Yashraj singh sisodiya
Verified n8n Creator and DevOps/Cloud enthusiast. I build scalable, API-driven automations with a focus on clean architecture, modular design, and documentation. From AI news pipelines to CI/CD workflows, my projects turn complex processes into simple, reusable solutions that help teams work smarter and faster.
Templates by Yashraj singh sisodiya
Generate Job-Specific ATS Resumes with Perplexity AI and PDF Export
This workflow contains community nodes that are only compatible with the self-hosted version of n8n. ATS Resume Maker Workflow Explanation Aim The aim of the ATS Resume Maker according to JD workflow is to automate the creation of an ATS-friendly resume by tailoring a candidate’s resume to a specific job description (JD). It streamlines the process of aligning resume content with JD requirements, producing a professional, scannable PDF resume that can be stored in Google Drive. Goal The goal is to: Allow users to input their resume (text or PDF) and a JD (PDF) via a web form. Extract and merge the text from both inputs. Use AI to customize the resume, prioritizing JD keywords while maintaining the candidate’s truthful information. Generate a clean, ATS-optimized HTML resume and convert it to a downloadable PDF. Upload the final PDF to Google Drive for easy access. This ensures the resume is optimized for Applicant Tracking Systems (ATS), which are used by employers to screen resumes, by incorporating relevant keywords and maintaining a simple, scannable format. Requirements The workflow relies on specific components and configurations: n8n Platform: The automation tool hosting the workflow. Node Requirements: Form Trigger: A web form to collect user inputs (resume text/PDF, JD PDF). Process one binary file1: JavaScript to rename and organize PDF inputs. Extracting resume1: Extracts text from PDF files. Merge Resume + JD1: Combines resume and JD text into a single string. Customize resume1: Uses Perplexity AI to generate an ATS-friendly HTML resume. HTML format1: Cleans the HTML output by removing newlines. HTML3: Processes HTML for potential display or validation. HTML to PDF: Converts the HTML resume to a PDF file. Upload file: Saves the PDF to a specified Google Drive folder. Credentials: CustomJS account for the HTML-to-PDF conversion API. Google Drive account for file uploads. Perplexity account for AI-driven resume customization. Input Requirements: Resume (plain text or PDF). Job description (PDF). Output: A tailored, ATS-friendly resume in PDF format, uploaded to Google Drive. API Usage The workflow integrates multiple APIs to achieve its functionality: Perplexity API: Used in the Customize resume1 node to leverage the sonar-reasoning model for generating an ATS-optimized HTML resume. The API processes the merged resume and JD text, aligning content with JD keywords while adhering to strict HTML and CSS guidelines (e.g., Arial font, no colors, single-column layout). [Ref: Workflow JSON] CustomJS API: Used in the HTML to PDF node to convert the cleaned HTML resume into a PDF file. This API ensures the resume is transformed into a downloadable format suitable for ATS systems. [Ref: Workflow JSON] Google Drive API: Used in the Upload file node to store the final PDF in a designated Google Drive folder (Resume folder in My Drive). This API handles secure file uploads using OAuth2 authentication. [Ref: Workflow JSON] These APIs are critical for AI-driven customization, PDF generation, and cloud storage, ensuring a seamless end-to-end process. HTML to PDF Conversion The HTML-to-PDF conversion is a key step in the workflow, handled by the HTML to PDF node: Process: The node takes the cleaned HTML resume ($json.cleanedResponse) from the HTML format1 node and uses the @custom-js/n8n-nodes-pdf-toolkit.html2Pdf node to convert it into a PDF. API: Relies on the CustomJS API for high-fidelity conversion, ensuring the PDF retains the ATS-friendly structure (e.g., no graphics, clear text hierarchy). Output: A binary PDF file passed to the Upload file node. Relevance: This step ensures the resume is in a widely accessible format, suitable for downloading or sharing with employers. The use of a dedicated API aligns with industry practices for HTML-to-PDF conversion, as seen in services like PDFmyURL or PDFCrowd, which offer similar REST API capabilities for converting HTML to PDF with customizable layouts. [Ref:,][](https://pdfmyurl.com/)[](https://pdfcrowd.com/api/html-to-pdf-api/) Download from Community Link The workflow does not explicitly include a community link for downloading the final PDF, but the Upload file node stores the PDF in Google Drive, making it accessible via a shared folder or link. To enable direct downloads: Workflow Summary The ATS Resume Maker according to JD workflow automates the creation of a tailored, ATS-friendly resume by: Collecting user inputs via a web form (Form Trigger). Processing and extracting text from PDFs (Process one binary file1, Extracting resume1). Merging and customizing the content using Perplexity AI (Merge Resume + JD1, Customize resume1). Formatting and converting the resume to PDF (HTML format1, HTML3, HTML to PDF). Uploading the PDF to Google Drive (Upload file). The workflow leverages APIs for AI processing, PDF conversion, and cloud storage, ensuring a professional output optimized for ATS systems. Community sharing can be enabled via Google Drive links or external platforms, as discussed in related web resources. [Ref:,,][](https://pdfmyurl.com/)[](https://www.servicenow.com/community/servicenow-ai-platform-forum/allow-user-to-download-a-pdf-file-on-click-of-a-button/td-p/1082457) Timestamp: 02:54 PM IST, Wednesday, August 20, 2025
By Yashraj singh sisodiyaYouTube video summarizer with Gemini AI to WhatsApp, Telegram & Google Sheets
Summarize YouTube Videos with Gemini AI, Google Sheets & WhatsApp/Telegram Aim The aim of the YouTube Video Summarizer Workflow is to automate the process of summarizing or extracting transcripts from YouTube videos with the help of Gemini AI, while optionally storing results and distributing them to users via WhatsApp, Telegram, or Google Sheets. This enables fast, consistent generation and sharing of English summaries or transcripts from public YouTube content. Goal The goal is to: Allow users to submit a YouTube link through various channels (Form Webhook, WhatsApp, Telegram). Use Gemini AI to either summarize the content or transcribe the complete video, always outputting in English. Return the output to the user via their original channel and optionally log it to Google Sheets for record-keeping. Requirements The workflow relies on specific integrations and configurations: n8n Platform: Self-hosted or cloud n8n instance to host and automate the workflow. Node Requirements: Form/Webhook Trigger: Web form for pasting the YouTube link. WhatsApp Trigger: Starts workflow from incoming WhatsApp messages (YouTube link as input). Telegram Trigger: Initiates workflow from Telegram chat messages containing YouTube links. Gemini AI Node: Consumes the YouTube link and processes it for summarization or transcription (always in English). Google Sheets Node: Writes the result (summary/transcript) into a Google Sheet for logging and future reference. WhatsApp/Telgram Send Message Nodes: Delivers summarized results or transcripts back to the user on the same platform where they triggered the workflow. Credentials: Gemini/Google AI Platform account for AI summarization and transcription. Google Sheets account for storing output. WhatsApp Business API for WhatsApp automation. Telegram Bot API for Telegram automation. Input Requirements: Publicly-accessible YouTube video link (max ~30 min, as per summarized logic). Output: English video summary or full transcript, delivered via user’s requested channel and/or stored in Google Sheets. API Usage The workflow integrates several APIs for optimal automation: Gemini AI API: Used in the main summarization node. Receives the YouTube link and a prompt with detailed instructions. Returns either a clear, concise English summary or a full transcript translated into English, handling Hindi, English, or mixed-language videos. [Ref: Workflow JSON] Google Sheets API: Used to log the output for each processed video, making it easy to reference histories or track requests. [Ref: Workflow JSON] WhatsApp Business API: Sends back the summary or transcript to the user who initiated via WhatsApp. [Ref: Workflow JSON] Telegram Bot API: Sends results back to Telegram users directly in chat. [Ref: Workflow JSON] Output Formatting/Conversion The AI output is always in English, tailored to the option chosen (summary vs transcript). Structured output: Bulleted, neutral, and easy to read, suitable for sharing with users or for business documentation. Google Sheets node maps and writes each video’s results to a dedicated row for easy history review. How to Use By default, the workflow uses a manual trigger via a web form, but you may add triggers for WhatsApp or Telegram to suit your needs. Users paste a YouTube link, then select whether they want a summary or transcript (based on your implementation logic). Results are returned in their channel and optionally logged to your Google Sheet. All processing is handled securely using your Gemini API credentials. You can expand this logic by adding more integrations (email, Slack, etc.). Customising this Workflow Custom prompts can be written for different styles or output formats (e.g., SEO key points, step-by-step guides). Add logic for batch processing multiple videos or bulk export to different cloud drives. Integrate into central dashboards, CRMs, or content pipelines using n8n’s hundreds of available integrations. Good to Know Gemini pricing: At the time of writing, each YouTube video summarization costs $0.039 USD. See official Gemini Pricing for current rates. Geo-restriction: The Gemini video model may be geo-restricted (error: “model not found” outside some regions). Video Limits: Intended for videos up to ~30 minutes for best processing reliability. Scaling: Can be easily adapted for high-volume operations using n8n’s queue and scheduling features. Workflow Summary The YouTube Video Summarizer Workflow automates summarizing and transcribing YouTube videos using AI and n8n. Users send video links via web forms, WhatsApp, or Telegram. Results are generated via Gemini, sent back in-app, and logged to Google Sheets, enabling effortless knowledge sharing and organizational automation at scale. Timestamp: 12:37 PM IST, Wednesday, September 17, 2025
By Yashraj singh sisodiyaDaily AI news digest with Perplexity AI and Gmail automation
AI Latest 24 Update Workflow Explanation Aim The aim of the AI Latest 24 Update Workflow is to automate the daily collection and distribution of the most recent Artificial Intelligence and Technology news from the past 24 hours. It ensures users receive a clean, well-structured HTML email containing headlines, summaries, and links to trusted sources, styled professionally for easy reading. --- Goal The goal is to: Automate news retrieval by fetching the latest AI developments from trusted sources daily. Generate structured HTML output with bold headlines, concise summaries, and clickable links. Format the content professionally with inline CSS, ensuring the email is visually appealing. Distribute updates automatically to selected recipients via Gmail. Provide reusability so the HTML can be processed for other platforms if needed. This ensures recipients receive accurate, up-to-date, and well-formatted AI news without manual effort. --- Requirements The workflow relies on the following components and configurations: n8n Platform Acts as the automation environment for scheduling, fetching, formatting, and delivering AI news updates. Node Requirements Schedule Trigger Runs the workflow every day at 10:00 AM. Automates the process without manual initiation. Message a model (Perplexity API) Uses the sonar-pro model from Perplexity AI. Fetches the most recent AI developments in the past 24 hours. Outputs results as a self-contained HTML email with inline CSS (card-style layout). Send a message (Gmail) Sends the generated HTML email with the subject: “Latest Tech and AI news update 🚀”*. Recipients: xyz@gmail.com. HTML Node Processes the AI model’s HTML response. Ensures the email formatting is clean, valid, and ready for delivery. Credentials Perplexity API account: For fetching AI news. Gmail OAuth2 account: For secure email delivery. Input Requirements No manual input required; the workflow runs automatically on schedule. Output A daily AI news digest email containing: Headlines in bold. One-sentence summaries in normal text. Full URLs as clickable links. Styled in a clean card-based format with hover effects. --- API Usage The workflow integrates APIs to achieve automation: Perplexity API Used in the Message a model node. The API fetches the latest AI news, ensures data accuracy, and outputs HTML formatted content. Provides styling via inline CSS (Segoe UI font, light background, card design). Ensures the news is fresh (past 24 hours only). Gmail API Used in the Send a message node. Handles the secure delivery of emails with OAuth2 authentication. Sends AI news updates directly to inboxes. --- HTML Processing The HTML Node ensures proper formatting before email delivery: Process: Cleans and validates HTML ($json.message) generated by Perplexity. Output: A self-contained HTML email with proper structure (<html>, <head>, <body>). Relevance: Ensures Gmail sends a styled digest instead of raw text. --- Workflow Summary The AI Latest 24 Update Workflow automates daily AI news collection and delivery by: Triggering at 10:00 AM using the Schedule Trigger node. Fetching AI news via Perplexity API (Message a model node). Formatting results into clean HTML (HTML node). Sending the email via Gmail API to multiple recipients. This workflow ensures a seamless, hands-off system where recipients get accurate, fresh, and well-designed AI news updates every day. ---
By Yashraj singh sisodiyaExtract & process invoices with Gemini AI, Google Sheets & Gmail notifications
AI-Powered Invoice Extraction & Automation System Workflow Aim The aim of the Invoice Data Automation workflow is to streamline invoice processing by extracting, validating, and storing invoice details automatically. It uses AI to read invoices (from images/PDFs), structures the data in JSON, checks for missing or duplicate entries, and records the information in a Google Sheet while also uploading the original invoice to Google Drive. Additionally, it sends email notifications for successful entries, duplicates, or missing fields. Goal The goal is to: Allow users to upload invoice images/files via chat. Extract invoice data using AI and format it into a structured JSON. Validate mandatory fields like invoiceid, shopname, date, Total, and items. Check if the invoice already exists in the Google Sheet. Append new invoices to Google Sheets and upload the original file to Google Drive. Notify the user via email about success, duplicates, or errors. This ensures that invoices are processed accurately, efficiently, and securely with minimal manual effort. Requirements The workflow relies on specific components and configurations: n8n Platform The automation is built and hosted on n8n, which orchestrates the entire workflow. Node Requirements When chat message received – Triggers the workflow when a user submits an invoice. Analyze image1 – Uses Google Gemini Vision AI to extract invoice data from the uploaded file. AI Agent1 + Google Gemini Chat Model1 – Restructures the extracted data into strict JSON format. Make data in JSON structure format – Prepares and organizes the structured output. Get data – Looks up the Google Sheet to check if the invoice already exists. check if Data exist or not in table – Decides whether to process as new data or duplicate. New data add using payload / Check Mandatory fields – Ensures invoice JSON is valid and complete. If – check missing field – Branches based on whether mandatory fields are present. no missing field – new data add using payload 2 – Passes only verified invoices forward. Append data to sheet – Saves invoice data in Google Sheets. Upload invoice to drive – Uploads the original invoice file to Google Drive. Send successful email – Notifies the user of successful entry. Duplicate entry send mail – Alerts the user if the invoice already exists. Send missing field error on mail – Sends an error email if mandatory fields are missing. Credentials Google Gemini (PaLM) API – For AI-based invoice data extraction and formatting. Google Sheets API – For storing structured invoice records. Google Drive API – For uploading original invoices. Gmail API – For sending success, duplicate, and error notifications. Input Requirements Invoice file (Image or PDF). Output Structured invoice data in Google Sheets. Original invoice stored in Google Drive. Notifications sent via Gmail (success, duplicate, or error). API Usage Google Gemini (PaLM API): Used in Analyze image1 and AI Agent1 to extract and structure invoice data. It ensures fields like invoiceid, shopname, date, and items are clearly formatted. Google Sheets API: Used in Get data and Append data to sheet nodes to validate duplicates and store new invoices. Google Drive API: Used in Upload invoice to drive to securely store the original uploaded invoice file. Gmail API: Used in Send successful email, Duplicate entry send mail, Send missing field error on mail to notify users about the status of invoice processing. Workflow Summary The Invoice Data Automation workflow automates the entire process of invoice handling by: Receiving invoice uploads via chat. Extracting invoice details with AI (Google Gemini). Structuring and validating the data in JSON format. Checking Google Sheets for duplicates. Appending new invoices to the sheet and uploading originals to Google Drive. Sending email notifications to keep the user updated on the process outcome. This end-to-end automation ensures invoices are handled with speed, accuracy, and transparency, reducing manual work and eliminating common errors in invoice management.
By Yashraj singh sisodiyaAnalyze bakery sales & inventory with Google Sheets & Azure GPT Chat Assistant
Bakery Data Analytics Workflow Explanation Aim The aim of the Bakery Data Analytics Workflow is to automate the process of analyzing bakery sales and stock data stored in Google Sheets. It allows bakery owners or managers to interact with an AI assistant via chat and receive clear, concise, and actionable insights about their business performance without manually reviewing spreadsheets. --- Goal The goal is to: Enable users to query bakery sales and stock data through a chat interface. Use an AI Agent to interpret user queries and fetch the required data. Retrieve relevant sales/stock figures from a Google Sheets dataset. Generate insights in plain English, with short summaries, highlights, or breakdowns. Maintain conversation context so users can ask follow-up questions naturally. This ensures that bakery owners can make quick, informed decisions about sales trends, inventory shortages, or product performance with minimal manual effort. --- Requirements The workflow relies on the following components and configurations: n8n Platform The automation platform hosting the workflow. Node Requirements When chat message received (Trigger) Captures user input via chat. Initiates the workflow execution. AI Agent Central reasoning engine. Interprets queries, decides when to fetch data, and ensures professional responses. Uses short, structured insights (bullets, tables, or compact summaries). Simple Memory Stores short-term conversation history. Maintains context across multiple user queries. Retrieve bakery data (Google Sheets) Connects to a linked Google Sheets file. Fetches sales/stock data (e.g., daily totals, item performance). Data source: Bakery Google Sheet. Azure OpenAI Chat Model Backend language model powering the AI Agent. Provides natural language understanding and generates concise responses. Credentials Google Sheets OAuth2 account (for accessing bakery data). Azure OpenAI API account (for AI-driven reasoning and conversation). Input Requirements User question/query via chat (e.g., “What was the best-selling pastry last week?”*). Output Compact, conversational insights (totals, highlights, trends) delivered via chat. --- API Usage The workflow integrates two main APIs: Google Sheets API Used by the Retrieve bakery data node. Fetches structured data (sales, stock, dates) from the bakery dataset. Provides the AI Agent with real-time data access. Azure OpenAI API Used by the Azure OpenAI Chat Model node. Powers natural conversation, ensures responses are plain English, concise, and business-focused. Aligns with AI Agent rules to avoid assumptions and provide actionable insights only when asked. --- Workflow Summary The Bakery Data Analytics Workflow automates bakery performance analysis by: Triggering on chat message input. Passing the query to the AI Agent for interpretation. Using Simple Memory to track context across the conversation. Fetching relevant data from Google Sheets when needed. Leveraging Azure OpenAI to generate structured, professional responses. This creates an interactive AI-powered assistant for bakery data, enabling quick insights into sales and inventory trends without manually combing through spreadsheets. ---
By Yashraj singh sisodiya