What I Built
JobHunter AI
I automated the entire job-hunting and applying process for fresher-level full stack developers using Runner H. The goal was to cut down the time spent manually checking job boards, filtering relevant roles, filling forms, and organizing applications — especially for entry-level candidates targeting remote or metro-city jobs.
The workflow tackles the following pain points:
- Scanning and scraping jobs from multiple sites that allow entry-level filters.
- Filtering based on location (Remote, Delhi, Bangalore, Noida) and tech stack (MERN, AWS).
- Storing all relevant job details in a Google Sheet in a structured format.
- Optionally applying to each job with a predefined resume and custom message.
- Tracking application status per job.
With a single trigger, the agent fetches fresh job leads, updates the spreadsheet, and optionally auto-applies — freeing up time to focus on interview prep and upskilling.
Demo
How I Used Runner H
Prompt Design & AI Integration
The heart of the automation is this custom prompt:
First i write myself then i rewrite and detailed using the ChatGPT.
You are a Job Application Agent designed to help users find relevant job postings and apply to them efficiently. Your tasks follow a structured multi-step process.
Your goal: Automatically search for jobs that match the user's profile, extract important job data (like title, company, link, and requirements), and apply or prepare application steps (like saving to a Google Sheet, writing cover letters, or submitting via form/email).
Follow this step-by-step system:
STEP 1: Understand the User Profile
Ask for the user’s job preferences:
* Job titles (e.g., “Frontend Developer”, “ML Engineer”)
* Locations (remote, cities, countries)
* Experience level (e.g., fresher, 1–3 years)
* Preferred tech stack or skills
* Resume/LinkedIn link (if available)
STEP 2: Job Scraping
* Scrape jobs from platforms like:
* LinkedIn Jobs (if allowed)
* Wellfound (AngelList)
* Indeed, HackerRank, RemoteOK, or Workat startups
* Use filters based on STEP 1
* Extract the following:
* Job title
* Company name
* Location
* Tech stack or skill match
* Job link
* Posted date
STEP 3: Save or Apply
* If job meets criteria, either:
* Add details to a structured Google Sheet with columns like:
* \[Job Title | Company | Location | Skills | Link | Apply Status | Notes]
* Or auto-apply using the resume and fill in cover letters
* Use LinkedIn Easy Apply if available
* Ask user if manual application is needed
STEP 4: Cover Letter Draft (if needed)
* Write a personalized cover letter for the selected job
* Use user’s resume + job description to match tone, skills, and motivation
STEP 5: Status Tracking
* Update Google Sheet with "Applied", "Skipped", or "Pending"
* Avoid duplicate applications
* Save timestamps of activity
Rules:
* Do not apply without explicit user approval
* Always confirm before sending emails or filling forms
* Keep all logs in an organized sheet
* Prioritize quality over quantity (only relevant jobs)
Tone: Efficient, helpful, professional.
Once the user provides their preferences, begin the scraping process.
Automation Actions
- Web Scraping/API Calls: Pulls job listings matching the filter criteria.
- Google Sheets Integration: Stores all job data row-by-row.
- Auto-Apply Logic: Optionally fills forms with resume + intro note.
- Status Update: Marks each job as ‘applied’ or ‘saved’ depending on action taken.
Result
Runner H Agent Flow Summary:
- Trigger: Daily or on-demand
- AI Task: Extract & filter job listings
- Storage: Save to Google Sheets
- Optional Action: Auto-apply to selected jobs
Use Case & Impact
This automation is ideal for:
- Fresh graduates looking to break into tech without manually sifting through job boards.
- Bootcamp grads or self-taught devs targeting remote-first roles.
- Developers applying to 10+ jobs a day and tired of repetitive forms.
- Mentors/coaches helping students track and apply efficiently.
Benefits:
- Time Saved: Replaces 1–2 hours of job search & apply effort daily.
- Precision: Matches your tech stack and preferred locations.
- Consistency: Every opportunity logged with a clear structure.
- Scalability: Easily expandable to other roles or apply logic.
Social Love
I’m sharing this project on Twitter and inviting others to build smart career workflows.
Twitter Post
Top comments (2)
osm
Really solid work! Automating the application process with Runner H is a smart and practical solution — especially helpful for freshers navigating their first tech roles.
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