@zalko/linkedin-parser

A clean, lightweight TypeScript library for parsing LinkedIn PDF resumes and extracting structured profile data.

Installation • Quick Start • API Reference • Examples

✨ Features

📦 Installation

npm install @zalko/linkedin-parser

yarn add @zalko/linkedin-parser

pnpm add @zalko/linkedin-parser

🚀 Quick Start

import { parseLinkedInPDF } from '@zalko/linkedin-parser';
import fs from 'fs';

// Parse from PDF Buffer
const pdfBuffer = fs.readFileSync('resume.pdf');
const result = await parseLinkedInPDF(pdfBuffer);

console.log(result.profile.name);          // "John Silva"
console.log(result.profile.contact.email); // "[email protected]"
console.log(result.profile.experience);    // [{ title: "...", company: "..." }]

📚 Examples

Basic Usage

import { parseLinkedInPDF } from '@zalko/linkedin-parser';

const pdfBuffer = fs.readFileSync('linkedin-resume.pdf');
const { profile } = await parseLinkedInPDF(pdfBuffer);

// Access parsed data
console.log(`Name: ${profile.name}`);
console.log(`Email: ${profile.contact.email}`);
console.log(`Skills: ${profile.top_skills.join(', ')}`);
console.log(`Experience: ${profile.experience.length} positions`);

With Options

// Include raw extracted text in result
const result = await parseLinkedInPDF(pdfBuffer, {
  includeRawText: true
});

console.log(`Raw text: ${result.rawText?.substring(0, 100)}...`);

Parse Text Directly

// If you already have extracted text from PDF
const extractedText = "John Silva\nSoftware Engineer...";
const result = await parseLinkedInPDF(extractedText);

Error Handling

try {
  const result = await parseLinkedInPDF(pdfBuffer);
  console.log(result.profile);
} catch (error) {
  if (error.message === 'PDF appears to be empty or unreadable') {
    console.error('Invalid PDF file');
  } else {
    console.error('Parsing failed:', error.message);
  }
}

📖 API Reference

parseLinkedInPDF(input, options?)

Parses a LinkedIn PDF resume and extracts structured profile data.

Parameters

| Parameter | Type | Description | |-----------|------|-------------| | input | Buffer \| string | PDF Buffer or extracted text string | | options? | ParseOptions | Optional parsing configuration |

Returns

Promise<ParseResult> - Promise resolving to parsed profile data

Example

const result = await parseLinkedInPDF(pdfBuffer, { includeRawText: true });

🏗️ TypeScript Interfaces

interface LinkedInProfile {
  name: string;
  headline: string;
  location: string;
  contact: Contact;
  top_skills: string[];
  languages: Language[];
  summary?: string;
  experience: Experience[];
  education: Education[];
}

interface Contact {
  email: string;
  phone?: string;
  linkedin_url?: string;
  location?: string;
}

interface Experience {
  title: string;
  company: string;
  duration: string;
  location?: string;
  description?: string;
}

interface Education {
  degree: string;
  institution: string;
  year?: string;
  location?: string;
  description?: string;
}

interface Language {
  language: string;
  proficiency: string;
}

interface ParseOptions {
  includeRawText?: boolean;
}

interface ParseResult {
  profile: LinkedInProfile;
  rawText?: string;
}

🛠️ Development

# Clone repository
git clone https://github.com/zalkowitsch/linkedin-parser.git
cd linkedin-parser

# Install dependencies
npm install

# Run tests
npm test

# Build library
npm run build

# Run tests with coverage
npm run test:coverage

# Clean build artifacts
npm run clean

📊 Performance

• Processing time: ~62ms average for typical LinkedIn PDF
• Memory usage: Minimal memory footprint (~6MB)
• Bundle size: Lightweight with only essential dependencies

🛡️ Quality & Trust

🌍 Compatibility

Supported Environments:

• ✅ Node.js 16+ (ES2022 support)
• ✅ TypeScript 5.0+
• ✅ ESM (ES Modules)
• ✅ CommonJS (via build)
• ✅ Browsers (via bundlers)

Package Managers:

• ✅ npm 8+
• ✅ yarn 1.22+
• ✅ pnpm 7+

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

📄 License

MIT © Arkady Zalkowitsch

⭐ Star this project if you find it helpful!

Made with ❤️ by Arkady Zalkowitsch