Aayushman Sharma
Templates by Aayushman Sharma
Save time hiring with AI: automate screening, assessments & interviews
AI Recruitment Automation Pipeline – Resume Parsing, GPT-4 Evaluation, Assessment Triggers & Interview Scheduling This end-to-end AI-powered recruitment automation workflow helps HR and talent acquisition teams automate the complete hiring pipeline—from resume intake and parsing to GPT-4-based evaluation, TA approvals, assessment delivery, and interview scheduling. Built using n8n, this template integrates with OpenAI GPT-4, Google Sheets, Google Drive, Slack, and SMTP to reduce time-to-hire, improve candidate quality, and eliminate repetitive manual tasks. The workflow enables scalable, consistent, and intelligent decision-making by automating resume evaluation, semantic fit analysis, and candidate communication. This template is ideal for recruiters, TA teams, and founders looking to optimize hiring for tech, sales, support, and other roles with high applicant volume. --- Who is this for? HR and TA teams handling high-volume recruitment Startups and SMBs looking to reduce hiring time and cost Hiring managers seeking to automate CV parsing and candidate evaluation --- What problem does this solve? Eliminates manual resume screening Sends real-time updates to TA team on assessment completion Automates assessments, scoring, and interview scheduling Keeps candidate communication consistent and timely --- What this workflow does Smart Resume Intake Form Collects candidate data: name, email, phone, LinkedIn, job role, and CV (PDF). Custom-designed UI with branding-ready CSS. PDF Resume Parsing & Storage CV is uploaded to a dedicated Google Drive folder. Resume text is extracted for semantic analysis. AI-Based Candidate Evaluation (GPT-4 via LangChain) Extracts: City, Education, Job History, Skills. Summarizes candidate profile (100 words). Retrieves and summarizes job description from Google Sheets. Performs detailed evaluation: ✅ Semantic fit scoring (0–100%) ✅ Key matches and skill gaps ✅ Soft skills extraction ✅ Red flag detection (job-hopping, missing info) ✅ Final score (1–10) with rationale Google Sheets Integration Logs and updates candidate data at each stage: CV Submitted → Scored → Shortlisted → Assessment Sent → Interview Scheduled → Rejected TA Approval via Email (Send & Wait) TA receives evaluation summary and gives one-click approve/reject. ✅ Approved → Status: Resume Selected ❌ Rejected → Status: Resume Rejected Assessment Trigger (Post Approval) Sends assessment link to shortlisted candidates. Notifies TA via Slack and Email when assessment is submitted. Interview Scheduling Sends Calendly link for self-scheduled interview booking. Candidate receives detailed next-step instructions. Status-Based Candidate Emails Automatically sends: ✔️ Shortlisting confirmation + interview setup ❌ Rejection email with branded message --- Business Benefits Save 80%+ time spent on manual resume reviews and coordination Reduce cost-per-hire by eliminating manual tasks Improve hiring accuracy with structured, AI-based decision-making Scalable recruitment for 100s of candidates per week Enhance candidate experience with instant status updates Centralize data in Google Sheets for full team visibility --- 🔧 Setup Instructions Google Service Account Setup (One-Time) Before using Google Sheets or Google Drive in n8n: Go to Google Cloud Console. Create a Service Account under your project. Enable these APIs: Google Sheets API Google Drive API Download the JSON credentials for the service account. IMPORTANT: Share your target Google Sheets and Docs with the service account email (e.g., your-service-account@your-project.iam.gserviceaccount.com). --- Add Applicant's Details to Google Sheet Document: Select the Profiles Google Sheet document. Sheet: Select the Applicant's Details sheet. Fields to Map: EMAIL: {{ $('On form submission').item.json.Email }} DATE: {{ $now.format('dd-MM-yyyy') }} NAME: {{ $('On form submission').item.json.Name }} LINKEDIN URL: {{ $('On form submission').item.json["LinkedIn Profile URL"] }} JOB PROFILE: {{ $('On form submission').item.json["Job Openings"] }} STATUS: CV SUBMITTED LAST UPDATED DATE: {{ $now.format('dd-MM-yyyy hh:mm:ss') }} --- Extract Applicant's Resume Text Text: {{ $('Extract from File').item.json.text }} --- Get Job Description from Google Sheet Document: Profiles Sheet: Job Openings Filter: Column: Job Profile Value: {{ $('On form submission').item.json["Job Openings"] }} --- Save Evaluation Results in Google Sheets Document: Profiles Sheet: Applicant's Details Column Match On: EMAIL Fields to Map: EMAIL: {{ $('On form submission').item.json.Email }} CITY: {{ $('Applicant\'s Details').item.json.output.City }} EDUCATIONAL: {{ $('Applicant\'s Details').item.json.output["Educational Qualification"] }} JOB HISTORY: {{ $('Applicant\'s Details').item.json.output["Job History"] }} SKILLS: {{ $('Applicant\'s Details').item.json.output.Skills }} SUMMARIZE: {{ $('Summarize Applicant\'s Profile').item.json.response.text }} SEMANTIC FIT SCORE: {{ $json.output.semantic_fit.score }} KEY MATCHES: {{ $json.output.semanticfit.keymatches.toJsonString() }} KEY GAPS: {{ $json.output.semanticfit.keygaps.toJsonString() }} SEMANTIC FIT CONSIDERATION: {{ $json.output.semantic_fit.consideration }} SOFT SKILLS: {{ $json.output.soft_skills.toJsonString() }} EXPERIENCE GAP DETECTED: {{ $json.output.experienceanalysis.experiencegap_detected }} OVER QUALIFICATION DETECTED: {{ $json.output.experienceanalysis.overqualificationdetected }} EXPERIENCE ANALYSIS CONSIDERATION: {{ $json.output.experience_analysis.consideration }} RED FLAGS ISSUES DETECTED: {{ $json.output.redflags.issuesdetected.toJsonString() }} RED FLAGS CONSIDERATION: {{ $json.output.red_flags.consideration }} VOTE: {{ $json.output.overallevaluation.finalvote }} FINAL CONSIDERATION: {{ $json.output.overall_evaluation.consideration }} STATUS: CV SCORED LAST UPDATED DATE: {{ $now.format('dd-MM-yyyy hh:mm:ss') }} --- Update Applicant Statuses Resume Selected Document: Profiles Sheet: Applicant's Details Column Match On: EMAIL Update: STATUS: RESUME SELECTED LAST UPDATED DATE: {{ $now.format('dd-MM-yyyy hh:mm:ss') }} Resume Rejected Update: STATUS: RESUME REJECTED LAST UPDATED DATE: {{ $now.format('dd-MM-yyyy hh:mm:ss') }} Assessment Sent Email: {{ $('Loop to Send Assessment Link to Each Candidate').item.json.EMAIL }} Update: STATUS: ASSESSMENT SENT LAST UPDATED DATE: {{ $now.format('dd-MM-yyyy hh:mm:ss') }} Assessment Submitted Email: {{ $json["Enter Your Email Address"] }} Update: STATUS: ASSESSMENT SUBMITTED LAST UPDATED DATE: {{ $now.format('dd-MM-yyyy hh:mm:ss') }} Interview Booked Email: {{ $json.payload.email }} Update: STATUS: INTERVIEW BOOKED LAST UPDATED DATE: {{ $now.format('dd-MM-yyyy hh:mm:ss') }} --- Fetch Applicants with Specific Status Status: RESUME SELECTED Document: Profiles Sheet: Applicant's Details Filter: Column: STATUS Value: RESUME SELECTED --- Get Assessment Form URL from Job Profile Document: Profiles Sheet: Job Openings Filter: Column: Job Profile Value: {{ $json["JOB PROFILE"] }} --- Trigger on Applicant Status Update Document: Profiles Sheet: Applicant's Details Trigger Settings: Columns to Watch: STATUS --- ⚠️ Important Notes Always use “Select Document from List” instead of manually pasting the sheet/document ID. Share your Sheets/Docs with the Google Service Account email for proper access. Keep your date formats consistent using {{ $now.format('dd-MM-yyyy hh:mm:ss') }}. --- Add credentials for: Google Drive Google Sheets SMTP (for emails) OpenAI API Key (GPT-4) Replace placeholders: Google Sheet & Folder IDs Calendly Link Assessment Link (Optional) Customize GPT-4 prompts for domain-specific scoring (Optional) Use your Slack webhook for TA notifications --- 🛠️ Tools & Integrations Form Trigger – Candidate form with file upload Google Drive + Extract PDF – CV parsing Google Sheets – Database for all applicant statuses LangChain GPT-4 Nodes – AI profile + job analysis Email Send & Send & Wait – Candidate/TA communication IF Node – Logic for approve/reject Slack Integration – TA notification Calendly Link – Interview scheduling --- AI resume screening, GPT-4 recruitment workflow, automated hiring pipeline, semantic fit evaluation, LangChain for HR, resume parsing automation, AI in talent acquisition, assessment workflow automation, interview scheduling automation, candidate shortlisting automation, OpenAI HR integration, Google Sheets recruitment tracker, n8n HR automation template, self-scheduling interviews with Calendly, Slack notifications in recruitment
By Aayushman SharmaAutomatically create Google Tasks from Gmail labeled emails
Automatically create Google Tasks from new Gmail emails labeled "To-Do". Who is this for? This template is perfect for individuals and teams who want to boost their productivity by automatically converting important emails into actionable tasks in Google Tasks. What problem is this workflow solving? Manually managing emails and creating tasks can be tedious. This workflow ensures you never miss a follow-up by instantly turning important emails into tasks without switching between apps. What this workflow does? Watches for new emails in Gmail with the label "To-Do". Creates a new Google Task with the email subject as the task title and the email snippet as notes. Sets the task due date to 24 hours after the email is received. Setup Create a label "To-Do" in your Gmail account if it doesn't already exist. Connect your Gmail and Google Tasks accounts to n8n using OAuth2 credentials. Import the workflow into n8n and activate it. How to customize this workflow to your needs? Change the Gmail label to a different one (e.g., "Important", "Follow-up"). Modify the due date logic in the expression if you want more/less time to complete tasks: {{ $now.add(2, 'days').toISOString() }} Add additional Gmail filters (like only unread emails) to refine which emails create tasks.
By Aayushman Sharma🚀 YouTube comment sentiment analyzer with Google Sheets & OpenAI
🚀 YouTube Comment Sentiment Analyzer with Google Sheets & OpenAI --- Who Should Use This? Influencers, marketers, and data teams who need instant insights into audience sentiment—without manual exports or scattered tools. --- The Challenge Manual exports from YouTube Studio Time-consuming sentiment tagging Data scattered across multiple platforms Our workflow automates everything: from fetching comments to logging analysis—so you can focus on insights, not spreadsheets. --- What You’ll Get Dynamic Input Read a list of YouTube URLs from your Google Sheet. Full Comment Harvest Pull all top-level comments (handles pagination 100/page). Deep Sentiment Scan Classify each comment as Positive, Neutral, or Negative using OpenAI. Smart Formatting Capture metadata (author, likes, timestamp) alongside sentiment. Seamless Storage Append or update rows in your Google Sheet—ready for reporting. --- Easy Setup Prepare Google Sheet Create a sheet with a video_urls column (full YouTube links). Add and authorize a Google Sheets Oauth or service-account credential in n8n. Enable YouTube API Activate Data API v3 in Google Cloud, grab an API key, and save as an HTTP credential in n8n. Configure OpenAI Enter your API key under the “OpenAI Chat” credential in n8n. Import the Workflow Paste the provided JSON into n8n. Run Manually Use the Manual Trigger node to start fetching and analyzing comments on demand. --- Customize to Your Needs Filter Comments: Add an IF node to process only comments with specific keywords or minimum likes. Automate Schedule: Swap the Manual Trigger for a Cron node if you later want periodic runs. Extended Analysis: Swap sentiment classification for topic extraction, summarization, or translation by tweaking the LLM prompt. Alternate Destinations: Replace Google Sheets with Airtable, Notion, or any database node. --- Tags YouTube Google Sheets OpenAI Sentiment Analysis n8n Manual Trigger
By Aayushman SharmaSmart knowledge base builder — auto-convert websites into AI training data
AI-Powered Knowledge Base Builder — Turn Any Website into LLM-Optimized Markdown & TXT Files Automate the entire process of converting any website or domain into clean, structured, AI-ready knowledge bases for Large Language Models (LLMs), semantic search, and chatbot development. --- Key Workflow Highlights URL Input via Simple Form – Paste a single link or a full domain. Automated Link Discovery – Crawl and map all related pages with Firecrawl API. Clean Markdown Extraction – Use Parsera API for accurate, clutter-free content. LLM-Optimized Formatting – Standardize with OpenAI GPT-4.1-mini for llms.txt. Cloud Storage Integration – Save directly to Google Drive for instant access. Batch Processing at Scale – Handle single pages or hundreds of URLs effortlessly. --- Perfect For: AI engineers building domain-specific training datasets Data scientists running semantic search & vector database pipelines Researchers collecting website archives for AI or analytics Automation specialists creating chatbot-ready content libraries --- Why This Workflow Outperforms Manual Processes 100% Automated — From link input to Google Drive-ready .txt file Flexible Scope — Choose between single-page extraction or full-site crawling Clean, AI-Friendly Output — Markdown converted to standardized LLM format Scalable & Reliable — Handles bulk data ingestion without formatting issues Cloud-First — Centralized storage for team-wide accessibility --- Problems Solved No more manual copy-paste from dozens of web pages Eliminate formatting inconsistencies across datasets Avoid scattered files — all output stored in one central folder Instead, you get: Automated URL mapping for deep data coverage Proxy-enabled scraping for accurate extraction Ready-to-use llms.txt files for chatbots, fine-tuning, and AI pipelines --- How It Works — Step-by-Step Form Submission Input your URL and choose “Single Page” or “Full Domain Crawl.” URL Mapping with Firecrawl API Automatically discovers all internal links related to the starting URL. Content Extraction with Parsera API Removes ads, navigation clutter, and irrelevant elements to produce clean Markdown. LLM-Optimized Formatting with OpenAI GPT-4.1-mini Generates structured files including: Site title & meta description Page sections with summaries & full text Cloud Upload to Google Drive Final .txt or .md files stored in your specified folder. --- Business & AI Advantages Save 90%+ time preparing AI training datasets Improve AI accuracy with high-quality, consistent input Maintain centralized, cloud-based storage Scale globally with proxy-based content collection --- Setup in Under 10 Minutes Import the workflow into n8n. Add credentials for: Firecrawl API Parsera API OpenAI API Key Google Drive (Service Account or OAuth) Update your Google Drive folder ID. Run a test job with a sample URL. Deploy and connect to your AI pipeline. --- Tools & Integrations Used n8n Form Trigger – For user-friendly input Firecrawl API – Comprehensive internal link mapping Parsera API – Clean, structured content extraction OpenAI GPT-4.1-mini – LLM-optimized formatting Google Drive API – Secure cloud storage Batch & Switch Logic – Efficient multi-page processing --- Advanced Customization Options Change output format: .md, .json, .csv Swap storage to Dropbox, AWS S3, Notion, Airtable Modify AI prompts for alternative formatting Filter by keywords or metadata before saving Automate runs via Google Sheets, email triggers, or cron schedules Add AI-powered translation for multilingual datasets Enrich with SEO metadata or author information Push directly to vector databases like Pinecone, Weaviate, Qdrant --- SEO-Optimized Keywords for Maximum Reach AI data extraction workflow Automated LLM training dataset builder Web to Markdown converter for AI Firecrawl Parsera OpenAI n8n integration llms.txt file generator for chatbots Automated website content scraper for AI Knowledge base creation automation AI-ready data pipeline for semantic search Batch website-to-dataset conversion
By Aayushman Sharma