Workflow Templates

Description This workflow lets you upload a PDF document and automatically analyze it with AI. It extracts the text, summarizes the content, flags key clauses or risks, and then delivers the results via Gmail while also storing them in Google Sheets for tracking. It’s designed for legal, compliance, or contract review use cases, but can be adapted for any document analysis scenario. Test it here: PDF Document Assistant 🔹 Instructions / Setup Webhook Input Upload a PDF document by sending it to the webhook URL. Extract from File The workflow extracts text from the uploaded PDF. Pre-processing (Code Node) Cleans and formats extracted text to remove unwanted line breaks or artifacts. Basic LLM Chain (OpenAI) Summarizes or restructures document content using OpenAI. Adjust the prompt inside to fit your analysis needs (summary, risk flags, clause extraction). Post-processing (Code Node) Further structures the AI output into a clean format (JSON, HTML, or plain text). AI Agent (OpenAI) Runs deeper analysis, answers questions, and extracts insights. Gmail Sends the results to a recipient. Configure Gmail credentials and set your recipient address. Google Sheets Appends results to a Google Sheet for record-keeping or audits. Respond to Webhook Sends a quick acknowledgment back to confirm the document was received. 🔹 Credentials Needed OpenAI API key (for Chat Model + Agent) Gmail account (OAuth2) Google Sheets account (OAuth2) 🔹 Example Use Case Upload a contract PDF → workflow extracts clauses → AI flags risky terms → Gmail sends formatted summary → results stored in Google Sheets.

AI LinkedIn Content Bot with Approval Loop This n8n workflow transforms your Telegram messenger into a personal assistant for creating and publishing LinkedIn posts. You can simply send an idea as a text or voice message, collaboratively edit the AI's suggestion in a chat, and then publish the finished post directly to LinkedIn just by saying "Okay." What You'll Need to Get Started Before you can use this workflow, you'll need a few prerequisites set up. This workflow connects three different services, so you will need API credentials for each: Telegram Bot API Key: You can get this by talking to the "BotFather" on Telegram. It will guide you through creating your new bot and provide you with the API token. New Chat with Telegram BotFather OpenAI API Key: This is required for the "Speech to Text" and "AI Agent" nodes. You'll need an account with OpenAI to generate this key. [OpenAI API Platform](https://platform.openai.com ) Blotato API Key: This service is used to publish the final post to LinkedIn. You'll need a Blotato account and to connect your LinkedIn profile there to get the key. [Blotato platform for social media publishing] Once you have these keys, you can add them to the corresponding credentials in your n8n instance. How the Workflow Operates, Step-by-Step Here is a detailed breakdown of how the workflow processes your request and handles the publishing. 1\. Input & Initial Processing This phase captures your idea and converts it into usable text. | Node Name | Role in Workflow | | :--- | :--- | | Start: Telegram Message | This Telegram Trigger node initiates the entire process upon receiving any message from you in the bot. | | Prepare Input | Consolidates the message content, ensuring the AI receives only one clean text input. | | Check: ist it a Voice? | Checks the incoming message for text. If text is empty, it proceeds to voice handling. | | Get Voice File | If a voice note is detected, this node downloads the raw audio file from Telegram. | | Speech to Text | This node uses the OpenAI Whisper API to convert the downloaded audio file into a text string. | 2\. AI Core & Iteration Loop This is the central dialogue system where the AI drafts the content and engages in the feedback loop. | Node Name | Role in Workflow | | :--- | :--- | | AI: Draft & Revise Post | The main logic agent. It analyzes your request, applies the "System Prompt" rules, drafts the post, and handles revisions based on your feedback. | | OpenAI Chat Model | Defines the large language model (LLM) used for generating and revising the post. | | Window Buffer Memory | A memory buffer that stores the last turns of the conversation, allowing the AI to maintain context when you request changes (e.g., "Make it shorter"). | | Check if Approved | This crucial node detects the specific JSON structure the AI outputs only when you provide an approval keyword (like "ok" or "approved"). | | Post Suggestion Or Ask For Approval | Sends the AI's post draft back to your Telegram chat for review and feedback. | AI Agent System Prompt (Internal Instructions - English) The agent operates under a strict prompt that dictates its behavior and formatting (found within the AI: Draft & Revise Post node): > You are a LinkedIn Content Creator Agent for Telegram. > Keep the confirmation process, but change the output format as follows: > > Your Task > Analyze the user's message: > > * Topic > * Goal (e.g., reach, show expertise, recruiting, personal branding, leads) > * Target Audience > * Tonality (e.g., factual, personal, bold, inspiring) > > Create a LinkedIn post as ONE continuous text: > > * Strong hook in the first 1–2 lines. > * Clear main part with added value, story, example, or insight. > * Optional Call-to-Action (e.g., question to the community, invitation to exchange). > * Integrate hashtags at the end of the post (5–12 suitable hashtags, mix of niche + somewhat broader). > * Readable on LinkedIn: short paragraphs, emojis only sparingly. > > Present the suggestion to the user in the following format: > > Headline: Post Proposal: > Below that, the complete LinkedIn post (incl. hashtags at the end in the same text). > > Ask for feedback: > For example: > "Any changes? (Tone, length, formality, personal vs. professional, more technical content, different hashtags?)" > > If the user requests changes: > Adjust the post specifically based on the feedback. > Again, output only: > Post Proposal: > the revised complete post. > > If the user says “approved”, “ok”, “sounds good”, or similar: > Return exclusively this JSON, without additional text, without Markdown: > > > { > "Post": "The final LinkedIn post as one text, including hashtags at the end" > } > > > Important: > > * Never output JSON before approval, only normal suggestion text. > * The final output after approval consists of only one field: Post. 3\. Publishing & Status Check Once approved, the workflow handles the publication and monitors the post's status in real-time. | Node Name | Role in Workflow | | :--- | :--- | | Approval: Extract Final Post Text | Parses the incoming JSON, extracting only the clean text ready for publishing. | | Create post with Blotato | Uses the Blotato API to upload the finalized content to your connected LinkedIn account. | | Give Blotat 5s :) | A brief pause to allow the publishing service to start processing the request. | | Check post status | Checks back with Blotato to determine if the post is published, in progress, or failed. | | Published? | Checks if the status is "published" to send the success message. | | In Progress? | Checks if the post is still being processed. If so, it loops back to the next wait period. | | Give Blotat other 5s :) | Pauses the workflow before re-checking the post status, preventing unnecessary API calls. | 4\. Final Notification | Node Name | Role in Workflow | | :--- | :--- | | Send a confirmation message | Sends a confirmation message and the direct link to the published LinkedIn post. | | Send an error message | Sends a notification if the post failed to upload or encountered an error during processing. | 🛠️ Personalizing Your Content Bot The true power of this n8n workflow lies in its flexibility. You can easily modify key components to match your unique brand voice and technical preferences. 1\. Tweak the Content Creator Prompt The personality, tone, and formatting rules for your LinkedIn content are all defined in the System Prompt. Where to find it: Inside the AI: Draft & Revise Post node, under the System Message setting. What to personalize: Adjust the tone, change the formatting rules (e.g., number of hashtags, required emojis), or insert specific details about your industry or target audience. 2\. Switch the AI Model or Provider You can easily swap the language model used for generation. Where to find it: The OpenAI Chat Model node. What to personalize: Model: Swap out the default model for a more powerful or faster alternative (e.g., gpt-4 family, or models from other providers if you change the node). Provider: You can replace the entire Langchain block (including the AI Model and Window Buffer Memory nodes) with an equivalent block using a different provider's Chat/LLM node (e.g., Anthropic, Cohere, or Google Gemini), provided you set up the corresponding credentials and context flow. 3\. Modify Publishing Behavior (Schedule vs. Post) The final step is currently set to publish immediately, but you might prefer to schedule posts. Where to find it: The Create post with Blotato node. What to personalize: Consult the Blotato documentation for alternative operations. Instead of choosing the "Create Post" operation (which often posts immediately), you can typically select a "Schedule Post" or "Add to Queue" operation within the Blotato node. If scheduling, you will need to add a step (e.g., a Set node or another agent prompt) before publishing to calculate and pass a Scheduled Time parameter to the Blotato node.

Automatically fetches daily sales, shifts, and receipts from Loyverse. Calculates gross profit, net operating profit, other key metrics, saves them to a Google Sheet and sends out a daily report via email. Who’s it for This template is for any business owner, manager, or analyst using Loyverse POS who needs more advanced financial reporting. If you're a restaurant, bar, or retail owner who wants to automatically track daily net profit, compare sales to historical averages, and build a custom financial dashboard in Google Sheets, this workflow is for you. How it works / What it does This workflow runs automatically on a daily schedule. It fetches all sales data and receipts from your Loyverse account for the previous business day, defined by your custom shift times (even past midnight). A powerful Code node then processes all the data to calculate the metrics that Loyverse either doesn't provide at all, or only spreads out across several separate reports instead of in one consolidated place. Already set up are metrics like... -Total Revenue, Gross Profit, and Net Operating Profit Cash handling differences (over/under) Average spend per receipt (ATV) 30-day rolling Net Operating Profit (NOP) Performance vs. your historical weekday average Finally, it appends the single, calculated row of daily metrics to a Google Sheet and sends an easily customizable summary report to your email. How to set up This workflow includes detailed Sticky Notes to guide you through the setup process. Because every business has a unique POS configuration (different POS devices, categories, and payment types), you'll need to set up a few things manually before executing the workflow. I've tried to make this as easy as possible to follow, and the entire setup should only take about 15 minutes. Preparations & Credential setup Subscribe to "Integrations" Add-on in Loyverse ($9 / month) to gain API access. Create an Access token in Loyverse Create Credentials: In your n8n instance, create credentials for Loyverse (use "Generic" > "Bearer Auth"), Google Sheets (OAuth2), and your Email (SMTP or other). Make a copy of a prep-configured Google Spreadsheet (Link in the second sticky note inside the workflow). Fill MASTER CONFIG: Open the MASTER CONFIG node. Follow the comments inside to add your Google Sheet ID, Sheet Names, business hours, timezone, and Loyverse IDs (for POS devices, payment types, and categories). Configure Google Sheet Nodes Configure Read Historical Data: Open this node. Follow the instructions in the nearby Sticky Note to paste the expressions for your Document ID and Sheet Name. Configure Save Product List: Open this node. Paste in the expressions for Document ID and Sheet Name. The column mapper will load; map your sheet columns (e.g., item_name) to the data on the left (e.g., {{ $json.item_name }}). Configure Save Latest Sales Data: Open this node. Paste in the expressions for Document ID and Sheet Name. Save and run the workflow. After that, the column mapper will load. This is the most important step: map your sheet's column names (e.g., "Total Revenue") to the calculated metrics from the Calculate All Metrics node (e.g., {{ $json.totalGrossRevenue }}). Activate the workflow. 🫡 Requirements Loyverse Integrations Subscription Loyverse Access Token Credentials for Loyverse (Bearer Auth) Credentials for Google Sheets (OAuth2) Credentials for Email/SMTP sender How to customize the workflow This template is designed to be highly flexible. Central Configuration: Almost all customization (POS devices, categories, payment types, sheet names) is done in the MASTER CONFIG node. You don't need to dig through other nodes. Add/Remove Metrics: The Calculate All Metrics node has additional metrics already set up, just add the relevant collumns to the SalesData sheet or even add your own calculations to the node. Any new metric you add (e.g., metrics.myNewMetric = 123) will be available to map in the Save Latest Sales Data node. Email Body: You can easily edit the Send email node to change the text or add new metrics from the Calculate All Metrics node.

This is an advanced n8n workflow for transforming product concepts into 3D showcase videos with AI packaging design and auto-rotation rendering. Workflow Features: 🎯 Core Capabilities: AI Product Concept Generation - Uses Claude Sonnet 4 to analyze product prompts and generate comprehensive 3D specifications Automated Packaging Design - DALL-E 3 generates professional packaging visuals Texture Map Generation - Creates PBR-ready texture maps for realistic materials 3D Scene Script Generation - Produces complete Blender Python scripts with: Product geometry based on shape Professional 3-point lighting (key, fill, rim) 360° rotation animation (8 seconds) Camera setup and render settings Preview Rendering - Generates photorealistic 3D preview images Video Processing - Handles encoding and upload to video hosting services Database Storage - Saves all showcase data for tracking Status Monitoring - Checks render progress with automatic retry logic 📋 Required Setup: API Credentials needed: Anthropic API (for Claude AI) OpenAI API (for DALL-E image generation) Replicate API (optional for additional rendering) Video hosting service (Cloudflare Stream or similar) PostgreSQL database 🔧 How to Use: Import the JSON - Copy the artifact content and import directly into n8n Configure Credentials - Add your API keys in n8n credentials manager Activate Workflow - Enable the webhook trigger Send Request to webhook endpoint: POST /product-showcase { "productPrompt": "A premium organic energy drink in a sleek aluminum can with nature-inspired graphics" } 📤 Output Includes: Product specifications (dimensions, materials, colors) Packaging design image URL Texture map URLs Downloadable Blender script 3D preview render Video showcase URL Rendering metadata

📊 Google Ads + OpenAI + Sheets — Monthly AI Performance Analysis Automate monthly ad performance insights with AI-powered recommendations 🧩 Overview This workflow automatically analyzes Google Ads performance every month, using the Google Ads API and OpenAI (GPT-4o) to uncover which ad themes, categories, and messages perform best. It then generates a structured AI report, saves it to Google Sheets, and sends a Slack summary to your marketing team. 💡 Perfect for digital marketers, agencies, and growth analysts who want automated campaign insights without manually crunching numbers. ⚙️ Features ✅ Automatically runs on the 1st of each month ✅ Fetches last 30 days of ad performance via Google Ads API (GAQL) ✅ Uses GPT-4o for natural-language insights & improvement ideas ✅ Groups ads by category and theme (e.g., “Free Shipping,” “Premium”) ✅ Generates a clean, formatted markdown report ✅ Archives reports in Google Sheets for trend tracking ✅ Notifies your Slack channel with AI-driven recommendations 🧠 Architecture | Component | Purpose | | ------------------- | ------------------------------------------------ | | n8n | Workflow engine | | Google Ads API | Source of ad performance data | | OpenAI (GPT-4o) | Analyzes CTR patterns and writes recommendations | | Google Sheets | Report archiving and history tracking | | Slack | Team notifications | 🧭 Workflow Logic (Summary) Monthly Trigger (1st of Month) ⬇️ 1️⃣ Get Performance Data (Google Ads API) Fetches 30-day CTR, clicks, impressions for all responsive search ads. ⬇️ 2️⃣ Prepare Performance Data Groups data by ad group and theme keywords, builds an AI prompt. ⬇️ 3️⃣ AI Agent (LangChain) + GPT-4o Analyzes patterns and generates actionable insights. ⬇️ 4️⃣ Generate Report (Code) Formats a Markdown report with AI recommendations and KPIs. ⬇️ 5️⃣ Save to Google Sheets Archives results for long-term analytics. ⬇️ 6️⃣ Send Report to Slack Delivers the summary directly to your marketing channel. 🔑 Environment Variables | Variable | Example | Description | | ------------------------ | ----------------------------- | ------------------------------ | | GOOGLE_ADS_CUSTOMER_ID | 123-456-7890 | Google Ads customer account ID | | GOOGLE_ADS_API_VERSION | v17 | Current Ads API version | | GOOGLE_SHEET_ID | 1xA1B2c3D4EFgH... | Target spreadsheet ID | | OPENAI_API_KEY | sk-xxxxx | OpenAI API key for GPT-4o | | SLACK_WEBHOOK_URL | https://hooks.slack.com/... | Slack incoming webhook | 🔐 Credential Setup | Service | Type | Required Scopes | | ----------------- | ----------------------------- | ---------------------------------------------- | | Google Ads | OAuth2 (googleAdsOAuth2Api) | https://www.googleapis.com/auth/adwords | | OpenAI | API key (openAiApi) | Full access | | Google Sheets | OAuth2 | https://www.googleapis.com/auth/spreadsheets | | Slack | Webhook | chat:write | 🧱 Node-by-Node Breakdown | Node | Purpose | Key Configuration | | ---------------------------------- | ----------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------- | | Monthly Trigger | Starts workflow on 1st of every month | Cron: 0 0 1 * * | | Get Performance Data | Queries Ads data | Endpoint: https://googleads.googleapis.com/v17/customers/{id}/googleAds:searchQuery: GAQL (CTR, clicks, impressions, last 30 days) | | Prepare Performance Data | Aggregates and builds AI prompt | Groups by ad group and theme, computes CTRs | | AI Agent – Analyze Performance | Passes formatted data to GPT-4o | System message: “You are a Google Ads performance analyst…” | | OpenAI Chat Model (GPT-4o) | Analytical reasoning engine | Model: gpt-4o, Temperature 0.2 | | Generate Report | Parses AI output, formats Markdown report | Adds recommendations + next steps | | Save Report to Sheets | Archives report | Sheet name: Performance Reports | | Send Report (Slack) | Sends summary | Uses report_markdown variable | 🧠 AI Report Example 30-Day Performance Analysis Report Executive Summary Analyzed: 940 ads Period: Last 30 days Top Performing Categories Running Shoes: 9.4% CTR (120 ads) Fitness Apparel: 8.2% CTR (90 ads) Top Performing Themes "Free Shipping" messaging: 9.8% CTR (58 ads) "Premium" messaging: 8.5% CTR (44 ads) AI-Powered Recommendations [HIGH] Emphasize “Free Shipping” across more ad groups. Expected Impact: +5 % CTR [MEDIUM] Test “Premium Quality” vs. “New Arrivals.” Expected Impact: +3 % CTR Next Steps Implement new ad variations A/B test messaging Re-analyze next month 🧩 Testing Procedure 1️⃣ Temporarily disable the cron trigger. 2️⃣ Run the workflow manually. 3️⃣ Confirm: Google Ads node returns JSON with results. AI Agent output is valid JSON. Report is written to Sheets. Slack message received. 4️⃣ Re-enable the monthly trigger once verified. 🧾 Output in Google Sheets | Date | Ads Analyzed | Top Category | Top Theme | Key Recommendations | Generated At | | ---------- | ------------ | ------------- | ------------- | ---------------------------------- | ----------------- | | 2025-10-01 | 940 | Running Shoes | Free Shipping | “Add Free Shipping copy to 10 ads” | 2025-10-01T00:05Z | 🪜 Maintenance | Frequency | Task | | --------- | ----------------------------------------- | | Monthly | Review AI accuracy and update themes list | | Quarterly | Refresh Google Ads API credentials | | As needed | Update GAQL fields for new metrics | ⚙️ API Verification Endpoint: POST https://googleads.googleapis.com/v17/customers/{customer_id}/googleAds:search Scopes: https://www.googleapis.com/auth/adwords GAQL Query: SELECT ad_group_ad.ad.id, ad_group_ad.ad.responsive_search_ad.headlines, ad_group.name, metrics.impressions, metrics.clicks, metrics.ctr FROM ad_group_ad WHERE segments.date DURING LAST_30_DAYS AND metrics.impressions > 100 ORDER BY metrics.clicks DESC LIMIT 1000 ✅ Fully valid query — verified for GAQL syntax, fields, and resource joins. ✅ OAuth2 flow handled by n8n’s googleAdsOAuth2Api. ✅ Optional: add "timeout": 60000 for large accounts. 📈 Metrics of Success | KPI | Target | | -------------------------- | ---------------- | | Report accuracy | ≥ 95 % | | Monthly automation success | ≥ 99 % | | CTR improvement tracking | +3–5 % over time | 🔗 References Google Ads API Docs LangChain in n8n OpenAI API Reference Google Sheets API Slack Incoming Webhooks 🎯 Conclusion You now have a fully automated Google Ads performance analysis workflow powered by: Google Ads API** for granular metrics OpenAI GPT-4o** for intelligent recommendations Google Sheets** for archiving Slack** for team-wide updates 💡 Result: A recurring, data-driven optimization loop that improves ad performance every month — with zero manual effort.

Description Activate this workflow once and every Monday morning it automatically scans all your client meetings from the past week. It pulls Fireflies sentiment scores per meeting, then sends each transcript to GPT-4o-mini which explains why the sentiment was positive or negative, scores the churn risk, extracts a key quote, and recommends one concrete action. Every meeting is logged to Google Sheets and any negative or high-risk meeting triggers an immediate Slack alert to your dedicated urgent channel. Built for account managers, customer success teams, and agency leads who need early warning on at-risk clients before small problems become lost accounts. What This Workflow Does Pulls last 7 days of client meetings automatically** — Fetches up to 50 recent Fireflies transcripts and filters to only those recorded this week Extracts Fireflies NLP sentiment scores** — Uses the built-in positive, neutral, and negative percentages already computed by Fireflies per meeting Computes a composite sentiment score** — Converts raw percentages into a single 0–100 score and assigns a label: Positive, Mostly Positive, Neutral, Mostly Negative, or Negative Adds AI context with GPT-4o-mini** — Explains WHY sentiment was what it was, scores churn risk from Low to Critical, and identifies the most important quote from the meeting Logs every meeting to Google Sheets** — Appends a 15-column row per meeting with emoji labels, scores, analysis, and a direct Fireflies link Fires instant Slack alerts for at-risk meetings** — Any meeting with Negative sentiment or High/Critical churn risk triggers an immediate alert to your #urgent-alerts channel Exits cleanly with no meetings** — If no meetings were recorded this week, the workflow stops silently without errors Setup Requirements Tools Needed n8n instance (self-hosted or cloud) Fireflies.ai account with API access OpenAI account with GPT-4o-mini API access Google Sheets (one sheet with a tab named Client Sentiment Log) Slack workspace with OAuth2 app configured and two channels ready Credentials Required Fireflies API key (pasted into 2. Set — Config Values) OpenAI API key Google Sheets OAuth2 Slack OAuth2 Estimated Setup Time: 15–20 minutes Step-by-Step Setup Import the workflow — Open n8n → Workflows → Import from JSON → paste the workflow JSON → click Import Get your Fireflies API key — Log in to fireflies.ai → Settings → Integrations → copy your API key Fill in Config Values — Open node 2. Set — Config Values → replace all placeholders: | Field | What to enter | |---|---| | YOUR_FIREFLIES_API_KEY | Your Fireflies API key from step 2 | | YOUR_GOOGLE_SHEET_ID | The ID from your Google Sheet URL (the string between /d/ and /edit) | | Client Sentiment Log | Leave as-is, or match your sheet tab name exactly | | #client-health | Your Slack channel for general weekly health monitoring | | #urgent-alerts | Your Slack channel for immediate at-risk client alerts | | YOUR COMPANY NAME | Your company name (used in the AI prompt) | Connect OpenAI — Open node 10. OpenAI — GPT-4o-mini Model → click the credential dropdown → add your OpenAI API key → test the connection Create your Google Sheet — Open Google Sheets → add a tab named exactly Client Sentiment Log → add these 15 column headers in row 1: Week, Meeting Date, Meeting Title, Client Name, Duration, Participants, Sentiment Score, Sentiment Label, Positive %, Negative %, GPT Analysis, Churn Risk, Recommended Action, Fireflies URL, Logged At Connect Google Sheets — Open node 13. Google Sheets — Log Client Sentiment → click the credential dropdown → add Google Sheets OAuth2 → sign in with your Google account → authorize access Connect Slack — Open node 15. Slack — Send Urgent Alert → click the credential dropdown → connect your Slack workspace via OAuth2 → invite the n8n bot to both channels in Slack (/invite @n8n in each) Activate the workflow — Toggle the workflow to Active — it will run automatically every Monday at 9AM > ⚠️ To test immediately — Click on node 1. Schedule — Every Monday 9AM and use the manual Execute option to trigger a test run without waiting for Monday. How It Works (Step by Step) Step 1 — Schedule: Every Monday 9AM This step fires the workflow automatically every Monday morning at 9AM. No manual trigger needed. Once the workflow is active, it runs on its own every week. Step 2 — Set: Config Values Your Fireflies API key, Google Sheet ID, sheet tab name, both Slack channel names, company name, and the 7-day date window are stored here. Week start and end dates are calculated automatically from today's date so you never need to update them. Step 3 — HTTP: Fetch Last 50 Transcripts A request is sent to the Fireflies API using your API key. It retrieves the 50 most recent transcripts from your account — title, date, duration, participants, and transcript URL for each. This is a lightweight list fetch; full content comes in a later step per meeting. Step 4 — Code: Filter Last 7 Days The 50 transcripts are filtered to only those recorded in the past 7 days. Each one that passes becomes its own separate entry so subsequent steps process each meeting individually. If no transcripts exist at all, or none fall within the window, a fallback result is passed forward. Step 5 — IF: Meetings Found? This is the gate check. If meetings were found (YES path), each one moves forward to full analysis. If no meetings were found this week (NO path), the workflow routes to 6. Set — No Meetings This Week and stops cleanly. Step 6 — Set: No Meetings This Week This step handles the empty-week case. It sets a brief message confirming nothing was recorded and the workflow ends here for that run. Step 7 — HTTP: Fetch Full Transcript For each meeting that passed the filter, a second Fireflies API request retrieves the complete transcript: all sentences with speaker labels, the meeting summary, keywords, action items, and the Fireflies NLP sentiment analytics (positive, neutral, and negative percentages). Step 8 — Code: Extract Sentiment Data The full transcript is processed here. Sentences are assembled into readable text limited to 4,000 characters for GPT efficiency. The three Fireflies sentiment percentages are combined into a single composite score (0–100) using a weighted formula. A sentiment label is assigned: Positive (60%+ positive), Negative (35%+ negative), Mostly Positive, Mostly Negative, or Neutral. The client name is extracted automatically from the meeting title using common patterns like "Call with [Name]" or "Client | [Name]". Meeting overview, keywords, and action items are also pulled for the AI step. Step 9 — AI Agent: Sentiment Analysis GPT-4o-mini receives the full meeting context including the Fireflies sentiment scores, overview, keywords, action items, and transcript excerpt. It returns exactly four fields: a 2–3 sentence explanation of why the sentiment was what it was (referencing actual meeting content), a churn risk score (Low, Medium, High, or Critical), one concrete action the account manager should take within 48 hours, and the single most important quote from the meeting. Step 10 — OpenAI: GPT-4o-mini Model This is the language model powering the analysis step. It runs at temperature 0.3 for consistent, grounded responses and is capped at 500 tokens to keep each analysis concise and costs predictable. Step 11 — Parser: Structured Analysis Output This step enforces the exact four-field schema GPT-4o-mini must return. It validates that all required fields are present and correctly typed before results move forward, preventing malformed AI output from reaching your sheet. Step 12 — Code: Combine Analysis Results The AI output is merged with all meeting metadata. Emoji flags are added automatically based on sentiment label (🟢 Positive, 🟡 Mostly Positive, ⚪ Neutral, 🟠 Mostly Negative, 🔴 Negative) and churn risk (✅ Low, ⚠️ Medium, 🚨 High, 🆘 Critical). A needsAlert flag is set to true if the sentiment is Negative or Mostly Negative, or if churn risk is High or Critical. Step 13 — Google Sheets: Log Client Sentiment A new row is appended to your Client Sentiment Log tab for every meeting. All 15 columns are populated including the week date range, meeting details, sentiment score and label with emojis, positive and negative percentages, GPT analysis, churn risk with emoji, recommended action, and a direct Fireflies transcript link. Step 14 — IF: Needs Urgent Alert? This check reads the needsAlert flag set in step 12. If the meeting is negative or high-risk (YES path), an immediate Slack alert fires to your #urgent-alerts channel. If the meeting is healthy (NO path), the workflow routes to 16. Set — No Alert Needed and ends silently for that meeting. Step 15 — Slack: Send Urgent Alert A formatted Slack alert is posted to your #urgent-alerts channel. It includes the client name, meeting title, date, duration, sentiment label with emoji, churn risk level, the GPT explanation of what happened, the key quote in italics, the recommended action, and a direct link to the Fireflies transcript. Step 16 — Set: No Alert Needed This step handles healthy meetings. It logs a brief confirmation that sentiment and churn risk are within acceptable levels. No Slack message is sent for these meetings. Key Features ✅ Fully automated weekly cadence — Runs every Monday at 9AM with zero input after setup — your team starts the week already knowing which clients need attention ✅ Dual-layer sentiment analysis — Combines Fireflies built-in NLP scores with GPT-4o-mini contextual interpretation for deeper insight than numbers alone ✅ Automatic client name extraction — Pulls the client name from the meeting title using common naming patterns so your sheet is organized by client, not by meeting ID ✅ Emoji-coded at a glance — Every row in Google Sheets and every Slack alert uses emoji flags for instant visual scanning — no need to read every cell ✅ Churn risk scoring per meeting — Every meeting gets a Low / Medium / High / Critical rating so you can prioritize which clients need calls this week ✅ Key quote extracted per meeting — The single most revealing sentence from the meeting is surfaced in every alert so the account manager has context instantly ✅ Alert only when needed — Healthy meetings log silently. Only negative or high-risk meetings fire a Slack alert, keeping your #urgent-alerts channel signal-only ✅ Clean empty-week exit — No meetings this week means a clean stop — no errors, no blank rows, no empty Slack messages Customisation Options Change the alert trigger threshold — In node 12. Code — Combine Analysis Results, the needsAlert flag currently fires for Negative and Mostly Negative sentiment. Remove Mostly Negative from the check if you only want alerts for clearly Negative meetings to reduce noise. Extend the date window to 14 days — In node 2. Set — Config Values, change {days: 7} in both the weekStart and sevenDaysAgoMs fields to {days: 14} to analyze a bi-weekly window instead of the past 7 days. Add a weekly digest to the general Slack channel — After node 13. Google Sheets — Log Client Sentiment, add a second Code step that aggregates all meetings into a weekly summary (total meetings, positive count, at-risk count) and post it to #client-health as a Monday morning digest alongside the per-meeting urgent alerts. Filter to specific client meetings only — In node 4. Code — Filter Last 7 Days, add a keyword filter after the date filter: only include transcripts whose title contains words like "client", "customer", or specific client names — excluding internal team meetings from the sentiment log. Send a follow-up email for Critical risk meetings — After node 15. Slack — Send Urgent Alert, add an IF check for churnRisk === 'Critical' and connect a Gmail node to automatically email the account manager with the full analysis, recommended action, and Fireflies link for the most severe cases. Troubleshooting Workflow not triggering on Monday: Confirm the workflow is toggled to Active — it will not run on a schedule if inactive Check that the cron expression in node 1. Schedule — Every Monday 9AM is 0 9 * * 1 exactly — any accidental edit breaks the schedule To test immediately, click on node 1 and use the manual Execute option Fireflies returning no transcripts or auth error: Confirm YOUR_FIREFLIES_API_KEY in node 2. Set — Config Values is replaced with your actual key — not the placeholder text Get your key from fireflies.ai → Settings → Integrations → API Key Check that your Fireflies plan includes API access — some plans restrict this feature Google Sheets not logging rows: Confirm the Google Sheets OAuth2 credential in node 13. Google Sheets — Log Client Sentiment is connected and not expired — re-authorize if needed Check that YOUR_GOOGLE_SHEET_ID in node 2. Set — Config Values matches the ID in your sheet URL exactly Confirm the tab is named Client Sentiment Log exactly — capitalization must match sheetName in Config Values Verify all 15 column headers in row 1 of your sheet match exactly the column names listed in setup step 5 Slack alerts not arriving: Confirm the Slack OAuth2 credential in node 15. Slack — Send Urgent Alert is connected and authorized Check that #urgent-alerts in node 2. Set — Config Values includes the # prefix and matches your channel name exactly Type /invite @n8n in your #urgent-alerts channel to confirm the bot has posting permission Check the needsAlert flag in the execution log of node 12. Code — Combine Analysis Results — if it is false, the meeting was classified as healthy and no alert fires AI returning incomplete output or wrong churn risk values: The schema parser in node 11. Parser — Structured Analysis Output validates all four fields — check the execution log of node 9. AI Agent — Sentiment Analysis for the raw GPT response if fields are missing Confirm the OpenAI credential in node 10. OpenAI — GPT-4o-mini Model is connected and your account has available credits If churnRisk returns a value other than Low, Medium, High, or Critical, the prompt in node 9 explicitly restricts to those four options — re-check the prompt text is intact Support Need help setting this up or want a custom version built for your team or agency? 📧 Email:info@isawow.com 🌐 Website:https://isawow.com

This n8n template demonstrates how to fully automate the creation of UGC-style product videos using AI, starting from a simple Google Sheet. It transforms product data into AI-generated images, cinematic video scripts, and final videos, then uploads everything to Google Drive and updates your sheet automatically. 💡 Use cases Generate UGC ads at scale for e-commerce products Create TikTok / Reels content automatically Build content pipelines for agencies or creators Rapidly test different product angles, audiences, and messaging Automate creative production from structured data (Google Sheets) Good to know This workflow uses multiple AI services (image + video), so cost depends on usage: Image generation (DALL·E) Video generation (Sora) Video generation is asynchronous and may take several minutes per item Some AI models (like Sora) may be region-restricted or limited access Generated image URLs may expire, so storing them (as done here) is important How it works Reads product data from Google Sheets and selects rows marked "Pending". Creates a prompt and generates a product image. Analyzes the image and turns it into a video script. Sends the script to Sora and waits until the video is ready. Uploads the video to Google Drive and updates the sheet. Logs errors and marks the row as "Error". How to use Add products to Google Sheets with name, description, audience, and set status to "Pending". Run the workflow or let the schedule trigger process items automatically. The system generates image → script → video, uploads them, and updates your sheet. Requirements Requires OpenAI, Google Sheets, and Google Drive accounts. Requires an n8n instance with credentials configured. Customizing this workflow Replace the schedule trigger with a webhook or form for real-time use. Generate multiple videos per product. Send outputs to platforms like TikTok, Meta Ads, or CMS tools. Add voiceovers, captions, or permanent asset storage.

Automated Meeting Minutes from Video Recordings This workflow automatically transforms video recordings of meetings into structured, professional meeting minutes in Notion. It uses local AI models (Whisper for transcription and Ollama for summarization) to ensure privacy and cost efficiency, while uploading the original video to Google Drive for safekeeping. Ideal for creative teams, production reviews, or any scenario where visual context is as important as the spoken word. 🔄 How It Works Wait & Detect: The workflow monitors a local folder. When a new .mkv video file is added, it waits until the file has finished copying. Prepare Audio: The video is converted into a .wav audio file optimized for transcription (under 25 MB with high clarity). Transcribe Locally: The local Whisper model generates a timestamped text transcript. Generate Smart Minutes: The transcript is sent to a local Ollama LLM, which produces structured, summarized meeting notes. Store & Share: The original video is uploaded to Google Drive, a new page is created in Notion with the notes and a link to the video, and a completion notification is sent via Discord. ⏱️ Setup Steps Estimated Time**: 10–15 minutes (for technically experienced users). Prerequisites**: Install Python, FFmpeg, and required packages (openai-whisper, ffmpeg-python). Run Ollama locally with a compatible model (e.g., gpt-oss:20b, llama3, mistral). Configure n8n credentials for Google Drive, Notion, and Discord. Workflow Configuration**: Update the file paths for the helper scripts (wait-for-file.ps1, create_wav.py, transcribe_return.py) in the respective "Execute Command" nodes. Change the input folder path (G:\OBS\videos) in the "File" node to your own recording directory. Replace the Google Drive folder ID and Notion database/page ID in their respective nodes. > 💡 Note: Detailed instructions for each step, including error handling and variable setup, are documented in the Sticky Notes within the workflow itself. 📁 Helper Scripts Documentation wait-for-file.ps1 A PowerShell script that checks if a file is still being written to (i.e., locked by another process). It returns 0 if the file is free and 1 if it is still locked. Usage: .\wait-for-file.ps1 -FilePath "C:\path\to\your\file.mkv" create_wav.py A Python script that converts a video file into a .wav audio file. It automatically calculates the necessary audio bitrate to keep the output file under 25 MB—a common requirement for many transcription services. Usage: python create_wav.py "C:\path\to\your\file.mkv" transcribe_return.py A Python script that uses a local Whisper model to transcribe an audio file. It can auto-detect the language or use a language code specified in the filename (e.g., meeting.en.mkv for English, meeting.es.mkv for Spanish). The transcript is printed directly to stdout with timestamps, which is then captured by the n8n workflow. Usage: Auto-detect language python transcribe_return.py "C:\path\to\your\file.mkv" Force language via filename python transcribe_return.py "C:\path\to\your\file.es.mkv" `

Upload a construction photo via web form → get a detailed cost estimate with work breakdown, resource costs, and professional HTML report. Powered by GPT-4 Vision and the open-source DDC CWICR database (55,000+ work items). Who's it for Site managers** who need quick estimates from mobile photos Renovation contractors** evaluating project scope from initial site visit Real estate inspectors** estimating repair costs Construction consultants** providing rapid ballpark figures DIY enthusiasts** planning home improvement budgets What it does Collects photo + region/language via n8n Form Analyzes photo with GPT-4 Vision (room type, elements, dimensions) Decomposes visible elements into construction work items Searches DDC CWICR vector database for matching rates Generates professional HTML report with cost breakdown Supports 9 regions: 🇩🇪 Berlin · 🇬🇧 Toronto · 🇷🇺 St. Petersburg · 🇪🇸 Barcelona · 🇫🇷 Paris · 🇧🇷 São Paulo · 🇨🇳 Shanghai · 🇦🇪 Dubai · 🇮🇳 Mumbai How it works ┌──────────────┐ ┌───────────────┐ ┌───────────────┐ ┌──────────────┐ │ Web Form │ → │ STAGE 1 │ → │ STAGE 4 │ → │ Loop Works │ │ Photo+Lang │ │ GPT-4 Vision │ │ Decompose │ │ per item │ └──────────────┘ └───────────────┘ └───────────────┘ └──────────────┘ ↓ ↓ ↓ ┌─────────────────────────────────────────────────────┐ │ Identify room, elements, fixtures, dimensions │ │ → Break down into 15-40 construction work items │ └─────────────────────────────────────────────────────┘ ↓ ┌──────────────┐ ┌───────────────┐ ┌───────────────┐ ┌──────────────┐ │ HTML Report │ ← │ STAGE 7.5 │ ← │ STAGE 5 │ ← │ Qdrant │ │ Response │ │ Aggregate │ │ Parse+Score │ │ Vector DB │ └──────────────┘ └───────────────┘ └───────────────┘ └──────────────┘ Pipeline stages: | Stage | Node | Description | |-------|------|-------------| | 1 | GPT-4 Vision | Analyzes photo: room type, elements, materials, dimensions | | 4 | GPT-4 Decompose | Breaks elements into work items with quantities | | 5 | Vector Search + Score | Finds matching rates in DDC CWICR, quality scoring | | 7.5 | Aggregate & Validate | Sums costs, groups by phase, validates results | | 9 | HTML Report | Generates professional estimate document | Prerequisites | Component | Requirement | |-----------|-------------| | n8n | v1.30+ with Form Trigger support | | OpenAI API | GPT-4 Vision + Embeddings access | | Qdrant | Vector DB with DDC CWICR collections | | DDC CWICR Data | github.com/datadrivenconstruction/DDC-CWICR | Setup 1. n8n Credentials (Settings → Credentials) OpenAI API** — required (GPT-4 Vision + text-embedding-3-large) Qdrant API** — your Qdrant instance connection 2. Qdrant Collections Load DDC CWICR embeddings for your target regions: DE_BERLIN_workitems_costs_resources_EMBEDDINGS_3072_DDC_CWICR ENG_TORONTO_workitems_costs_resources_EMBEDDINGS_3072_DDC_CWICR RU_STPETERSBURG_workitems_costs_resources_EMBEDDINGS_3072_DDC_CWICR ES_BARCELONA_workitems_costs_resources_EMBEDDINGS_3072_DDC_CWICR FR_PARIS_workitems_costs_resources_EMBEDDINGS_3072_DDC_CWICR PT_SAOPAULO_workitems_costs_resources_EMBEDDINGS_3072_DDC_CWICR ZH_SHANGHAI_workitems_costs_resources_EMBEDDINGS_3072_DDC_CWICR AR_DUBAI_workitems_costs_resources_EMBEDDINGS_3072_DDC_CWICR HI_MUMBAI_workitems_costs_resources_EMBEDDINGS_3072_DDC_CWICR 3. Activate Workflow Import JSON into n8n Link OpenAI + Qdrant credentials to respective nodes Activate workflow Access form at: https://your-n8n/form/photo-estimate-pro-v3 Features | Feature | Description | |---------|-------------| | 📸 Photo Analysis | GPT-4 Vision identifies room type, elements, fixtures | | 📏 Dimension Estimation | Uses reference objects (doors, tiles) for sizing | | 🔧 Work Decomposition | Breaks down to 15-40 specific work items | | 🎯 Quality Scoring | Rates match quality (high/medium/low/not_found) | | 📊 Phase Grouping | PREPARATION → MAIN → FINISHING → MEP | | 💰 Cost Breakdown | Labor, materials, machines per item | | ✅ Validation | Warns if <50% rates found or missing demolition | | 🌍 9 Languages | Full localization + regional pricing | Form Fields | Field | Type | Options | |-------|------|---------| | 📷 Upload Photo | File | .jpg, .png, .webp | | 🌍 Region & Language | Dropdown | 9 regions with currencies | | 🏗️ Work Type | Dropdown | New / Renovation / Repair / Auto | | 📝 Description | Textarea | Optional context | Example Output Input: Bathroom photo (renovation) Region: 🇩🇪 German - Berlin (EUR €) Generated Work Items: PREPARATION (3 items) ├── Demolition of wall tiles — 12 m² — €180 ├── Demolition of floor tiles — 4.5 m² — €95 └── Disposal of construction waste — 0.8 m³ — €120 MAIN (8 items) ├── Floor waterproofing — 4.5 m² — €225 ├── Wall waterproofing wet zone — 8 m² — €280 ├── Floor screed — 4.5 m² — €135 ├── Wall tiling — 22 m² — €880 ├── Floor tiling — 4.5 m² — €225 ├── Toilet installation — 1 pcs — €320 ├── Sink installation — 1 pcs — €185 └── Shower cabin installation — 1 pcs — €450 FINISHING (3 items) ├── Ceiling painting — 4.5 m² — €68 ├── Grouting — 26.5 m² — €133 └── Silicone sealing — 8 m — €48 MEP (4 items) ├── Socket installation — 2 pcs — €90 ├── Light point installation — 2 pcs — €120 ├── Mixer/faucet installation — 2 pcs — €160 └── Ventilation installation — 1 pcs — €85 ───────────────────────────────────── TOTAL: €3,799.00 Labor: €1,520 · Materials: €1,900 · Machines: €379 Quality: 78% high match · 18 work items Quality Scoring System | Score | Level | Meaning | |-------|-------|---------| | 60-100 | 🟢 High | Exact match with resources | | 40-59 | 🟡 Medium | Good match, minor differences | | 20-39 | 🟠 Low | Partial match, review needed | | 0-19 | 🔴 Not Found | No suitable rate found | Scoring factors: Has price in database (+30) Has resources breakdown (+25) Unit matches expected (+20) Material keywords match (+15) Work type keywords match (+10) Vector similarity >0.5 (+10) Notes & Tips Best photo angles:** Capture full room, include reference objects (doors, sockets) Renovation mode:** AI automatically adds demolition works Validation warnings:** Check if <50% rates found — may need manual additions Rate accuracy:** Depends on DDC CWICR coverage for your region Extend:** Chain with PDF generation, email delivery, or CRM integration Categories AI · Data Extraction · Document Ops · Files & Storage Tags photo-analysis, gpt-4-vision, construction, cost-estimation, qdrant, vector-search, form-trigger, html-report, multilingual Author DataDrivenConstruction.io https://DataDrivenConstruction.io info@datadrivenconstruction.io Consulting & Training We help construction, engineering, and technology firms implement: AI-powered visual estimation systems CAD/BIM data processing pipelines Vector database integration for construction data Multilingual cost database solutions Contact us to test with your data or adapt to your project requirements. Resources DDC CWICR Database:** GitHub Qdrant Documentation:** qdrant.tech/documentation n8n Form Trigger:** docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.formtrigger ⭐ Star us on GitHub! github.com/datadrivenconstruction/DDC-CWICR

This n8n template shows you how to automate document summarization while keeping full digital sovereignty. By combining Nextcloud for file storage and the IONOS AI Model Hub, your sensitive documents stay on European infrastructure—fully outside US CLOUD Act jurisdiction. Use cases Daily briefings: Automatically summarize lengthy reports as soon as your team uploads them. Meeting notes: Turn uploaded transcripts into clear, actionable bullet points—hands-free. Research management: Get instant summaries of academic papers or PDFs the moment they land in your research folder. How it works A Schedule Trigger kicks off the workflow at regular intervals. It calls the Nextcloud List a Folder node to retrieve all files in your chosen directory. A Code node then acts as a filter, checking each file's last-modified timestamp and letting through only files changed within the past 24 hours. Filtered files are pulled into the workflow via the Nextcloud Download a File node. From there, the Summarization Chain takes over: a Default Data Loader and Token Splitter prepare the content, and the IONOS AI Model Hub Chat Model generates a concise, structured summary. The final output is saved back to your Nextcloud instance as a note for your team. Good to know Nextcloud setup: Make sure your Nextcloud credentials include both read and write permissions—one to download source files, one to upload the generated summary. Model selection: The IONOS AI Model Hub currently offers several LLMs, including Mistral Small 24B and Nemo. For document summarization, Mistral Small 24B strikes the best balance between output quality and speed. How to set it up Set your folder path in the List a Folder node to the directory you want to monitor. In the New Files Only code node, adjust the 24 value to change how far back the workflow looks. Open the Summarization Chain and define your output format—for example: "Summarize this document in 3 bullet points." Requirements Nextcloud account for file hosting and retrieval IONOS Cloud account to access the AI Model Hub

Zoho CRM → AI Sentiment Analysis for customer interactions & Automatic Alerts Workflow This workflow analyzes newly created Notes (in Any module) in Zoho CRM, detects customer sentiment using an AI model, updates the related CRM record with custom fields - sentiment label and score, and sends an instant alert whenever negative sentiment is detected. It runs on a scheduled interval and gives teams real-time visibility into customer emotions and potential risks. Quick Implementation Steps Connect Zoho CRM OAuth2 credentials Add custom fields in Zoho CRM: Sentiment_Label and Sentiment_Score Add AI provider credentials Set Gmail alert recipient Activate workflow and test by adding a Note What It Does This workflow automatically monitors Zoho CRM Notes. When a new Note is detected, the text is extracted and analyzed through an AI-powered sentiment model. The AI classifies the text as Positive, Neutral or Negative and produces a numeric sentiment score. The workflow updates the related CRM module with these values. If the sentiment is negative, a Gmail alert is triggered so your team can follow up quickly. This automation helps organizations maintain high customer satisfaction and detect potential issues early. Who’s It For Support teams Sales teams CRM administrators Customer success managers Businesses needing automated customer sentiment tracking Requirements n8n instance Zoho CRM OAuth2 credentials Gmail OAuth2 credentials AI provider key Custom fields in Zoho CRM: Sentiment_Label & Sentiment_Score (if you are using different field name then do changes in workflow accoredingly) How It Works & Setup Step 1: Schedule Trigger Runs periodically to check for new or updated Notes. Step 2: Fetch Latest Note Retrieves the most recently modified Note. Step 3: Extract Details Extracts Note text, note_id, parent_id and module name. Step 4: AI Sentiment Analysis Sends text to the AI (via LangChain chain) for sentiment classification. Step 5: Conditional Branching If Negative: Send Gmail alert and update CRM Otherwise: Just update CRM Step 6: Update CRM Writes sentiment data back into the related parent record. How to Customize Nodes Adjust sentiment output by modifying the AI prompt. Change field mappings in Zoho update nodes. Customize the Gmail alert message. Adjust Schedule Trigger frequency. Add additional metadata (e.g., emotion tags). Add‑Ons Slack/Teams alerts for negative sentiment. Historical sentiment logging. Weekly sentiment reports. Auto-task creation for negative interactions. Priority-based escalation logic. Use Case Examples Detect unhappy customers in support interactions. Monitor sentiment across sales conversations. Escalate negative feedback automatically. Quality assurance tracking for customer interactions. Early detection of churn indicators. Troubleshooting Guide | Issue | Possible Cause | Solution | |------|----------------|----------| | Sentiment not updating | Missing Zoho fields | Add custom fields in CRM | | Note not detected | Fetching only latest note | Increase frequency or widen fetch scope | | AI output invalid | Prompt mismatch | Update prompt and parser | | Alerts not sending | Gmail OAuth expired | Reconnect Gmail | | Incorrect sentiment | Weak prompt instructions | Refine prompt wording | Need Help? WeblineIndia can help you configure, customize and extend workflows like this. We specialize in: n8n automation CRM integrations AI/LLM-powered workflows Zoho CRM customization Reach out if you'd like assistance building or enhancing similar n8n automation solutions.

How It Works This workflow automates complex data engineering operations by orchestrating multiple specialized AI agents to analyze datasets, calculate risk metrics, and route findings based on severity levels. Designed for data engineers, analytics teams, and business intelligence managers, it solves the challenge of processing diverse datasets through appropriate analytical frameworks while ensuring critical insights reach stakeholders immediately. The system receives data processing requests via webhook, deploys an orchestration agent that determines which specialized analysis agents to invoke (Anthropic Chat Model for general analysis, Risk Analysis Verification Agent, and Test Validation Agent), calculates risk scores, fetches relevant historical context, then routes results by severity. High-severity findings trigger immediate HTTP notifications to stakeholders, while all results are aggregated into comprehensive reports, formatted for clarity, and logged with appropriate priority markers before webhook response. Setup Steps Configure webhook trigger endpoint for data processing system integration Set up Anthropic API credentials for Orchestrating Orchestration Agent node Configure specialized agent tools Update Calculate Risk Score node with your risk scoring methodology Set up Fetch Historical Data node with data warehouse API credentials Configure severity threshold in Route by Severity node for alert triggering Connect HTTP Request nodes with stakeholder notification endpoints Prerequisites Active Anthropic and OpenAI API accounts, data processing system with webhook capability Use Cases ETL pipeline quality monitoring, data anomaly detection, dataset validation before production deployment Customization Modify orchestration agent logic for custom analysis pathways Benefits Accelerates data quality assessment by 70%, enables proactive issue detection before production impact