Enhance AI prompts with GPT-4o-mini and Telegram delivery
Workflow Documentation Description: This workflow is designed to optimize prompts by enhancing user inputs for clarity and specificity using AI. The workflow takes a user-provided prompt as input and uses a Natural Language Processing (NLP) model to refine and improve the prompt. The optimized prompt is then sent back to the user, ready for use in further workflows or processes. Setup: This workflow is suitable for users who want to improve their prompts for better communication and understanding in their workflows. The workflow utilizes an AI Agent powered by an OpenAI Chat Model to enhance user prompts. Expected Outcomes: Users can provide vague or imprecise prompts as input to the workflow. The AI Agent will refine and optimize the prompt, adding clarity and specific details. The optimized prompt will be delivered back to the user via Telegram or can be input for the next nodes. Extra Information: A. A Telegram node is used to deliver the optimized prompt back to the user. B. Ensure you have the necessary credentials set up for Telegram and OpenAI accounts. C. Customize the workflow's settings, such as the AI model used for prompt optimization, to suit your requirements. D. Activate the workflow once all configurations are set to start optimizing prompts efficiently.
By Jesse DavidsGenerate LinkedIn posts from Wikipedia with GPT-4 summaries and Ideogram images
Wikipedia to LinkedIn AI Content Poster with Image via Bright Data ๐ Overview Workflow Description: Automatically scrapes Wikipedia articles, generates AI-powered LinkedIn summaries with custom images, and posts professional content to LinkedIn using Bright Data extraction and intelligent content optimization. --- ๐ How It Works The workflow follows these simple steps: Article Input: User submits a Wikipedia article name through a simple form interface Data Extraction: Bright Data scrapes the Wikipedia article content including title and full text AI Summarization: Advanced AI models (OpenAI GPT-4 or Claude) create professional LinkedIn-optimized summaries under 2000 characters Image Generation: Ideogram AI creates relevant visual content based on the article summary LinkedIn Publishing: Automatically posts the summary with generated image to your LinkedIn profile URL Generation: Provides a shareable LinkedIn post URL for easy access and sharing --- โก Setup Requirements Estimated Setup Time: 10-15 minutes Prerequisites n8n instance (self-hosted or cloud) Bright Data account with Wikipedia dataset access OpenAI API account (for GPT-4 access) Anthropic API account (for Claude access - optional) Ideogram AI account (for image generation) LinkedIn account with API access --- ๐ง Configuration Steps Step 1: Import Workflow Copy the provided JSON workflow file In n8n: Navigate to Workflows โ + Add workflow โ Import from JSON Paste the JSON content and click Import Save the workflow with a descriptive name Step 2: Configure API Credentials ๐ Bright Data Setup Go to Credentials โ + Add credential โ Bright Data API Enter your Bright Data API token Replace BRIGHTDATAAPI_KEY in all HTTP request nodes Test the connection to ensure access ๐ค OpenAI Setup Configure OpenAI credentials in n8n Ensure GPT-4 model access Link credentials to the "OpenAI Chat Model" node Test API connectivity ๐จ Ideogram AI Setup Obtain Ideogram AI API key Replace IDEOGRAMAPIKEY in the "Image Generate" node Configure image generation parameters Test image generation functionality ๐ผ LinkedIn Setup Set up LinkedIn OAuth2 credentials in n8n Replace LINKEDINPROFILEID with your profile ID Configure posting permissions Test posting functionality Step 3: Configure Workflow Parameters Update Node Settings: Form Trigger: Customize the form title and field labels as needed AI Agent: Adjust the system message for different content styles Image Generate: Modify image resolution and rendering speed settings LinkedIn Post: Configure additional fields like hashtags or mentions Step 4: Test the Workflow Testing Recommendations: Start with a simple Wikipedia article (e.g., "Artificial Intelligence") Monitor each node execution for errors Verify the generated summary quality Check image generation and LinkedIn posting Confirm the final LinkedIn URL generation --- ๐ฏ Usage Instructions Running the Workflow Access the Form: Use the generated webhook URL to access the submission form Enter Article Name: Type the exact Wikipedia article title you want to process Submit Request: Click submit to start the automated process Monitor Progress: Check the n8n execution log for real-time progress View Results: The workflow will return a LinkedIn post URL upon completion Expected Output ๐ Content Summary Professional LinkedIn-optimized text Under 2000 characters Engaging and informative tone Bullet points for readability ๐ผ๏ธ Generated Image High-quality AI-generated visual 1280x704 resolution Relevant to article content Professional appearance ๐ LinkedIn Post Published to your LinkedIn profile Includes both text and image Shareable public URL Professional formatting --- ๐ ๏ธ Customization Options Content Personalization AI Prompts: Modify the system message in the AI Agent node to change writing style Character Limits: Adjust summary length requirements Tone Settings: Change from professional to casual or technical Hashtag Integration: Add relevant hashtags to LinkedIn posts Visual Customization Image Style: Modify Ideogram prompts for different visual styles Resolution: Change image dimensions based on LinkedIn requirements Rendering Speed: Balance between speed and quality Brand Elements: Include company logos or brand colors --- ๐ Troubleshooting Common Issues & Solutions โ ๏ธ Bright Data Connection Issues Verify API key is correctly configured Check dataset access permissions Ensure sufficient API credits Validate Wikipedia article exists ๐ค AI Processing Errors Check OpenAI API quotas and limits Verify model access permissions Review input text length and format Test with simpler article content ๐ผ๏ธ Image Generation Failures Validate Ideogram API key Check image prompt content Verify API usage limits Test with shorter prompts ๐ผ LinkedIn Posting Issues Re-authenticate LinkedIn OAuth Check posting permissions Verify profile ID configuration Test with shorter content --- โก Performance & Limitations Expected Processing Times Wikipedia Scraping: 30-60 seconds AI Summarization: 15-30 seconds Image Generation: 45-90 seconds LinkedIn Posting: 10-15 seconds Total Workflow: 2-4 minutes per article Usage Recommendations Best Practices: Use well-known Wikipedia articles for better results Monitor API usage across all services Test content quality before bulk processing Respect LinkedIn posting frequency limits Keep backup of successful configurations --- ๐ Use Cases ๐ Educational Content Create engaging educational posts from Wikipedia articles on science, history, or technology topics. ๐ข Thought Leadership Transform complex topics into accessible LinkedIn content to establish industry expertise. ๐ฐ Content Marketing Generate regular, informative posts to maintain active LinkedIn presence with minimal effort. ๐ฌ Research Sharing Quickly summarize and share research findings or scientific discoveries with your network. --- ๐ Conclusion This workflow provides a powerful, automated solution for creating professional LinkedIn content from Wikipedia articles. By combining web scraping, AI summarization, image generation, and social media posting, you can maintain an active and engaging LinkedIn presence with minimal manual effort. The workflow is designed to be flexible and customizable, allowing you to adapt the content style, visual elements, and posting frequency to match your professional brand and audience preferences. For any questions or support, please contact: info@incrementors.com or fill out this form: https://www.incrementors.com/contact-us/
By IncrementorsFind valid vouchers and promo codes with SerpAPI, Decodo, and GPT-5 Mini
Promo Seeker finds fresh, working promo codes and vouchers on the web so your team never misses a deal. This n8n workflow uses SerpAPI and Decodo Scrapper for real-time search, an agent powered by GPT-5 Mini for filtering and validation, and Chat Memory to keep contextโsaving time, reducing manual checks, and helping marketing or customer support teams deliver discounts faster to customers (and yes, it's better at hunting promos than your inbox). ๐ก Why Use Promo Seeker? Speed: Saves hours per week by automatically finding and validating current promo codes, so you can publish deals faster. Simplicity: Eliminates manual searching across sites, no more copy-paste scavenger hunts. Accuracy: Reduces false positives by cross-checking results and keeping only working vouchersโfewer embarrassed "expired code" moments. Edge: Combine search APIs with an AI agent to surface hard-to-find, recently-live offersโwin over competitors who still rely on manual scraping. โก Perfect For Marketing teams: Quickly populate newsletters, landing pages, or ads with valid promos. Customer support: Give verified discount codes to users without ping-ponging between tabs. Deal aggregators & affiliates: Discover fresh vouchers faster and boost conversion rates. ๐ง How It Works โฑ Trigger: A user message via the chat webhook starts the search (Message node). ๐ Process: The agent queries SerpAPI and Decodo Scrapper to collect potential promo codes and voucher pages. ๐ค Smart Logic: The Promo Seeker Agent uses GPT-5 Mini with Chat Memory to filter for fresh, working promos and to verify validity and relevance. ๐ Output: Results are returned to the chat with clear, copy-ready promo codes and source links. ๐ Storage: Chat Memory stores context and recent searches so the agent avoids repeating old results and can follow up with improved queries. ๐ Quick Setup Import JSON file to your n8n instances Add credentials: SerpAPI, Azure OpenAI (Gpt 5 Mini), Decodo API Customize: Search parameters (brands, regions, validity window), agent system message, and result formatting Update: Azure OpenAI endpoint and API key in the Gpt 5 Mini credentials; add your SerpAPI key and Decodo key Test: Run a few queries like "latest Amazon promo" or "food delivery voucher" and confirm returned codes are valid ๐งฉ You'll Need Active n8n instances SerpAPI account and API key Azure OpenAI (for GPT-5 Mini) with key and endpoint Decodo account/API key ๐ ๏ธ Level Up Ideas Push verified promos to a Slack channel or email digest for the team. Add scheduled scans to detect newly expired codes and remove them from lists. Integrate with a CMS to auto-post verified deals to landing pages. Made by: khaisa Studio Tags: promo, vouchers, discounts Category: Marketing Automation Need custom work? Contact Us
By Khaisa StudioConvert training prescriptions to Intervals.icu workouts with Claude Opus AI
Description Transform training prescriptions into perfectly formatted Intervals.icu workouts using AI. This workflow automatically converts free-text workout descriptions into structured interval training sessions with proper heart rate zones, pace calculations, and exercise formatting. What this workflow does Collects workout details via a web form (date, title, and workout description) Fetches athlete data from Intervals.icu (FTP, max HR, threshold pace, LTHR) Processes with AI using Claude Opus 4.1 to intelligently parse and format the workout Auto-detects workout type (Run, Ride, Strength, HYROX, CrossFit, etc.) Converts training zones - RPE โ HR%, pace calculations, power zones Formats workout structure with proper transitions, rest periods, circuit formatting Creates the workout in Intervals.icu via API Use cases Coaches: Convert training plans from documents/spreadsheets into Intervals.icu format Athletes: Quickly add structured workouts from coaching apps or training programs Hybrid training: Handle complex HYROX, CrossFit, or multi-sport sessions with circuit formatting Time savings: Eliminate manual workout entry and zone calculations Supported workout types Running, cycling, swimming, strength training, HYROX, CrossFit, indoor rowing, virtual training (Zwift), triathlon, and more. Key features โ Intelligent workout type detection โ Automatic RPE to HR zone conversion using athlete-specific data โ Proper formatting for intervals, circuits, supersets, and progressions โ Adds transitions between exercises/machines โ Calculates exercise durations and pacing โ Handles warmup/cooldown sections โ Generates unique workout IDs Setup requirements Intervals.icu account with API access (API key required) Anthropic API key for Claude AI Athlete must have training zones configured in Intervals.icu (FTP, max HR, LTHR, threshold pace) Setup instructions Getting your Intervals.icu API key Log in to Intervals.icu Go to Settings (gear icon) โ Developer Settings Click Generate API Key (or copy your existing key) Save the API key securely Configuring credentials in n8n For Intervals.icu (HTTP Basic Auth): In n8n, open the GetAthleteInfo or CreateWorkoutAPI node Click on Credentials โ Create New Credential Select HTTP Basic Auth Enter: Username: APIKEY (literally type "APIKEY") Password: Your actual API key from Intervals.icu Click Save Apply this credential to both HTTP Request nodes For Anthropic: Open the Anthropic Chat Model node Click on Credentials โ Create New Credential Enter your Anthropic API key Click Save Important: The Intervals.icu API uses HTTP Basic Authentication where the username is always the literal string "API_KEY" and the password is your actual API key. How it works The workflow uses a sophisticated AI agent with a detailed system prompt that understands training terminology, zones, and Intervals.icu formatting requirements. It applies sport-specific rules to ensure workouts are properly structured for tracking during training sessions.
By Martijn Kerver