How to vibe codeJobscan

Prerequisites

• Node.jsfree

  Required for running the Next.js frontend and build tooling.

• GitHubfree

  Source control and deployment pipeline integration.

• Anthropic API keypay-as-you-go

  Powers resume tailoring, keyword extraction, and cover letter generation.

AI coding tools

• Claude Code

  Best-in-class agentic coding tool for scaffolding full-stack features and writing robust document parsing logic.

• Cursor

  Ideal editor for iterative UI polish on the dashboard and resume editor views.

Build guide

1. 01Project Scaffolding and Database Schema

   Initialize the Next.js application with Tailwind CSS, shadcn/ui, better-auth, and Turso database connection.

   Create a new Next.js project configured with TypeScript, Tailwind CSS, and App Router. Set up better-auth for local email/password authentication backed by a Turso SQLite database using Drizzle ORM. Design database tables for users, resumes (storing raw text and structured JSON fields), target job descriptions, match reports, and job application tracking pipelines. Create a clean dashboard layout with a sidebar navigation for Resume Scanner, AI Optimizer, Cover Letter Generator, and Job Tracker. Ensure all configuration files, environment variables, and initial migration scripts are fully written and verified.

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2. 02Document Parsing Engine (.docx and PDF)

   Implement file upload and text extraction endpoints supporting both PDF and Microsoft Word formats.

   Build a robust document parsing module in Next.js (or a lightweight Python helper service if needed) that accepts PDF and .docx file uploads. For .docx files, use standard parsing libraries to extract clean text blocks. For PDFs, implement text-layer extraction and a fallback OCR text-reading mechanism to handle unstructured resume layouts safely. Store the extracted plain text alongside metadata such as word count, detected section headings (Experience, Education, Skills), and formatting flags. Create a React UI component allowing users to drag and drop their resume file, display a live parsing preview, and highlight potential formatting errors like tables or headers.

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3. 03Keyword Extraction and Match Rate Engine

   Build the core matching engine that compares resume text against job descriptions to calculate match percentages and surface missing skills.

   Implement the core job matching engine. Build an API endpoint that takes parsed resume text and a pasted target job description, sending them to the Anthropic API using structured JSON output mode via the Vercel AI SDK. Configure the prompt to extract hard skills, soft skills, educational requirements, and key job titles from the job description, and cross-reference them against the resume text. Calculate an overall match rate score based on hard skills and keyword overlap. Return categorized lists of matched keywords, missing keywords, and specific ATS compatibility recommendations. Render this data in an interactive Match Report dashboard with clear score meters and actionable checklist items.

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4. 04AI Resume Optimizer and Bullet Point Generator

   Add generative AI tools to rewrite resume bullet points and tailor summaries to match target job listings.

   Build an AI Resume Optimizer module. Create an interface where users select weak resume bullet points or request a tailored resume summary. Implement server actions connecting to the Anthropic API that rewrite bullets to be achievement-driven, metric-focused, and densely packed with missing keywords identified in Step 3, while preventing keyword-stuffing over-optimization. Provide a side-by-side diff viewer so users can review, accept, or reject individual AI-suggested rewrites before updating their stored master resume.

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5. 05Cover Letter Generator and LinkedIn Auditor

   Implement automated cover letter creation and a LinkedIn profile optimization scanner.

   Add a Cover Letter Generator and LinkedIn Optimization tool. For the cover letter, build a form taking company name, role title, resume text, and job description, then call the LLM to generate a tailored, professional cover letter with customizable tones. For the LinkedIn optimizer, create an input field where users paste their exported LinkedIn profile text or headline/summary sections. Run an automated critique against best practices for recruiter keyword search visibility, returning a score and specific recommendations for headlines and experience descriptions.

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6. 06Job Application Tracker and Polish

   Build a Kanban-style job application tracker and finalize overall dashboard polish.

   Build a Job Application Tracker dashboard with a Kanban board layout (Columns: Wishlist, Applied, Interviewing, Offer, Rejected). Allow users to manually add applications or link them directly from a saved resume match report, tracking company name, role title, salary, application date, and notes. Add status filtering, sorting, and clean empty states. Perform a comprehensive UI polish pass across all views to ensure responsive design, loading skeletons, error handling, and smooth toast notifications throughout the application.

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Cost vs paying for Jobscan