TopSourcer – Natural Language-Based LinkedIn Resume Search Assistant
This workflow combines natural language processing and automation technology to help recruiters quickly generate precise LinkedIn resume search queries. After the user inputs a job description, the system automatically creates a Boolean search string and performs a Google search to extract relevant LinkedIn profile links, structuring and saving candidate information into Google Sheets. Through automated scraping and management, it significantly enhances recruitment efficiency, reduces the complexity of manual operations, and provides a convenient candidate screening solution for the recruitment team.

Workflow Name
TopSourcer – Natural Language-Based LinkedIn Resume Search Assistant
Key Features and Highlights
This workflow leverages the OpenAI GPT-4 model to automatically convert user-inputted job descriptions or ideal candidate profiles into precise Google Boolean search queries specifically designed for locating LinkedIn personal profiles. By simulating Google searches and parsing the results, it automatically extracts LinkedIn resume URLs and systematically saves detailed candidate information into Google Sheets for easy subsequent filtering and management. The workflow incorporates pagination handling and wait strategies to prevent Google rate limiting, ensuring efficient and stable acquisition of large volumes of candidate data.
Core Problems Addressed
In traditional recruitment, recruiters often need to manually craft complex Boolean search strings and repeatedly search LinkedIn or Google for matching candidates—a tedious and inefficient process. This workflow automates the generation of accurate search keywords from natural language input, automatically scrapes and organizes candidate data, significantly reducing the technical barrier and time cost for recruiters, and enabling automated collection and management of recruitment data.
Use Cases
- Recruitment teams quickly locating LinkedIn candidates that meet job requirements
- HR outsourcing firms bulk screening potential candidates
- Headhunters rapidly generating precise search strategies based on job descriptions and exporting candidate lists
- Internal corporate talent pool building and updating
Main Process Steps
- Receive User Input: Via a chat trigger node, the user inputs a job description or ideal candidate criteria.
- Generate Boolean Search String: Call the OpenAI GPT-4 model to convert natural language into a Google-specific LinkedIn Boolean search query.
- Create and Prepare Google Sheets Document: Create or use a specified Google Sheets file and set up header columns.
- Execute Google Search: Use an HTTP request node with cookie authentication to send Google search requests containing the Boolean query, ensuring result accuracy and avoiding rate limits.
- Extract LinkedIn URLs: Parse the returned Google search HTML pages via a code node to extract all LinkedIn personal profile URLs.
- Write Data to Spreadsheet: Append the extracted LinkedIn links and related fields into Google Sheets for easy review and filtering.
- Paginated Loop Crawling: Check if the preset number of results is reached; if not, automatically paginate and continue scraping until conditions are met.
Systems and Services Involved
- OpenAI GPT-4: For intelligent conversion from natural language to Boolean search queries.
- Google Search (HTTP Requests): To perform Boolean searches and return webpage data.
- Google Sheets: To store and manage the scraped candidate information.
- Chrome Cookie Editor Extension: Assists in exporting Google login cookies to enable authenticated searches and avoid rate limiting.
- n8n Automation Platform: The core execution environment for the workflow.
Target Users and Value
- Recruiters, headhunters, and HR managers seeking to quickly and efficiently find target candidates.
- Corporate talent sourcing teams aiming to improve recruitment accuracy and efficiency.
- Professionals requiring automated bulk collection of candidate information from LinkedIn.
- Users looking to reduce manual operation complexity through automation, save time costs, and enhance recruitment competitiveness.
This workflow combines advanced natural language processing with automated data scraping technology to provide a one-stop intelligent candidate search solution for the recruitment field, empowering users to complete talent search and management tasks easily and efficiently.