by Ranjan Dailata
This n8n workflow automates domain level keyword ranking analysis and enriches raw SEO metrics with AI-generated summaries. It combines structured keyword data from SE Ranking with natural-language insights produced by OpenAI, turning complex SERP datasets into actionable SEO intelligence. Who this is for? This workflow is designed for: SEO engineers and technical marketers Growth teams running programmatic SEO Agencies managing multi-domain keyword analysis Product teams building SEO analytics pipelines Developers using n8n for data enrichment and reporting If you work with keyword data and need machine-readable output plus human-readable insights, this workflow is for you. What this workflow does Accepts a target domain or URL, region, keyword type (organic/paid), and filters Fetches keyword ranking data from the SE Ranking Domain Keywords API Extracts metrics such as: Keyword positions Search volume & CPC Competition & difficulty SERP features & search intent Traffic estimates Uses OpenAI GPT-4.1-mini to generate: A comprehensive narrative summary A concise abstract overview Merges raw data and AI insights into a single enriched dataset Exports the final output as structured JSON for downstream use Setup Prerequisites Active SE Ranking API access OpenAI API key with GPT-4.1-mini enabled Running n8n instance (self-hosted or cloud) Basic understanding of keyword ranking metrics Configuration steps If you are new to SE Ranking, please signup on seranking.com Import the workflow JSON into n8n Configure credentials: SE Ranking using HTTP Header Authentication. Please make sure to set the header authentication as below. The value should contain a Token followed by a space with the SE Ranking API Key. OpenAI API (GPT-4.1-mini model) Open the Set the Input Fields node and define: target_site (domain or URL) source (region, e.g. us) type (organic or paid) limit, filters, and requested columns Verify the output as per the export data handling. Converts enriched SEO results into structured JSON output Creates binary data to support file-based exports Converts processed data into CSV format for easy analysis Inserts or updates records in Google Sheets for reporting Ensures data consistency across all export destinations Enables downstream automation, dashboards, and audits Click Execute Workflow How to customize this workflow to your needs You can easily adapt this workflow by: Switching between organic and paid keyword analysis Changing regions for international SEO tracking Modifying requested keyword columns and SERP filters Customizing the OpenAI prompt to generate: SEO action items Competitive insights Executive summaries Replacing file export with: Databases Dashboards Slack/Email alerts Data warehouses Summary This n8n template delivers a production ready SEO analytics pipeline that bridges structured SERP data with AI powered interpretation. By combining SE Ranking’s keyword intelligence with OpenAI driven summarization, it enables faster insights, better reporting, and scalable SEO decision making without manual analysis.
by Avkash Kakdiya
How it works This workflow automatically collects a list of companies from Google Sheets, searches for their competitors using SerpAPI, extracts up to 10 relevant competitor names with source links, and logs the results into both Google Sheets and Airtable. It runs on a set schedule, cleans and formats the company list, processes each entry individually, checks if competitors exist, and separates results into successful and “no competitors found” lists for organized tracking. Step-by-step 1. Trigger & Input Auto Run (Scheduled) – Executes every day at the set time (e.g., 9 AM). Read Companies Sheet – Pulls the list of companies from a Google Sheet (List column). Clean & Format Company List – Removes empty rows, trims names, and attaches row numbers for tracking. Loop Over Companies – Processes each company one at a time in batches. 2. Competitor Search Search Company Competitors (SerpAPI) – Sends a query like "{Company} competitors" to SerpAPI, retrieving structured search results in JSON format. 3. Data Extraction & Validation Extract Competitor Data from Search – Parses SerpAPI results to: Identify the company name Extract up to 10 competitor names Capture the top source URL Count total search results Has Competitors? – Checks if any competitors were found: Yes → Proceeds to logging No → Logs in “no results” list 4. Logging Results Log to Result Sheet – Appends or updates competitor data into the results Google Sheet. Log Companies Without Results – Records companies with zero competitors found in a separate section of the results sheet. Sync to Airtable – Pushes all results (successful or not) into Airtable for unified storage and analysis. Benefits Automated Competitor Research – Eliminates the need for manual Google searching. Daily Insights – Runs automatically at your chosen schedule. Clean Data Output – Stores structured competitor lists with sources for easy review. Multi-Destination Sync – Saves to both Google Sheets and Airtable for flexibility. Scalable & Hands-Free – Handles hundreds of companies without extra effort.
by Daniel Shashko
How it Works This workflow automates intelligent Reddit marketing by monitoring brand mentions, analyzing sentiment with AI, and engaging authentically with communities. Every 24 hours, the system searches Reddit for posts containing your configured brand keywords across all subreddits, finding up to 50 of the newest mentions to analyze. Each discovered post is sent to OpenAI's GPT-4o-mini model for comprehensive analysis. The AI evaluates sentiment (positive/neutral/negative), assigns an engagement score (0-100), determines relevance to your brand, and generates contextual, helpful responses that add genuine value to the conversation. It also classifies the response type (educational/supportive/promotional) and provides reasoning for whether engagement is appropriate. The workflow intelligently filters posts using a multi-criteria system: only posts that are relevant to your brand, score above 60 in engagement quality, and warrant a response type other than "pass" proceed to engagement. This prevents spam and ensures every interaction is meaningful. Selected posts are processed one at a time through a loop to respect Reddit's rate limits. For each worthy post, the AI-generated comment is posted, and complete interaction data is logged to Google Sheets including timestamp, post details, sentiment, engagement scores, and success status. This creates a permanent audit trail and analytics database. At the end of each run, the workflow aggregates all data into a comprehensive daily summary report with total posts analyzed, comments posted, engagement rate, sentiment breakdown, and the top 5 engagement opportunities ranked by score. This report is automatically sent to Slack with formatted metrics, giving your team instant visibility into your Reddit marketing performance. Who is this for? Brand managers and marketing teams** needing automated social listening and engagement on Reddit Community managers** responsible for authentic brand presence across multiple subreddits Startup founders and growth marketers** who want to scale Reddit marketing without hiring a team PR and reputation teams** monitoring brand sentiment and responding to discussions in real-time Product marketers** seeking organic engagement opportunities in product-related communities Any business** that wants to build authentic Reddit presence while avoiding spammy marketing tactics Setup Steps Setup time:** Approx. 30-40 minutes (credential configuration, keyword setup, Google Sheets creation, Slack integration) Requirements:** Reddit account with OAuth2 application credentials (create at reddit.com/prefs/apps) OpenAI API key with GPT-4o-mini access Google account with a new Google Sheet for tracking interactions Slack workspace with posting permissions to a marketing/monitoring channel Brand keywords and subreddit strategy prepared Create Reddit OAuth Application: Visit reddit.com/prefs/apps, create a "script" type app, and obtain your client ID and secret Configure Reddit Credentials in n8n: Add Reddit OAuth2 credentials with your app credentials and authorize access Set up OpenAI API: Obtain API key from platform.openai.com and configure in n8n OpenAI credentials Create Google Sheet: Set up a new sheet with columns: timestamp, postId, postTitle, subreddit, postUrl, sentiment, engagementScore, responseType, commentPosted, reasoning Configure these nodes: Brand Keywords Config: Edit the JavaScript code to include your brand name, product names, and relevant industry keywords Search Brand Mentions: Adjust the limit (default 50) and sort preference based on your needs AI Post Analysis: Customize the prompt to match your brand voice and engagement guidelines Filter Engagement-Worthy: Adjust the engagementScore threshold (default 60) based on your quality standards Loop Through Posts: Configure max iterations and batch size for rate limit compliance Log to Google Sheets: Replace YOUR_SHEET_ID with your actual Google Sheets document ID Send Slack Report: Replace YOUR_CHANNEL_ID with your Slack channel ID Test the workflow: Run manually first to verify all connections work and adjust AI prompts Activate for daily runs: Once tested, activate the Schedule Trigger to run automatically every 24 hours Node Descriptions (10 words each) Daily Marketing Check - Schedule trigger runs workflow every 24 hours automatically daily Brand Keywords Config - JavaScript code node defining brand keywords to monitor Reddit Search Brand Mentions - Reddit node searches all subreddits for brand keyword mentions AI Post Analysis - OpenAI analyzes sentiment, relevance, generates contextual helpful comment responses Filter Engagement-Worthy - Conditional node filters only high-quality relevant posts worth engaging Loop Through Posts - Split in batches processes each post individually respecting limits Post Helpful Comment - Reddit node posts AI-generated comment to worthy Reddit discussions Log to Google Sheets - Appends all interaction data to spreadsheet for permanent tracking Generate Daily Summary - JavaScript aggregates metrics, sentiment breakdown, generates comprehensive daily report Send Slack Report - Posts formatted daily summary with metrics to team Slack channel
by Rakin Jakaria
Who this is for This workflow is for content creators, digital marketers, or YouTube strategists who want to automatically discover trending videos in their niche, analyze engagement metrics, and get data-driven insights for their content strategy — all from one simple form submission. What this workflow does This workflow starts every time someone submits the YouTube Trends Finder Form. It then: Searches YouTube videos* based on your topic and specified time range using the *YouTube Data API**. Fetches detailed analytics** (views, likes, comments, engagement rates) for each video found. Calculates engagement rates** and filters out low-performing content (below 2% engagement). Applies smart filters** to exclude videos with less than 1000 views, content outside your timeframe, and hashtag-heavy titles. Removes duplicate videos** to ensure clean data. Creates a Google Spreadsheet** with all trending video data organized by performance metrics. Delivers the results** via a completion form with a direct link to your analytics report. Setup To set this workflow up: Form Trigger – Customize the "YouTube Trends Finder" form fields if needed (Topic Name, Last How Many Days). YouTube Data API – Add your YouTube OAuth2 credentials and API key in the respective nodes. Google Sheets – Connect your Google Sheets account for automatic report generation. Engagement Filters – Adjust the 2% engagement rate threshold based on your quality standards. View Filters – Modify the minimum view count (currently 1000+) in the filter conditions. Regional Settings – Update the region code (currently "US") to target specific geographic markets. How to customize this workflow to your needs Change the engagement rate threshold to be more or less strict based on your niche requirements. Add additional filters like video duration, subscriber count, or specific keywords to refine results. Modify the Google Sheets structure to include extra metrics like "Channel Name", "Video Duration", or "Trending Score". Switch to different output formats like CSV export or direct email reports instead of Google Sheets.
by Rahul Joshi
📊 Description Streamline Facebook Messenger inbox management with an AI-powered categorization and response system. 💬⚙️ This workflow automatically classifies new messages as Lead, Query, or Spam using GPT-4, routes them for approval via Slack, responds on Facebook once approved, and logs all interactions into Google Sheets for tracking. Perfect for support and marketing teams managing high volumes of inbound DMs. 🚀📈 What This Template Does 1️⃣ Trigger – Runs hourly to fetch new Facebook Page messages. ⏰ 2️⃣ Extract & Format – Collects sender info, timestamps, and message content for analysis. 📋 3️⃣ AI Categorization – Uses GPT-4 to identify message type (Lead, Query, Spam) and suggest replies. 🧠 4️⃣ Slack Approval Flow – Sends categorized leads and queries to Slack for quick team approval. 💬 5️⃣ Facebook Response – Posts AI-suggested replies back to the original sender once approved. 💌 6️⃣ Data Logging – Records every message, reply, and approval status into Google Sheets for analytics. 📊 7️⃣ Error Handling – Automatically alerts via Slack if the workflow encounters an error. 🚨 Key Benefits ✅ Reduces manual message triage on Facebook Messenger ✅ Ensures consistent and professional customer replies ✅ Provides full visibility via Google Sheets logs ✅ Centralizes team approvals in Slack for faster response times ✅ Leverages GPT-4 for accurate categorization and natural replies Features Hourly Facebook message fetch with Graph API GPT-4 powered text classification and reply suggestion Slack-based dual approval flow Automated Facebook replies post-approval Google Sheets logging for all categorized messages Built-in error detection and Slack alerting Requirements Facebook Graph API credentials with page message permissions OpenAI API key for GPT-4 processing Slack API credentials with chat:write permission Google Sheets OAuth2 credentials Environment Variables: FACEBOOK_PAGE_ID GOOGLE_SHEET_ID GOOGLE_SHEET_NAME SLACK_CHANNEL_ID Target Audience Marketing and lead-generation teams using Facebook Pages 📣 Customer support teams managing Messenger queries 💬 Businesses seeking automated lead routing and CRM sync 🧾 Teams leveraging AI for customer engagement optimization 🤖 Step-by-Step Setup Instructions 1️⃣ Connect Facebook Graph API credentials and set your page ID. 2️⃣ Add OpenAI API credentials for GPT-4. 3️⃣ Configure Slack channel ID and credentials. 4️⃣ Link your Google Sheet for message logging. 5️⃣ Replace environment variable placeholders with your actual IDs. 6️⃣ Test the workflow manually before enabling automation. 7️⃣ Activate the schedule trigger for ongoing hourly execution. ✅
by Rahul Joshi
Description Keep your CRM pipeline clean and actionable by automatically archiving inactive deals, logging results to Google Sheets, and sending Slack summary reports. This workflow ensures your sales team focuses on active opportunities while maintaining full audit visibility. 🚀📈 What This Template Does Triggers daily at 9 AM to check all GoHighLevel CRM opportunities. ⏰ Filters deals that have been inactive for 10+ days using last activity or update date. 🔍 Automatically archives inactive deals to keep pipelines clutter-free. 📦 Formats and logs deal details into Google Sheets for record-keeping. 📊 Sends a Slack summary report with total archived count, value, and deal names. 💬 Key Benefits ✅ Keeps pipelines organized by removing stale opportunities. ✅ Saves time through fully automated archiving and reporting. ✅ Maintains a transparent audit trail in Google Sheets. ✅ Improves sales visibility with automated Slack summaries. ✅ Easily adjustable inactivity threshold and scheduling. Features Daily scheduled trigger (9 AM) with adjustable cron expression. GoHighLevel CRM integration for fetching and updating opportunities. Conditional logic to detect inactivity periods. Google Sheets logging with automatic updates. Slack integration for real-time reporting and team visibility. Requirements GoHighLevel API credentials (OAuth2) with opportunity access. Google Sheets OAuth2 credentials with edit permissions. Slack Bot token with chat:write permission. A connected n8n instance (cloud or self-hosted). Target Audience Sales and operations teams managing CRM hygiene. Business owners wanting automated inactive deal cleanup. Agencies monitoring client pipelines across teams. CRM administrators ensuring data accuracy and accountability. Step-by-Step Setup Instructions Connect your GoHighLevel OAuth2 credentials in n8n. 🔑 Link your Google Sheets document and replace the Sheet ID. 📋 Configure Slack credentials and specify your target channel. 💬 Adjust inactivity threshold (default: 10 days) as needed. ⚙️ Update the cron schedule (default: 9 AM daily). ⏰ Test the workflow manually to verify end-to-end automation. ✅
by Hugo
🤖 n8n AI Workflow Dashboard Template Overview This template is designed to collect execution data from your AI workflows and generate an interactive dashboard for easy monitoring. It's compatible with any AI Agent or RAG workflow in n8n. Main Objectives 💾 Collect Execution Data Track messages, tokens used (prompt/completion), session IDs, model names, and compute costs Designed to plug into any AI agent or RAG workflow in n8n 📊 Generate an Interactive Dashboard Visualize KPIs like total messages, unique sessions, tokens used, and costs Display daily charts, including stacked bars for prompt vs completion tokens Monitor AI activity, analyze usage, and track costs at a glance ✨ Key Features 💬 Conversation Data Collection Messages sent to the AI agent are recorded with: sessionId chatInput output promptTokens, completionTokens, totalTokens globalCost and modelName This allows detailed tracking of AI interactions across sessions. 💰 Model Pricing Management A sub-workflow with a Set node provides token prices for LLMs Data is stored in the Model price table for cost calculations 🗄️ Data Storage via n8n Data Tables Two tables need to be created: Model price { "id": 20, "createdAt": "2025-10-11T12:16:47.338Z", "updatedAt": "2025-10-11T12:16:47.338Z", "name": "claude-4.5-sonnet", "promptTokensPrice": 0.000003, "completionTokensPrice": 0.000015 } Messages [ { "id": 20, "createdAt": "2025-10-11T15:28:00.358Z", "updatedAt": "2025-10-11T15:31:28.112Z", "sessionId": "c297cdd4-7026-43f8-b409-11eb943a2518", "action": "sendMessage", "output": "Hey! \nHow's it going?", "chatInput": "yo", "completionTokens": 6, "promptTokens": 139, "totalTokens": 139, "globalCost": null, "modelName": "gpt-4.1-mini", "executionId": 245 } ] These tables store conversation data and pricing info to feed the dashboard and calculations. 📈 Interactive Dashboard KPIs Generated**: total messages, unique sessions, total/average tokens, total/average cost 💸 Charts Included**: daily messages, tokens used per day (prompt vs completion, stacked bar) Provides a visual summary of AI workflow performance ⚙️ Installation & Setup Follow these steps to set up and run the workflow in n8n: 1. Import the Workflow Download or copy the JSON workflow and import it into n8n. 2. Create the Data Tables Model price table**: stores token prices per model Messages table**: stores messages generated by the AI agent 3. Configure the Webhook The workflow is triggered via a webhook Use the webhook URL to send conversation data 4. Set Up the Pricing Sub-workflow Automatically generates price data for the models used Connect it to your main workflow to enrich cost calculations 5. Dashboard Visualization The workflow returns HTML code rendering the dashboard View it in a browser or embed it in your interface 🌐 Once configured, your workflow tracks AI usage and costs in real-time, providing a live dashboard for quick insights. 🔧 Adaptability The template is modular and can be adapted to any AI agent or RAG workflow KPIs, charts, colors, and metrics can be customized in the HTML rendering Ideal for monitoring, cost tracking, and reporting AI workflow performance
by Recrutei Automações
Overview: Automated LinkedIn Job Posting with AI This workflow automates the publication of new job vacancies on LinkedIn immediately after they are created in the Recrutei ATS (Applicant Tracking System). It leverages a Code node to pre-process the job data and a powerful AI model (GPT-4o-mini, configured via the OpenAI node) to generate compelling, marketing-ready content. This template is designed for Recruitment and Marketing teams aiming to ensure consistent, timely, and high-quality job postings while saving significant operational time. Workflow Logic & Steps Recrutei Webhook Trigger: The workflow is instantly triggered when a new job vacancy is published in the Recrutei ATS, sending all relevant job data via a webhook. Data Cleaning (Code Node 1): The first Code node standardizes boolean fields (like remote, fixed_remuneration) from 0/1 to descriptive text ('yes'/'no'). Prompt Transformation (Code Node 2): The second, crucial Code node receives the clean job data and: Maps the original data keys (e.g., title, description) to user-friendly labels (e.g., Job Title, Detailed Description). Cleans and sanitizes the HTML description into readable Markdown format. Generates a single, highly structured prompt containing all job details, ready for the AI model. AI Content Generation (OpenAI): The AI Model receives the structured prompt and acts as a 'Marketing Copywriter' to create a compelling, engaging post specifically optimized for the LinkedIn platform. LinkedIn Post: The generated text is automatically posted to the configured LinkedIn profile or Company Page. Internal Logging (Google Sheets): The workflow concludes by logging the event (Job Title, Confirmation Status) into a Google Sheet for internal tracking and auditing. Setup Instructions To implement this workflow successfully, you must configure the following: Credentials: Configure OpenAI (for the Content Generator). Configure LinkedIn (for the Post action). Configure Google Sheets (for the logging). Node Configuration: Set up the Webhook URL in your Recrutei ATS settings. Replace YOUR_SHEET_ID_HERE in the Google Sheets Logging node with your sheet's ID. Select the correct LinkedIn profile/company page in the Create a post node.
by AFK Crypto
Try It Out! The AI Investment Research Assistant (Discord Summary Bot) transforms your Discord server into a professional-grade AI-driven crypto intelligence center. Running automatically every morning, it gathers real-time news, sentiment, and market data from multiple trusted sources — including NewsAPI, Crypto Compare, and CoinGecko — covering the most influential digital assets like BTC, ETH, SOL, BNB, and ADA. An AI Research Analyst Agent then processes this data using advanced reasoning and summarization to deliver a structured Market Intelligence Briefing. Each report distills key market events, sentiment shifts, price movements, and analyst-grade insights, all formatted into a visually clean and actionable message that posts directly to your Discord channel. Whether you’re a fund manager, community owner, or analyst, this workflow helps you stay informed about market drivers — without manually browsing dozens of news sites or data dashboards. Detailed Use Cases Crypto Research Teams:** Automate daily market briefings across key assets. Investment Communities:** Provide daily insights and sentiment overviews directly on Discord. Trading Desks:** Quickly review summarized market shifts and performance leaders. DAOs or Fund Analysts:** Centralize institutional-style crypto intelligence into your server. How It Works Daily Trigger (Schedule Node) – Activates each morning to begin data collection. News Aggregation Layer – Uses NewsAPI (and optionally CryptoPanic or GDELT) to fetch the latest crypto headlines and event coverage. Market & Sentiment Fetch – Collects market metrics via CoinGecko or Crypto Compare, including: 24-hour price change Market cap trend Social sentiment or Fear & Greed index AI Research Analyst (LLM Agent) – Processes and synthesizes all data into a cohesive insight report containing: 🧠 Executive Summary 📊 Top Gainers & Losers 💬 Sentiment Overview 🔍 Analyst Take / Actionable Insight Formatting Layer (Code Node) – Converts the analysis into a Discord-ready structure. Discord Posting Node – Publishes the final Market Intelligence Briefing to a specified Discord channel. Setup and Customization Import this workflow into your n8n workspace. Configure credentials: NewsAPI Key – For crypto and blockchain news. CoinGecko / Crypto Compare API Key – For real-time asset data. LLM Credential – OpenAI, Gemini, or Anthropic. Discord Webhook URL or Bot Token – To post updates. Customize the tracked assets in the News and Market nodes (BTC, ETH, SOL, BNB, ADA, etc.). Set local timezone for report delivery. Deploy and activate — your server will receive automated morning briefings. Output Format Each daily report includes: 📰 AI Market Intelligence Briefing 📅 Date: October 16, 2025 💰 Top Movers: BTC +2.3%, SOL +1.9%, ETH -0.8% 💬 Sentiment: Moderately Bullish 🔍 Analyst Take: Accumulation signals forming in mid-cap layer-1s. 📈 Outlook: Positive bias, with ETH showing strong support near $2,400. Compact yet rich in insight, this format ensures quick readability and fast decision-making for traders and investors. (Optional) Extend This Workflow Portfolio-Specific Insights:** Fetch your wallet holdings from AFK Crypto or Zapper APIs for personalized reports. Interactive Commands:** Add /compare or /analyze commands for Discord users. Multi-Language Summaries:** Auto-translate for international communities. Historical Data Logging:** Store briefings in Notion or Google Sheets. Weekly Recaps:** Summarize all daily reports into a long-form analysis. Requirements n8n Instance** (with HTTP Request, AI Agent, and Discord nodes enabled) NewsAPI Key** CoinGecko / Crypto Compare API Key** LLM Credential** (OpenAI / Gemini / Anthropic) Discord Bot Token or Webhook URL** APIs Used GET https://newsapi.org/v2/everything?q=crypto OR bitcoin OR ethereum OR defi OR nft&language=en&sortBy=publishedAt&pageSize=10 GET https://api.coingecko.com/api/v3/simple/price?ids=bitcoin,ethereum,solana&vs_currencies=usd&include_market_cap=true&include_24hr_change=true (Optional) GET https://cryptopanic.com/api/v1/posts/?auth_token=YOUR_TOKEN&kind=news (Optional) GET https://api.gdeltproject.org/api/v2/doc/doc?query=crypto&format=json Summary The AI Investment Research Assistant (Discord Summary Bot) is your personal AI research analyst — delivering concise, data-backed crypto briefings directly to Discord. It intelligently combines news aggregation, sentiment analysis, and AI reasoning to create actionable market intelligence each morning. Ideal for crypto traders, funds, or educational communities seeking a reliable daily edge — this workflow replaces hours of manual research with one automated, professional-grade summary. Our Website: https://afkcrypto.com/ Check our blogs: https://www.afkcrypto.com/blog
by Oussama
This n8n template creates an intelligent expense tracking system 🤖 that processes text, voice, and receipt images through Telegram. The assistant automatically categorizes expenses, handles currency conversions 🌍, and maintains financial records in Google Sheets while providing smart spending insights 💡. Use Cases: 🗣️ Personal expense tracking via Telegram chat 🧾 Receipt scanning and data extraction 💱 Multi-currency expense management 📂 Automated financial categorization 🎙️ Voice-to-expense logging 📊 Daily/weekly/monthly spending analysis How it works: Multi-Input Processing: Telegram trigger captures text messages, voice notes, and receipt images. Content Analysis: A Switch node routes different input types (text, audio, images) to appropriate processors. Voice Processing: ElevenLabs converts voice messages to text for expense extraction. Receipt OCR: Google Gemini analyzes receipt images to extract amounts and descriptions. Expense Classification: An LLM determines if the input is an expense or a general query. Expense Parsing: For multiple expenses, the AI splits and normalizes each item. Currency Conversion: An exchange rate API converts foreign currencies to USD. Smart Categorization: The AI agent assigns expenses to predefined categories with emojis. Data Storage: Google Sheets stores all expense records with automatic totals. Intelligent Responses: The agent provides spending summaries, alerts, and financial insights. Requirements: 🌐 Telegram Bot API access 🤖 OpenAI, Gemini, or any other AI model 🗣️ ElevenLabs API for voice processing 📝 Google Sheets API access 💹 Exchange rate API access Good to know: ⚠️ Daily spending alerts trigger when expenses exceed 100 USD. 🏷️ Supports 12 predefined expense categories with emoji indicators. 🔄 Automatic currency detection and conversion to USD. 🎤 Voice messages are processed through speech-to-text. 📸 Receipt images are analyzed using computer vision. Customizing this workflow: ✏️ Modify expense categories in the system prompt. 📈 Adjust spending alert thresholds. 💵 Change the base currency from USD to your preferred currency. ✅ Add additional expense validation rules. 🔗 Integrate with other financial platforms.
by Oneclick AI Squad
This AI-powered workflow automatically searches LinkedIn for relevant jobs, scores them using Claude AI based on your profile, sends personalized applications or connection requests, and logs everything to a Google Sheet for tracking. How it works Trigger - Runs on a schedule or via webhook to start a new job search Search LinkedIn - Fetches job listings based on keywords, location, and filters Filter & Deduplicate - Removes already-applied or seen jobs Analyze with Claude AI - Scores each job against your resume/profile Decision Gate - Only proceeds with jobs above your score threshold Apply or Connect - Sends Easy Apply or connection request to recruiter Log Results - Records all actions in Google Sheets for tracking Setup Steps Import this workflow into your n8n instance Configure credentials: LinkedIn OAuth2 - LinkedIn Developer Portal Anthropic API - For Claude AI job scoring Google Sheets - To track applications Update your profile/resume text in the Build Search Context node Set your job keywords and location preferences Activate the workflow Sample Trigger Payload { "keywords": "Product Manager", "location": "Bangalore, India", "experienceLevel": "mid-senior", "jobType": "full-time", "scoreThreshold": 70 } Features AI-powered job scoring** based on your skills and experience Duplicate prevention** - tracks seen and applied jobs Auto Easy Apply** for matching jobs Recruiter outreach** with personalized messages Full audit log** in Google Sheets Explore More LinkedIn & Social Automation: Contact us to design AI-powered lead nurturing, content engagement, and multi-platform reply workflows tailored to your growth strategy.
by Oneclick AI Squad
This n8n template demonstrates how to use AI to automatically review technical documentation against predefined compliance standards — and alert your team via Slack. Use cases: Engineering teams maintaining API docs, product teams enforcing terminology standards, or localization teams checking readiness before translation handoff. How it works A document URL or raw text is passed in via manual trigger (or webhook). The document content is fetched and extracted as plain text. An AI model scores the document across 4 compliance dimensions: Structure, Terminology, Localization Readiness, and Completeness. The AI returns a structured JSON report with scores and gap descriptions per dimension. A compliance status is determined: PASS / WARNING / FAIL based on the overall score. A formatted Slack alert is sent to the appropriate channel based on status severity. All results are logged for historical tracking. How to use Replace the document URL in the Set Document Input node with your own source. Update the AI prompt in the Score Documentation with AI node to match your standards. Add your Slack webhook URLs for each severity channel. Optionally connect to Google Sheets or PostgreSQL for audit logging. Requirements OpenAI or Anthropic API key for AI scoring Slack incoming webhook URLs Document accessible via URL or passed as text