }, { "parameters": { "numberInputs": 5 }, "type": "n8n-nodes-base.merge", "typeVersion": 3.2, "position": [ -2128, 320 ], "id": "4e622c00-07ec-4ec5-8287-f67108180941", "name": "Merge" }, { "parameters": { "numberInputs": 3 }, "type": "n8n-nodes-base.merge", "typeVersion": 3.2, "position": [ -944, 320 ], "id": "84d641d9-f021-4e8e-a7d0-f07320fd2976", "name": "Merge1" }, { "id": "10bfc194-49bc-54d9-b149-11b2bbb03d4c", "name": "Publication · Overview and setup", "type": "n8n-nodes-base.stickyNote", "typeVersion": 1, "position": [ -3744, -48 ], "parameters": { "content": "# Media Narrative Tracker — Mycroft Agent 3 (Groq)\n\n## Purpose and structure\nThis folder contains the **Media Narrative Tracker — Mycroft Agent 3 (Groq)** workflow. Its executable nodes use `code`, `httpRequest`, `merge`, `respondToWebhook`, `scheduleTrigger`, `supabase`, `webhook`. 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\nCron Trigger, Manual Pipeline Webhook\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": { "Cron Trigger": { "main": [ [ { "node": "Fetch Watchlist", "type": "main", "index": 0 } ] ] }, "Manual Pipeline Webhook": { "main": [ [ { "node": "Fetch Watchlist", "type": "main", "index": 0 } ] ] }, "Fetch Watchlist": { "main": [ [ { "node": "Fetch FT RSS", "type": "main", "index": 0 }, { "node": "Fetch Reuters RSS", "type": "main", "index": 0 }, { "node": "Fetch Seeking Alpha RSS", "type": "main", "index": 0 }, { "node": "Fetch CNBC RSS", "type": "main", "index": 0 }, { "node": "Fetch Yahoo Finance RSS", "type": "main", "index": 0 } ] ] }, "Fetch FT RSS": { "main": [ [ { "node": "Merge", "type": "main", "index": 0 } ] ] }, "Fetch Reuters RSS": { "main": [ [ { "node": "Merge", "type": "main", "index": 1 } ] ] }, "Parse & Clean Articles": { "main": [ [ { "node": "Deduplicate Against Supabase", "type": "main", "index": 0 } ] ] }, "Deduplicate Against Supabase": { "main": [ [ { "node": "Aggregate for Groq", "type": "main", "index": 0 } ] ] }, "Aggregate for Groq": { "main": [ [ { "node": "Groq 1: Core Claim Extractor", "type": "main", "index": 0 }, { "node": "Groq 2: Information Age Scorer", "type": "main", "index": 0 }, { "node": "Groq 3: Company Mention Extractor", "type": "main", "index": 0 } ] ] }, "Groq 1: Core Claim Extractor": { "main": [ [ { "node": "Merge1", "type": "main", "index": 0 } ] ] }, "Groq 2: Information Age Scorer": { "main": [ [ { "node": "Merge1", "type": "main", "index": 1 } ] ] }, "Signal Assembler": { "main": [ [ { "node": "Insert to Supabase", "type": "main", "index": 0 } ] ] }, "Insert to Supabase": { "main": [ [ { "node": "Generate Report", "type": "main", "index": 0 } ] ] }, "Generate Report": { "main": [ [ { "node": "Store Report in Supabase", "type": "main", "index": 0 } ] ] }, "Store Report in Supabase": { "main": [ [ { "node": "Pipeline Response", "type": "main", "index": 0 } ] ] }, "Groq 3: Company Mention Extractor": { "main": [ [ { "node": "Merge1", "type": "main", "index": 2 } ] ] }, "Fetch Seeking Alpha RSS": { "main": [ [ { "node": "Merge", "type": "main", "index": 2 } ] ] }, "Merge": { "main": [ [ { "node": "Parse & Clean Articles", "type": "main", "index": 0 } ] ] }, "Fetch CNBC RSS": { "main": [ [ { "node": "Merge", "type": "main", "index": 3 } ] ] }, "Fetch Yahoo Finance RSS": { "main": [ [ { "node": "Merge", "type": "main", "index": 4 } ] ] }, "Merge1": { "main": [ [ { "node": "Signal Assembler", "type": "main", "index": 0 } ] ] } }, "active": false, "settings": { "executionOrder": "v1" } }