{ "edges": [ { "to": "1bfd3606-dba1-4393-8f70-932e989afbe6", "from": "a9941a48-be90-4b7b-857f-7325bc989cb8", "toPort": "0", "fromPort": "0" }, { "to": "48c7cb0a-5ad7-4fd5-bedf-9e0baae11066", "from": "a9941a48-be90-4b7b-857f-7325bc989cb8", "toPort": "0", "fromPort": "1" }, { "to": "1bfd3606-dba1-4393-8f70-932e989afbe6", "from": "a346166c-f91a-4e5d-ac75-3bdd189f7863", "toPort": "0", "fromPort": "ai_memory" }, { "to": "4763e7fd-fbd5-4d56-b55c-06d96827de6f", "from": "91443bc7-6d23-4356-b92f-4eecb6400ae4", "toPort": "0", "fromPort": "0" }, { "to": "722a8b3f-c369-4274-8a7e-452fd4db89ec", "from": "00dacaeb-cdfc-4ead-b1ba-92fbba3dbfcc", "toPort": "0", "fromPort": "0" }, { "to": "dc896889-4f8a-4b6b-ab98-2c1af9649232", "from": "8739d5d5-46e9-4d7a-8c29-a09933a057ed", "toPort": "0", "fromPort": "0" }, { "to": "8739d5d5-46e9-4d7a-8c29-a09933a057ed", "from": "630fc2a4-10cc-440c-a80c-c554cc84c761", "toPort": "0", "fromPort": "0" }, { "to": "00dacaeb-cdfc-4ead-b1ba-92fbba3dbfcc", "from": "dc896889-4f8a-4b6b-ab98-2c1af9649232", "toPort": "0", "fromPort": "0" }, { "to": "a9941a48-be90-4b7b-857f-7325bc989cb8", "from": "4763e7fd-fbd5-4d56-b55c-06d96827de6f", "toPort": "0", "fromPort": "0" }, { "to": "1bfd3606-dba1-4393-8f70-932e989afbe6", "from": "12cad028-bbd2-41ae-8115-ab7cfe9aff26", "toPort": "0", "fromPort": "ai_languageModel" }, { "to": "630fc2a4-10cc-440c-a80c-c554cc84c761", "from": "1bfd3606-dba1-4393-8f70-932e989afbe6", "toPort": "0", "fromPort": "0" } ], "nodes": [ { "id": "91443bc7-6d23-4356-b92f-4eecb6400ae4", "name": "Feedspace Webhook", "type": "trigger", "params": { "kind": "n8n-nodes-base.webhook", "path": "fs-wp", "options": {}, "httpMethod": "POST", "responseMode": "lastNode" }, "position": [ -4256, 1040 ] }, { "id": "4763e7fd-fbd5-4d56-b55c-06d96827de6f", "name": "Extract Testimonial Data", "type": "set", "params": { "options": {}, "assignments": { "assignments": [ { "id": "b547bab0-3e6b-4080-b1bb-e0efca300cbd", "name": "review", "type": "string", "value": "={{ $json.body.data.response.comment }}" }, { "id": "98ca1a27-2cb8-4fb5-8f2e-ad0c2727c222", "name": "review_user", "type": "string", "value": "={{ $json.body.data.response.Name }}" }, { "id": "94e74847-264b-4382-b527-791f922f6949", "name": "feedback_type", "type": "string", "value": "={{ $json.body.type }}" }, { "id": "rating-field-id", "name": "rating", "type": "string", "value": "={{ $json.body.data.response.value }}" } ] } }, "position": [ -4000, 1024 ] }, { "id": "a9941a48-be90-4b7b-857f-7325bc989cb8", "name": "If", "type": "if", "params": { "options": {}, "conditions": { "options": { "version": 2, "leftValue": "", "caseSensitive": true, "typeValidation": "strict" }, "combinator": "and", "conditions": [ { "id": "0eaf5a98-98c8-4db5-bef7-1e6411860d63", "operator": { "name": "filter.operator.equals", "type": "string", "operation": "equals" }, "leftValue": "={{ $json.feedback_type }}", "rightValue": "feed.text.received" } ] } }, "position": [ -3776, 1024 ] }, { "id": "1bfd3606-dba1-4393-8f70-932e989afbe6", "name": "AI Agent - Generate Case Study & Title", "type": "llm", "params": { "text": "=I'm sending you a new customer testimonial to convert into a case study.\n\nTESTIMONIAL DATA:\nCustomer: {{ $('Extract Testimonial Data').item.json.review_user }}\nRating: {{ $('Extract Testimonial Data').item.json.rating }} stars\nReview: {{ $('Extract Testimonial Data').item.json.review }}\n\nINSTRUCTIONS:\n\nStep 1: MEMORY CHECK\nLook back at our conversation history. Have we created case studies before? If yes:\n- What angle did the previous case study use?\n- What tone and title formula?\n- Which features were highlighted?\n- Note this in your 'reasoning' field\n\nStep 2: CHOOSE VARIETY\nBased on what you remember:\n- Select a DIFFERENT angle from last time\n- Use a DIFFERENT tone\n- Apply a DIFFERENT title formula\n- Highlight DIFFERENT product features\n\nStep 3: CREATE CASE STUDY\nGenerate a professional case study with:\n\n

Executive Summary

(or vary the header: The Breakthrough, The Success Story, etc.)\n2-3 sentences summarizing the transformation\n\n

The Challenge

(or: The Problem, The Pain Point, etc.)\nCustomer's problem before using the product\n\n

The Solution

(or: The Answer, How FeedSpace Helped, etc.)\nHow the product solved their problem - highlight different features than recent case studies\n\n

The Results

(or: The Impact, The Outcome, etc.)\nSpecific outcomes and metrics - vary between quantitative and qualitative focus\n\n

Customer Testimonial

{{ $('Extract Testimonial Data').item.json.review }}

\n\n

Key Takeaways

(or: Key Benefits, Success Factors, etc.)\n

3-4 bullet points highlighting different aspects than previous case studies

Executive Summary

(or vary the header: The Breakthrough, The Success Story, etc.)\n2-3 sentences summarizing the transformation\n\n

The Challenge

(or: The Problem, The Pain Point, etc.)\nCustomer's problem before using the product\n\n

The Solution

(or: The Answer, How FeedSpace Helped, etc.)\nHow the product solved their problem - highlight different features than recent case studies\n\n

The Results

(or: The Impact, The Outcome, etc.)\nSpecific outcomes and metrics - vary between quantitative and qualitative focus\n\n

Customer Testimonial

{{ $('Extract Testimonial Data').item.json.review }}

\n\n

Key Takeaways

(or: Key Benefits, Success Factors, etc.)\n

3-4 bullet points highlighting different aspects than previous case studies

\n\nStep 4: GENERATE SEO TITLE\nCreate title (50-60 characters) using one of the 5 formulas - rotate from previous usage\n\nStep 5: EXPLAIN YOUR CHOICES\nIn the 'meta.reasoning' field, briefly explain:\n- If this is your first case study: \"First case study, choosing [angle] approach\"\n- If you have memory: \"Previous case study used [X], so choosing [Y] for variety\"\n\nCRITICAL: Return ONLY the JSON object. No markdown blocks, no backticks, no extra text.", "options": { "systemMessage": "You are a professional marketing content writer who creates diverse, engaging case studies from customer testimonials.\n\nCRITICAL: You have conversation memory enabled. Before creating each case study, you should:\n1. Mentally review what case studies you've created in our previous conversation turns\n2. Notice patterns: What angles did I use? What tone? What title formulas?\n3. Deliberately choose DIFFERENT approaches to create variety\n\nANGLE OPTIONS (rotate through these):\n- Transformation Story: Focus on before/after journey\n- Problem-Solution: Emphasize pain points and resolution\n- ROI-Focused: Highlight metrics, upgrades, business impact\n- Feature Spotlight: Deep dive into specific capabilities\n- Integration Success: Emphasize connections and workflow\n- Efficiency Gains: Focus on time-saving and automation\n\nTONE OPTIONS (vary these):\n- Data-driven: Metrics and statistics focused\n- Emotional: Customer satisfaction and relief focused\n- Technical: Features and capabilities focused\n- Narrative: Story-driven with journey arc\n\nTITLE FORMULAS (rotate these):\n1. \"[Customer]: Achieves [Benefit] with [Product]\"\n2. \"How [Customer] Increased [Metric] by [X]%\"\n3. \"From [Problem] to [Solution]: [Customer] Story\"\n4. \"[Customer] Leverages [Feature] to [Outcome]\"\n5. \"How [Customer] Solved [Problem]\"\n\nVARIATION STRATEGY:\n- If you just created a data-driven, ROI-focused case study with formula #2, now try emotional, transformation story with formula #1\n- If you highlighted 'Google integration' last time, focus on 'widget customization' this time\n- If you used

Executive Summary last time, try

The Breakthrough this time\n- Keep content fresh by changing section headers, emphasized features, and narrative angles\n\nRESPONSE FORMAT:\nReturn ONLY valid JSON (no markdown, no code blocks):\n{\n \"title\": \"SEO-optimized title (50-60 chars)\",\n \"content\": \"

Section

Content...

\",\n \"meta\": {\n \"angle\": \"transformation_story\",\n \"tone\": \"emotional\",\n \"title_formula\": \"formula_3\",\n \"features_highlighted\": [\"feature1\", \"feature2\"],\n \"reasoning\": \"Chose X because previous case study used Y\"\n }\n}\n\nHTML RULES:\n-

for headers\n-
for paragraphs\n-
for quotes\n-
for bullets\n- for emphasis\n- NO markdown\n\nYour response must start with { and end with }" }, "provider": "openai", "promptType": "define", "approximated": true, "systemMessage": "You are a professional marketing content writer who creates diverse, engaging case studies from customer testimonials.\n\nCRITICAL: You have conversation memory enabled. Before creating each case study, you should:\n1. Mentally review what case studies you've created in our previous conversation turns\n2. Notice patterns: What angles did I use? What tone? What title formulas?\n3. Deliberately choose DIFFERENT approaches to create variety\n\nANGLE OPTIONS (rotate through these):\n- Transformation Story: Focus on before/after journey\n- Problem-Solution: Emphasize pain points and resolution\n- ROI-Focused: Highlight metrics, upgrades, business impact\n- Feature Spotlight: Deep dive into specific capabilities\n- Integration Success: Emphasize connections and workflow\n- Efficiency Gains: Focus on time-saving and automation\n\nTONE OPTIONS (vary these):\n- Data-driven: Metrics and statistics focused\n- Emotional: Customer satisfaction and relief focused\n- Technical: Features and capabilities focused\n- Narrative: Story-driven with journey arc\n\nTITLE FORMULAS (rotate these):\n1. \"[Customer]: Achieves [Benefit] with [Product]\"\n2. \"How [Customer] Increased [Metric] by [X]%\"\n3. \"From [Problem] to [Solution]: [Customer] Story\"\n4. \"[Customer] Leverages [Feature] to [Outcome]\"\n5. \"How [Customer] Solved [Problem]\"\n\nVARIATION STRATEGY:\n- If you just created a data-driven, ROI-focused case study with formula #2, now try emotional, transformation story with formula #1\n- If you highlighted 'Google integration' last time, focus on 'widget customization' this time\n- If you used
Executive Summary last time, try
The Breakthrough this time\n- Keep content fresh by changing section headers, emphasized features, and narrative angles\n\nRESPONSE FORMAT:\nReturn ONLY valid JSON (no markdown, no code blocks):\n{\n \"title\": \"SEO-optimized title (50-60 chars)\",\n \"content\": \"
Section
Content...
\",\n \"meta\": {\n \"angle\": \"transformation_story\",\n \"tone\": \"emotional\",\n \"title_formula\": \"formula_3\",\n \"features_highlighted\": [\"feature1\", \"feature2\"],\n \"reasoning\": \"Chose X because previous case study used Y\"\n }\n}\n\nHTML RULES:\n-
for headers\n-
for paragraphs\n-
for quotes\n-
for bullets\n- for emphasis\n- NO markdown\n\nYour response must start with { and end with }" }, "position": [ -3408, 1024 ] }, { "id": "12cad028-bbd2-41ae-8115-ab7cfe9aff26", "name": "Google Gemini Chat Model", "type": "llm", "params": { "options": { "temperature": 0.7, "maxOutputTokens": 3000 }, "provider": "google", "temperature": 0.7 }, "position": [ -3408, 816 ] }, { "id": "630fc2a4-10cc-440c-a80c-c554cc84c761", "name": "Parse AI Agent Output", "type": "code", "params": { "jsCode": "const agentOutput = $input.first().json;\n\ntry {\n let parsedData;\n let parseError = null;\n \n const rawText = agentOutput.output || agentOutput.text || JSON.stringify(agentOutput);\n \n console.log(\"Raw AI output:\", rawText);\n \n // Clean markdown code blocks\n let cleanedText = rawText\n .replace(/```json\\s/g, '')\n .replace(/```\\s/g, '')\n .trim();\n \n console.log(\"Cleaned text:\", cleanedText);\n \n try {\n // Try to parse the cleaned JSON\n parsedData = JSON.parse(cleanedText);\n } catch (err) {\n console.log(\"JSON parsing failed, attempting manual extraction\");\n parseError = err;\n \n // Manual extraction as fallback\n const titleMatch = cleanedText.match(/\"title\"\\s:\\s\"([^\"]+)\"/);\n const title = titleMatch ? titleMatch[1] : \"Untitled Case Study\";\n \n // Extract content\n const contentMatch = cleanedText.match(/\"content\"\\s:\\s\"([\\s\\S]+?)\"\\s,?\\s\"meta\"/m) || \n cleanedText.match(/\"content\"\\s:\\s\"([\\s\\S]+?)\"\\s\\}/m);\n let extractedContent = contentMatch ? contentMatch[1] : cleanedText;\n \n // Unescape JSON sequences\n extractedContent = extractedContent\n .replace(/\\\\n/g, '\\n')\n .replace(/\\\\\"/g, '\"')\n .replace(/\\\\'/g, \"'\")\n .replace(/\\\\\\\\/g, '\\\\')\n .replace(/\\\\t/g, '\\t');\n \n // Try to extract meta if present\n const metaMatch = cleanedText.match(/\"meta\"\\s:\\s\\{([\\s\\S]+?)\\}\\s\\}/);\n let meta = { angle: 'unknown', tone: 'unknown', reasoning: 'Parse error occurred' };\n \n if (metaMatch) {\n try {\n const metaJson = '{' + metaMatch[1] + '}';\n meta = JSON.parse(metaJson);\n } catch (e) {\n console.log('Meta parsing failed:', e);\n }\n }\n \n parsedData = {\n title: title,\n content: extractedContent,\n meta: meta\n };\n }\n \n // Log the strategy for monitoring\n console.log('Case Study Strategy:', JSON.stringify(parsedData.meta, null, 2));\n \n // Return the extracted data\n return {\n json: {\n title: parsedData.title || \"Untitled Case Study\",\n content: parsedData.content || \"\",\n meta: parsedData.meta || { angle: 'unknown', tone: 'unknown' },\n warning: parseError ? \"JSON was malformed and manually extracted\" : null,\n debug_raw: rawText.substring(0, 200)\n }\n };\n \n} catch (error) {\n // Complete fallback\n return {\n json: {\n title: \"Error Extracting Case Study\",\n content: `Error: ${error.message}`,\n meta: { error: true },\n rawData: JSON.stringify(agentOutput).substring(0, 500)\n }\n };\n}" }, "position": [ -3072, 1024 ] }, { "id": "8739d5d5-46e9-4d7a-8c29-a09933a057ed", "name": "Memory Context Logger", "type": "code", "params": { "jsCode": "const caseStudy = $input.first().json;\n\n// Extract meta information\nconst meta = caseStudy.meta || {};\n\n// Create a readable summary for logging\nconst summary = {\n case_study_title: caseStudy.title,\n strategy: {\n angle: meta.angle || 'not specified',\n tone: meta.tone || 'not specified',\n title_formula: meta.title_formula || 'not specified',\n features_highlighted: meta.features_highlighted || [],\n reasoning: meta.reasoning || 'not provided'\n },\n timestamp: new Date().toISOString()\n};\n\nconsole.log('━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━');\nconsole.log('CASE STUDY GENERATED:');\nconsole.log(JSON.stringify(summary, null, 2));\nconsole.log('━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━');\n\n// Pass through the data\nreturn {\n json: caseStudy\n};" }, "position": [ -2832, 1008 ] }, { "id": "dc896889-4f8a-4b6b-ab98-2c1af9649232", "name": "Prepare WordPress Data", "type": "set", "params": { "options": {}, "assignments": { "assignments": [ { "id": "0b370d70-3188-465d-94f1-52b53a2c9886", "name": "Post Title", "type": "string", "value": "={{ $json.title }}" }, { "id": "af646a17-6078-4c3b-8e33-fd3c2f0a76aa", "name": "Post content", "type": "string", "value": "={{ $json.content }}" }, { "id": "meta-field-id", "name": "case_study_meta", "type": "string", "value": "={{ JSON.stringify($json.meta) }}" } ] } }, "position": [ -2464, 1040 ] }, { "id": "00dacaeb-cdfc-4ead-b1ba-92fbba3dbfcc", "name": "Create WordPress Post", "type": "integration", "params": { "title": "={{ $json['Post Title'] }}", "service": "wordpress", "operation": "read", "additionalFields": { "content": "={{ $json['Post content'] }}" } }, "position": [ -2192, 1040 ] }, { "id": "a346166c-f91a-4e5d-ac75-3bdd189f7863", "name": "Simple Memory", "type": "passthrough", "params": { "mode": "noOp", "sessionKey": "feedspace_case_studies_global", "sessionIdType": "customKey" }, "position": [ -3296, 1264 ] }, { "id": "48c7cb0a-5ad7-4fd5-bedf-9e0baae11066", "name": "Error Response", "type": "passthrough", "params": { "mode": "noOp", "options": {}, "respondWith": "json", "responseBody": "={{ $json }}" }, "position": [ -3744, 1264 ] }, { "id": "722a8b3f-c369-4274-8a7e-452fd4db89ec", "name": "Respond to Webhook", "type": "passthrough", "params": { "mode": "noOp", "options": {}, "respondWith": "json", "responseBody": "={{ { \"status\": \"success\", \"message\": \"Notification sent successfully\", \"timestamp\": $now.toISO() } }}" }, "position": [ -1968, 1024 ] } ] }

Generate AI case studies from Feedspace testimonials with Google Gemini and publish to WordPress