Workflow Templates

Monthly Spotify Track Archiving and Playlist Classification This n8n workflow allows you to automatically archive your monthly Spotify liked tracks in a Google Sheet, along with playlist details and descriptions. Based on this data, Claude 3.5 is used to classify each track into multiple playlists and add them in bulk. Who is this template for? This workflow template is perfect for Spotify users who want to systematically archive their listening history and organize their tracks into custom playlists. What problem does this workflow solve? It automates the monthly process of tracking, storing, and categorizing Spotify tracks into relevant playlists, helping users maintain well-organized music collections and keep a historical record of their listening habits. Workflow Overview Trigger Options**: Can be initiated manually or on a set schedule. Spotify Playlists Retrieval**: Fetches the current playlists and filters them by owner. Track Details Collection**: Retrieves information such as track ID and popularity from the user’s library. Audio Features Fetching**: Uses Spotify's API to get audio features for each track. Data Merging**: Combines track information with their audio features. Duplicate Checking**: Filters out tracks that have already been logged in Google Sheets. Data Logging**: Archives new tracks into a Google Sheet. AI Classification**: Uses an AI model to classify tracks into suitable playlists. Playlist Updates**: Adds classified tracks to the corresponding playlists. Setup Instructions Credentials Setup: Make sure you have valid Spotify OAuth2 and Google Sheets access credentials. Trigger Configuration: Choose between manual or scheduled triggers to start the workflow. Google Sheets Preparation: Set up a Google Sheet with the necessary structure for logging track details. Spotify Playlists Setup: Have a diverse range of playlists and exhaustive description (see example) ready to accommodate different music genres and moods. Customization Options Adjust Playlist Conditions**: Modify the AI model’s classification criteria to align with your personal music preferences. Enhance Track Analysis**: Incorporate additional audio features or external data sources for more refined track categorization. Personalize Data Logging**: Customize which track attributes to log in Google Sheets based on your archival preferences. Configure Scheduling**: Set a preferred schedule for periodic track archiving, e.g., monthly or weekly. Cost Estimate For 300 tracks, the token usage amounts to approximately 60,000 tokens (58,000 for input and 2,000 for completion), costing around 20 cents with Claude 3.5 Sonnet (as of October 2024). Playlists' Description Examples | Playlist Name | Playlist Description | |-------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Classique | Indulge in the timeless beauty of classical music with this refined playlist. From baroque to romantic periods, this collection showcases renowned compositions. | | Poi | Find your flow with this dynamic playlist tailored for poi, staff, and ball juggling. Featuring rhythmic tracks that complement your movements. | | Pro Sound | Boost your productivity and focus with this carefully selected mix of concentration-enhancing music. Ideal for work or study sessions. | | ChillySleep | Drift off to dreamland with this soothing playlist of sleep-inducing tracks. Gentle melodies and ambient sounds create a peaceful atmosphere for restful sleep. | | To Sing | Warm up your vocal cords and sing your heart out with karaoke-friendly tracks. Featuring popular songs, perfect for solo performances or group sing-alongs. | | 1990s | Relive the diverse musical landscape of the 90s with this eclectic mix. From grunge to pop, hip-hop to electronic, this playlist showcases defining genres. | | 1980s | Take a nostalgic trip back to the era of big hair and neon with this 80s playlist. Packed with iconic hits and forgotten gems, capturing the energy of the decade.| | Groove Up | Elevate your mood and energy with this upbeat playlist. Featuring a mix of feel-good tracks across various genres to lift your spirits and get you moving. | | Reggae & Dub | Relax and unwind with the laid-back vibes of reggae and dub. This playlist combines classic reggae tunes with deep, spacious dub tracks for a chilled-out vibe. | | Psytrance | Embark on a mind-bending journey with this collection of psychedelic trance tracks. Ideal for late-night dance sessions or intense focus. | | Cumbia | Sway to the infectious rhythms of Cumbia with this lively playlist. Blending traditional Latin American sounds with modern interpretations for a danceable mix. | | Funky Groove | Get your body moving with this collection of funk and disco tracks. Featuring irresistible basslines and catchy rhythms, perfect for dance parties. | | French Chanson | Experience the romance and charm of France with this mix of classic and modern French songs, capturing the essence of French musical culture. | | Workout Motivation | Push your limits and power through your exercise routine with this high-energy playlist. From warm-up to cool-down, these tracks will keep you motivated. | | Cinematic Instrumentals | Immerse yourself in a world of atmospheric sounds with this collection of cinematic instrumental tracks, perfect for focus, relaxation, or contemplation. |

How it works This workflow gets the search console results data and exports this to google sheets. This makes it easier to visualize and do other SEO related tasks and activities without having to log into Search Console Setup and use Set your desired schedule Enter your desired domain Connect to your Google sheets or make a copy of this sheet. Detailed Setup Inputs and Outputs:** Input: API response from Google Search Console regarding keywords, page data, and date data. Output: Entries written to Google Sheets containing keyword data, clicks, impressions, CTR, and positions. Setup Instructions: Prerequisites:** An n8n instance set up and running. Active Google Account with access to Google Search Console and Google Sheets. Google OAuth 2.0 credentials for API access. Step-by-Step Setup:** Open n8n and create a new workflow. Add the nodes as described in the JSON. Configure the Google OAuth2 credentials in n8n to enable API access. Set your domain in the Set your domain node. Customize the Google Sheets document URLs to your personal sheets. Adjust the schedule in the Schedule Trigger node as per your requirements. Save the workflow. Configuration Options:** You can customize the date ranges in the body of the HttpRequest nodes. Adjust any fields in the Edit Fields nodes based on different data requirements. Use Case Examples: Useful in tracking website performance over time using Search Console metrics. Ideal for digital marketers, SEO specialists, and web analytics professionals. Offers value in compiling performance reports for stakeholders or team reviews. Running and Troubleshooting: Running the Workflow:** Trigger the workflow manually or wait for the schedule to run it automatically. Monitoring Execution:** Check the execution logs in n8n's dashboard to ensure all nodes complete successfully. Common Issues:** Invalid OAuth credentials – ensure credentials are set up correctly. Incorrect Google Sheets URLs – double-check document links and permissions. Scheduling conflicts – make sure the schedule set does not overlap with other workflows.

This workflow acts as an error handler, sending real-time notifications to Telegram when another workflow fails. It provides detailed error information, including workflow name, timestamp, execution URL, last executed node, and error message. Pre-Conditions A Telegram bot created via BotFather. The bot token and Telegram group/channel chatId. An active n8n instance with the Telegram and Error Trigger nodes installed. Setup Workflow Configuration: Import the workflow into n8n. Update the Telegram chatId in the Config node. Add your Telegram bot token in the Telegram node credentials. Error Workflow Setup: Set this workflow as the Error Workflow in other workflows. Testing: Trigger an error in another workflow to verify Telegram notifications. Who the Workflow is For Developers:** Monitoring workflow failures in real-time. Teams:** Managing multiple n8n workflows and needing instant error alerts. n8n Users:** Looking for a simple way to handle workflow errors via Telegram. Primary Use Automates error notifications for failed workflows. Sends detailed error reports to Telegram for quick troubleshooting. Easily customizable to fit specific monitoring needs.

Introduction This workflow is designed to create and attach notes or comments to any record in your Odoo instance. It acts as a sub-workflow that can be triggered by a main workflow to log messages or comments in a centralized manner. By leveraging the powerful Odoo API, this template ensures that updates to records are handled efficiently, providing an organized way to document important information related to your business processes. Setup Instructions Import the Workflow: Import the provided JSON file into your n8n instance. Odoo Credentials: Ensure you have valid Odoo API credentials (e.g., "Roodsys Odoo Automation Account") configured in n8n. Node Configuration: Verify that the "Odoo" node (consider renaming it to "Odoo Record Manager" for clarity) is set up with your server details and authentication parameters. Check that the workflow trigger ("When Executed by Another Workflow") is configured to receive input parameters from the parent workflow. Execution Trigger: This workflow is designed to be initiated by another workflow. Make sure the main workflow supplies the required inputs. Workflow Details Trigger Node: The workflow begins with the "When Executed by Another Workflow" node, which accepts three inputs: rec_id: A numeric identifier for the Odoo record. message: The text of the comment or note. model: The specific Odoo model (e.g., rs.deployment.action.log) where the note should be attached. Odoo Node: The second node in the workflow calls the Odoo API to create a new log message. It maps the inputs as follows: message_type is set to "comment". model is assigned the provided model name. res_id is assigned the record ID (rec_id). body is assigned the message content. Additional Information: A sticky note node is included to provide a brief overview of the workflow’s purpose directly within the interface. Input Parameters Record ID (rec_id): The unique identifier of the record in Odoo where the note will be added. Message (message): The content of the comment or note that is to be logged. Model (model): The Odoo model name indicating the context in which the note should be created (e.g., rs.deployment.action.log). Usage Examples Internal Logging: Use the workflow to attach internal comments or logs to specific records, such as customer profiles, orders, or deployment logs. Audit Trails: Create a comprehensive audit trail by documenting changes or important events in Odoo records. Integration with Other Workflows: Link this workflow with other automation processes in n8n (like email notifications, data synchronization, or reporting) to create a seamless integration across your systems. Pre-conditions The Odoo instance must be accessible and correctly configured. API permissions and user roles should be validated to ensure that the workflow has the necessary access rights. The workflow expects inputs from an external trigger or parent workflow. Customization & Integration This template offers several customization options to tailor it to your needs: Field Customization: Modify or add new fields to match your logging or commenting requirements. Node Renaming: Rename nodes for better clarity and consistency within your workflow ecosystem. Integration Possibilities: Easily integrate this workflow with other processes in n8n, such as triggering notifications or synchronizing data across different systems. This sub-workflow receives data from a main workflow (for example, a record ID, a message, and the Odoo model) and creates a new note (or comment) in the corresponding Odoo record. Essentially, it acts as a centralized point for logging comments or notes in a specific Odoo model, ensuring that the information remains organized and easy to track. Your model must inherit from _inherit = ['portal.mixin', 'mail.thread.main.attachment']

Google Play Review Intelligence with Bright Data & Telegram Alerts Overview This n8n workflow automates the process of scraping Google Play Store reviews, analyzing app performance, and sending alerts for low-rated applications. It integrates with Bright Data for web scraping, Google Sheets for data storage, and Telegram for notifications. Workflow Components 1. ✅ Trigger Input Form Type:** Form Trigger Purpose:** Initiates the workflow with user input Input Fields:** URL (Google Play Store app URL) Number of reviews to fetch Function:** Captures user requirements to start the scraping process 2. 🚀 Start Scraping Request Type:** HTTP Request (POST) Purpose:** Sends scraping request to Bright Data API Endpoint:** https://api.brightdata.com/datasets/v3/trigger Parameters:** Dataset ID: gd_m6zagkt024uwvvwuyu Include errors: true Limit multiple results: 5 Custom Output Fields:** url, review_id, reviewer_name, review_date review_rating, review, app_url, app_title app_developer, app_images, app_rating app_number_of_reviews, app_what_new app_content_rating, app_country, num_of_reviews 3. 🔄 Check Scrape Status Type:** HTTP Request (GET) Purpose:** Monitors the progress of the scraping job Endpoint:** https://api.brightdata.com/datasets/v3/progress/{snapshot_id} Function:** Checks if the dataset scraping is complete 4. ⏱️ Wait for Response 45 sec Type:** Wait Node Purpose:** Implements polling mechanism Duration:** 45 seconds Function:** Pauses workflow before checking status again 5. 🧩 Verify Completion Type:** IF Condition Purpose:** Evaluates scraping completion status Condition:** status === "ready" Logic:** True: Proceeds to fetch data False: Loops back to status check 6. 📥 Fetch Scraped Data Type:** HTTP Request (GET) Purpose:** Retrieves the final scraped data Endpoint:** https://api.brightdata.com/datasets/v3/snapshot/{snapshot_id} Format:** JSON Function:** Downloads completed review and app data 7. 📊 Save to Google Sheet Type:** Google Sheets Node Purpose:** Stores scraped data for analysis Operation:** Append rows Target:** Specified Google Sheet document Data Mapping:** URL, Review ID, Reviewer Name, Review Date Review Rating, Review Text, App Rating App Number of Reviews, App What's New, App Country 8. ⚠️ Check Low Ratings Type:** IF Condition Purpose:** Identifies poor-performing apps Condition:** review_rating < 4 Logic:** True: Triggers alert notification False: No action taken 9. 📣 Send Alert to Telegram Type:** Telegram Node Purpose:** Sends performance alerts Message Format:** ⚠️ Low App Performance Alert 📱 App: {app_title} 🧑‍💻 Developer: {app_developer} ⭐ Rating: {app_rating} 📝 Reviews: {app_number_of_reviews} 🔗 View on Play Store Workflow Flow Input Form → Start Scraping → Check Status → Wait 45s → Verify Completion ↑ ↓ └──── Loop ────┘ ↓ Fetch Data → Save to Sheet & Check Ratings ↓ Send Telegram Alert Configuration Requirements API Keys & Credentials Bright Data API Key:** Required for web scraping Google Sheets OAuth2:** For data storage access Telegram Bot Token:** For alert notifications Setup Parameters Google Sheet ID:** Target spreadsheet identifier Telegram Chat ID:** Destination for alerts N8N Instance ID:** Workflow instance identifier Key Features Data Collection Comprehensive app metadata extraction Review content and rating analysis Developer and country information App store performance metrics Quality Monitoring Automated low-rating detection Real-time performance alerts Continuous data archiving Integration Capabilities Bright Data web scraping service Google Sheets data persistence Telegram instant notifications Polling-based status monitoring Use Cases App Performance Monitoring Track rating trends over time Identify user sentiment patterns Monitor competitor performance Quality Assurance Early warning for rating drops Customer feedback analysis Market reputation management Business Intelligence Review sentiment analysis Performance benchmarking Strategic decision support Technical Notes Polling Interval:** 45-second status checks Rating Threshold:** Alerts triggered for ratings < 4 Data Format:** JSON with structured field mapping Error Handling:** Includes error tracking in dataset requests Result Limiting:** Maximum 5 multiple results per request For any questions or support, please contact: info@incrementors.com or fill out this form https://www.incrementors.com/contact-us/

This template is an error handler that will log n8n workflow errors to a Monday.com board for troubleshooting and tracking. Prerequisites Monday account and Monday credential Create a board on Monday for error logging, with the following columns and types: Timestamp (text) Error Message (text) Stack Trace (long text) Determine the column IDs using Monday's instructions Setup Edit the Monday nodes to use your credential Edit the node labeled CREATE ERROR ITEM to point to your error log board and group name Edit the column IDs in the "Column Values" field of the UPDATE node to match the IDs of the fields on your error log board To trigger error logging, select this automation as the error workflow on any automation For more detailed logging, add Stop and Error nodes in your workflow to send specific error messages to your board.

Disclaimer: This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Important distinction: This template manages Telegram Copilot's UserBots (client accounts), not Telegram Bots. UserBot vs. Bot: Key Differences 🔹 Telegram Copilot's UserBots Authenticate as real user accounts (phone number required) Can join groups/channels without "Bot" label Subject to Telegram's client API limits Require manual login (MFA supported) 🔹 Telegram Bots Use @BotFather-created tokens Limited to bot API functionality Can't initiate chats with unbidden users No phone number required This template solves the unique challenges of UserBot management through: Core Functionality 🛡️ Session Reliability Automatic crash recovery (5-step restart sequence) Persistent session monitoring (checks every 6h) Database cleanup via /clear command 📱 Multi-Device Support Manages sessions independently from mobile clients Tracks active devices via /stat command Isolates session data per credential 🔔 Smart Notifications Real-time alerts to admin chat Detailed error context with authState snapshots Success confirmations with session metadata Setup Guide Prerequisites Self-hosted n8n instance (community node required) Valid Telegram account for UserBot Telegram bot token for notifications TelePilot credentials with api_id/api_hash Configuration Steps Credential Setup Add TelePilot credentials in n8n Configure Telegram bot token in notification nodes Set admin chat ID for alerts Monitoring Customization Adjust check frequency in Schedule Trigger Modify alert thresholds in Filter nodes Configure retry logic in recovery sequence Session Management Test /start command flow Verify /stat output format Confirm notification delivery Workflow Customization Advanced Options Add secondary notification channels (Email, Slack) Implement escalating alert system Integrate with monitoring dashboards Customize recovery attempt limits Compliance Notes UserBots must comply with Telegram's Terms of Service Not intended for bulk messaging or spam Recommended for legitimate automation use cases Note: UserBots must comply with Telegram ToS. Not for spam/mass messaging. Why This Matters: UserBots enable automation scenarios impossible with regular bots (e.g., group management as normal user, reacting as human account). This workflow keeps them reliably online 24/7.

AI-Powered SEO Keyword Research Workflow with n8n > automates comprehensive keyword research for content creation Table of Contents Introduction Workflow Architecture NocoDB Integration Data Flow Core Components Setup Requirements Possible Improvements Introduction This n8n workflow automates SEO keyword research using AI and data-driven analytics. It combines OpenAI's language models with DataForSEO's analytics to generate comprehensive keyword strategies for content creation. The workflow is triggered by a webhook from NocoDB, processes the input data through multiple stages, and returns a detailed content brief with optimized keywords. Workflow Architecture The workflow follows a structured process: Input Collection: Receives data via webhook from NocoDB Topic Expansion: Generates keywords using AI Keyword Metrics Analysis: Gathers search volume, CPC, and difficulty metrics Competitor Analysis: Analyzes competitor content for ranking keywords Final Strategy Creation: Combines all data to generate a comprehensive keyword strategy Output Storage: Saves results back to NocoDB and sends notifications NocoDB Integration Database Structure The workflow integrates with two tables in NocoDB: Input Table Schema This table collects the input parameters for the keyword research: | Field Name | Type | Description | | --------------- | ------------- | --------------------------------------------------------------------------- | | ID | Auto Number | Unique identifier | | Primary Topic | Text | The main keyword/topic to research | | Competitor URLs | Text | Comma-separated list of competitor websites | | Target Audience | Single Select | Description of the target audience (Solopreneurs, Marketing Managers, etc.) | | Content Type | Single Select | Type of content (Blog, Product page, etc.) | | Location | Single Select | Target geographic location | | Language | Single Select | Target language for keywords | | Status | Single Select | Workflow status (Pending, Started, Done) | | Start Research | Checkbox | Active Workflow when you set this to true | Output Table Schema This table stores the generated keyword strategy: | Field Name | Type | Description | | ------------------ | ----------- | ------------------------------------------------ | | ID | Auto Number | Unique identifier | | primary_topic_used | Text | The topic that was researched | | report_content | Long Text | The complete keyword strategy in Markdown format | | generatedAt | Datetime | Automatically generated by NocoDb | Webhook Settings NocoDB Webhook Settings Data Flow The workflow handles data in the following sequence: Webhook Trigger: Receives input from NocoDB when a new keyword research request is created Field Extraction: Extracts primary topic, competitor URLs, audience, and other parameters AI Topic Expansion: Uses OpenAI to generate related keywords, categorized by type and intent Keyword Analysis: Sends primary keywords to DataForSEO to get search volume, CPC, and difficulty Competitor Research: Analyzes competitor pages to identify their keyword rankings Strategy Generation: Combines all data to create a comprehensive keyword strategy Storage & Notification: Saves the strategy to NocoDB and sends a notification to Slack Core Components 1. Topic Expansion This component uses OpenAI and a structured output parser to generate: 20 primary keywords 30 long-tail keywords with search intent 15 question-based keywords 10 related topics 2. DataForSEO Integration Two API endpoints are used: Search Volume & CPC**: Gets monthly search volume and cost-per-click data Keyword Difficulty**: Evaluates how difficult it would be to rank for each keyword 3. Competitor Analysis This component: Analyzes competitor URLs to identify which keywords they rank for Identifies content gaps or opportunities Determines the search intent their content targets 4. Final Keyword Strategy The AI-generated strategy includes: Top 10 primary keywords with metrics 15 long-tail opportunities with low competition 5 question-based keywords to address in content Content structure recommendations 3 potential content titles optimized for SEO Setup Requirements To use this workflow, you'll need: n8n Instance: Either cloud or self-hosted NocoDB Account: For data input and storage API Keys: OpenAI API key DataForSEO API credentials Slack API token (for notifications) Database Setup: Create the required tables in NocoDB as described above Possible Improvements The workflow could be enhanced with the following improvements: Enhanced Keyword Strategy Add topic clustering to group related keywords Enhance the final output with more specific content structure suggestions Include word count recommendations for each content section Additional Data Sources Integrate Google Search Console data for existing content optimization Add Google Trends data to identify rising topics Include sentiment analysis for different keyword groups Improved Competitor Analysis Analyze content length and structure from top-ranking pages Identify common backlink sources for competitor content Extract content headings to better understand content organization Automation Enhancements Add scheduling capabilities to run updates on existing content Implement content performance tracking over time Create alert thresholds for changes in keyword difficulty or search volume Example Output Here is an example Output the Workflow generated based on the following inputs. Inputs: Primary Topic: AI Automation Competitor URLs: n8n.io, zapier.com, make.com Target Audience: Small Business Owners Content Type: Landing Page Location: United States Language: English Output: Final Keyword Strategy The workflow provides a powerful automation for content marketers and SEO specialists to develop data-driven keyword strategies with minimal manual effort. > Original Workflow: AI-Powered SEO Keyword Research Automation - The vibe Marketer

This n8n template lets you summarize individual team member activity on MS Teams for the past week and generates a report. For remote teams, chat is a crucial communication tool to ensure work gets done but with so many conversations happening at once and in multiple threads, ideas, information and decisions usually live in the moment and get lost just as quickly - and all together forgotten by the weekend! Using this template, this doesn't have to be the case. Have AI crawl through last week's activity, summarize all messages and replies and generate a casual and snappy report to bring the team back into focus for the current week. A project manager's dream! How it works A scheduled trigger is set to run every Monday at 6am to gather all team channel messages within the last week. Messages are grouped by user. AI analyses the raw messages and replies to pull out interesting observations and highlights. This is referred to as the individual reports. All individual reports are then combined and summarized together into what becomes the team weekly report. This allows understanding of group and similar activities. Finally, the team weekly report is posted back to the channel. The timing is important as it should be the first message of the week and ready for the team to glance over coffee. How to use Ideally works best per project and where most of the comms happens on a single channel. Avoid combining channels and instead duplicate this workflow for more channels. You may need to filter for specific team members if you want specific team updates. Customise the report to suit your organisation, team or the channel. You may prefer to be more formal if clients or external stakeholders are also present. Requirements MS Teams for chat platform OpenAI for LLM Customising this workflow If the teams channel is busy enough already, consider posting the final report to email. Pull in project metrics to include in your report. As extra context, it may be interesting to tie the messages to production performance. Use an AI Agent to query for knowledgebase or tickets relevant to the messages. This may be useful for attaching links or references to add context.

🤖 AI-Powered Content Marketing Research Tool > Transform your content strategy with automated competitor intelligence ⚡ What It Does Never miss a competitor move again. This workflow automatically: 🔍 Monitors competitor content across multiple domains 📊 Tracks trending keywords by region 💬 Extracts audience pain points from Reddit & forums 🤖 Generates AI strategy recommendations via OpenAI 📋 Outputs to Airtable, Notion & Slack for instant action 🎯 Perfect For Growth marketers** tracking competitor strategies Content teams** discovering trending topics SEO specialists** finding keyword opportunities Marketing agencies** managing multiple clients 🛠️ Technical Setup Required APIs & Credentials | Service | Credential Type | Monthly Cost | Purpose | |---------|----------------|--------------|---------| | Ahrefs | Header Auth | $99+ | Backlink & traffic analysis | | SEMrush | Query Auth | $119+ | Keyword research | | BuzzSumo | Header Auth | $199+ | Content performance | | OpenAI | Header Auth | ~$50 | AI recommendations | | Reddit | OAuth2 | Free | Audience insights | | Google Trends | Public API | Free | Trending topics | 📊 Database Schema Airtable Base: content-research-base Table 1: competitor-intelligence timestamp (Date) domain (Single line text) traffic_estimate (Number) backlinks (Number) content_gaps (Long text) publishing_frequency (Single line text) Table 2: keyword-opportunities timestamp (Date) trending_keywords (Long text) top_questions (Long text) content_opportunities (Long text) 🚀 Quick Start Guide Step 1: Import & Configure Import the workflow JSON Update competitor domains in 📋 Configuration Settings Map all API credentials Step 2: Setup Storage Airtable:** Create base with exact schema above Notion:** Create database with properties listed Slack:** Create #content-research-alerts channel Step 3: Test & Deploy First run populates: ✅ Airtable tables with competitor data ✅ Notion database with AI insights ✅ Slack channel with formatted alerts 💡 Example Output AI Recommendations Format { "action_items": [ { "topic": "Copy trading explainer", "format": "Video", "region": "UK", "priority": "High" } ], "publishing_calendar": [ {"week": "W34", "posts": 3} ], "alerts": [ "eToro gained 8 .edu backlinks this week" ] } Slack Alert Preview 🚨 Content Research Alert 📊 Top Findings: Sustainable packaging solutions Circular economy trends Eco-friendly manufacturing 📈 Trending Keywords: forex trading basics (+45%) social trading platforms (+32%) copy trading strategies (+28%) 💡 AI Recommendations: Focus on educational content in UK market... 🔧 Advanced Features ✅ Data Quality Validation Automatic retry** for failed API calls Data validation** before storage Error notifications** via Slack ⚙️ Scalability Options Multi-region support** (US, UK, DE, FR, JP) Batch processing** for large competitor lists Rate limiting** to respect API quotas 🎨 Customization Ready Modular design** - disable unused APIs Industry templates** - forex, ecommerce, SaaS Custom scoring** algorithms 📈 ROI & Performance Cost Analysis Setup time:** ~2 hours Monthly API costs:** $400-500 Time saved:** 15+ hours/week ROI:** 300%+ within first month Success Metrics Competitor insights:** 50+ data points daily Keyword opportunities:** 100+ suggestions/week Content ideas:** 20+ AI-generated topics Trend alerts:** Real-time notifications 🛡️ Troubleshooting Common Issues & Solutions | Symptom | Cause | Fix | |-------------|-----------|---------| | OpenAI timeout | Large data payload | Reduce batch size → Split processing | | Airtable 422 error | Field mismatch | Copy schema exactly | | Reddit 401 | OAuth expired | Re-authorize application | Rate Limiting Best Practices Ahrefs:** Max 1000 requests/day SEMrush:** 3000 requests/day OpenAI:** Monitor token usage 🌟 Why Choose This Template? > "From manual research to automated intelligence in 15 minutes" ✅ Production-ready - No additional coding required ✅ Cost-optimized - Uses free tiers where possible ✅ Scalable - Add competitors with one click ✅ Actionable - AI outputs ready for immediate use ✅ Community-tested - 500+ successful deployments Start your competitive intelligence today 🚀 Built with ❤️ for the n8n community

This workflow template provides a robust solution for efficiently sending multiple prompts to Anthropic's Claude models in a single batch request and retrieving the results. It leverages the Anthropic Batch API endpoint (/v1/messages/batches) for optimized processing and outputs each result as a separate item. Core Functionality & Example Usage Included This template includes: The Core Batch Processing Workflow: Designed to be called by another n8n workflow. An Example Usage Workflow: A separate branch demonstrating how to prepare data and trigger the core workflow, including examples using simple strings and n8n's Langchain Chat Memory nodes. Who is this for? This template is designed for: Developers, data scientists, and researchers** who need to process large volumes of text prompts using Claude models via n8n. Content creators** looking to generate multiple pieces of content (e.g., summaries, Q&As, creative text) based on different inputs simultaneously. n8n users** who want to automate interactions with the Anthropic API beyond single requests, improve efficiency, and integrate batch processing into larger automation sequences. Anyone needing to perform bulk text generation or analysis tasks with Claude programmatically. What problem does this workflow solve? Sending prompts to language models one by one can be slow and inefficient, especially when dealing with hundreds or thousands of requests. This workflow addresses that by: Batching:** Grouping multiple prompts into a single API call to Anthropic's dedicated batch endpoint (/v1/messages/batches). Efficiency:** Significantly reducing the time required compared to sequential processing. Scalability:** Handling large numbers of prompts (up to API limits) systematically. Automation:** Providing a ready-to-use, callable n8n structure for batch interactions with Claude. Structured Output:** Parsing the results and outputting each individual prompt's result as a separate n8n item. Use Cases: Bulk content generation (e.g., product descriptions, summaries). Large-scale question answering based on different contexts. Sentiment analysis or data extraction across multiple text snippets. Running the same prompt against many different inputs for research or testing. What the Core Workflow does (Triggered by the 'When Executed by Another Workflow' node) Receive Input: The workflow starts when called by another workflow (e.g., using the 'Execute Workflow' node). It expects input data containing: anthropic-version (string, e.g., "2023-06-01") requests (JSON array, where each object represents a single prompt request conforming to the Anthropic Batch API schema). Submit Batch Job: Sends the formatted requests data via POST to the Anthropic API /v1/messages/batches endpoint to create a new batch job. Requires Anthropic credentials. Wait & Poll: Enters a loop: Checks if the processing_status of the batch job is ended. If not ended, it waits for a set interval (10 seconds by default in the 'Batch Status Poll Interval' node). It then checks the batch job status again via GET to /v1/messages/batches/{batch_id}. Requires Anthropic credentials. This loop continues until the status is ended. Retrieve Results: Once the batch job is complete, it fetches the results file by making a GET request to the results_url provided in the batch status response. Requires Anthropic credentials. Parse Results: The results are typically returned in JSON Lines (.jsonl) format. The 'Parse response' Code node splits the response text by newlines and parses each line into a separate JSON object, storing them in an array field (e.g., parsed). Split Output: The 'Split Out Parsed Results' node takes the array of parsed results and outputs each result object as an individual item from the workflow. Prerequisites An active n8n instance (Cloud or self-hosted). An Anthropic API account with access granted to Claude models and the Batch API. Your Anthropic API Key. Basic understanding of n8n concepts (nodes, workflows, credentials, expressions, 'Execute Workflow' node). Familiarity with JSON data structures for providing input prompts and understanding the output. Understanding of the Anthropic Batch API request/response structure. (For Example Usage Branch) Familiarity with n8n's Langchain nodes (@n8n/n8n-nodes-langchain) if you plan to adapt that part. Setup Import Template: Add this template to your n8n instance. Configure Credentials: Navigate to the 'Credentials' section in your n8n instance. Click 'Add Credential'. Search for 'Anthropic' and select the Anthropic API credential type. Enter your Anthropic API Key and save the credential (e.g., name it "Anthropic account"). Assign Credentials: Open the workflow and locate the three HTTP Request nodes in the core workflow: Submit batch Check batch status Get results In each of these nodes, select the Anthropic credential you just configured from the 'Credential for Anthropic API' dropdown. Review Input Format: Understand the required input structure for the When Executed by Another Workflow trigger node. The primary inputs are anthropic-version (string) and requests (array). Refer to the Sticky Notes in the template and the Anthropic Batch API documentation for the exact schema required within the requests array. Activate Workflow: Save and activate the core workflow so it can be called by other workflows. ➡️ Quick Start & Input/Output Examples: Look for the Sticky Notes within the workflow canvas! They provide crucial information, including examples of the required input JSON structure and the expected output format. How to customize this workflow Input Source:* The core workflow is designed to be called. You will build *another workflow that prepares the anthropic-version and requests array and then uses the 'Execute Workflow' node to trigger this template. The included example branch shows how to prepare this data. Model Selection & Parameters:* Model (claude-3-opus-20240229, etc.), max_tokens, temperature, and other parameters are defined *within each object inside the requests array you pass to the workflow trigger. You configure these in the workflow calling this template. Polling Interval:** Modify the 'Wait' node ('Batch Status Poll Interval') duration if you need faster or slower status checks (default is 10 seconds). Be mindful of potential rate limits. Parsing Logic:** If Anthropic changes the result format or you have specific needs, modify the Javascript code within the 'Parse response' Code node. Error Handling:** Enhance the workflow with more specific error handling for API failures (e.g., using 'Error Trigger' or checking HTTP status codes) or batch processing issues (batch.status === 'failed'). Output Processing:* In the workflow that *calls this template, add nodes after the 'Execute Workflow' node to process the individual result items returned (e.g., save to a database, spreadsheet, send notifications). Example Usage Branch (Manual Trigger) This template also contains a separate branch starting with the Run example Manual Trigger node. Purpose:** This branch demonstrates how to construct the necessary anthropic-version and requests array payload. Methods Shown:** It includes steps for: Creating a request object from a simple query string. Creating a request object using data from n8n's Langchain Chat Memory nodes (@n8n/n8n-nodes-langchain). Execution:** It merges these examples, constructs the final payload, and then uses the Execute Workflow node to call the main batch processing logic described above. It finishes by filtering the results for demonstration. Note:** This branch is for demonstration and testing. You would typically build your own data preparation logic in a separate workflow. The use of Langchain nodes is optional for the core batch functionality. Notes API Limits:** According to the Anthropic API documentation, batches can contain up to 100,000 requests and be up to 256 MB in total size. Ensure your n8n instance has sufficient resources for large batches. API Costs:** Using the Anthropic API, including the Batch API, incurs costs based on token usage. Monitor your usage via the Anthropic dashboard. Completion Time:** Batch processing time depends on the number and complexity of prompts and current API load. The polling mechanism accounts for this variability. Versioning:** Always include the anthropic-version header in your requests, as shown in the workflow and examples. Refer to Anthropic API versioning documentation.

Automatically scrape Meta Threads for posts hiring specific roles (e.g. automation engineers, video editors, graphic designers), filter true hiring intent, deduplicate, and send alerts. We are taking automation roles as an example for now. What it does This workflow continuously scans Threads for fresh posts mentioning the roles you care about. It uses AI to filter out self-promotion and service ads, keeping only posts where the author is hiring. Qualified posts are saved into Google Sheets for tracking and sent to Telegram for instant alerts. It’s ideal for freelancers, agencies, and job seekers who want a steady radar of opportunities. How it works (Step by Step) Schedule trigger – Runs on a set interval (e.g. every 12 hours). Scrape Threads posts – Fetches recent posts from multiple keywords (e.g., “n8n expert”, “hire video editor”, “graphic designer”, etc.) via Apify. Merge results – Combines posts into a single stream. Normalize fields – Maps raw data into clean fields: text, author, URL, timestamp, profile link. AI filter – Uses an AI Agent to: Accept only posts where someone is hiring (rejects “hire me” style self-promo). Apply simple geography rules (e.g., allow US, UK, UAE, CA; pass unknowns). Exclude roles outside your scope. Deduplication – Checks Google Sheets to skip posts already seen. Save to Google Sheets – Writes qualified posts with full details. Telegram alerts – Sends you the matched post instantly so you can act. Who it’s for Freelancers: Get first dibs on gigs before others spot them. Agencies: Build a client pipeline by tracking hiring signals. Job seekers: Spot hidden opportunities in your target field. Customization Ideas Swap keywords to monitor roles you care about (e.g., “UI/UX designer”, “motion graphics editor”, “copywriter”). Add Slack or Discord notifications instead of Telegram. Expand geo rules to match your region. Use Sheets as a CRM—add columns for status, outreach date, etc