What if you could focus on finding the best candidates and let AI do the heavy lifting by analyzing their work history, summarizing their background, categorizing them into an industry and scoring them with custom criteria. That’s exactly what this automation does and it takes less than 5 minutes to build completely from scratch with AgentHub.

What does this Automation Take as Input

A LinkedIn Profile URL (must be a public profile. We do not spoof any LinkedIn credentials) either one at a time or in bulk through an Excel or Google Sheets file!

Ex: https://www.linkedin.com/in/max-brodeur-urbas-1a4b25172/

What does it output?

A complete summary of the candidate ready for inserting into an Excel or Google Sheets file!

Full name: Max Brodeur-Urbas
Professional Background: Founder of AgentHub (YC W24), experienced in software engineering with a history of working at Microsoft and competitive programming leadership at McGill University.
Industry: AI
Score: 80

How it Works

In this post I’m going to explain exactly how each step of this automation works so you can customize it to meet your exact needs. If you want to skip the technical side of things, scroll to the end of this post to start using the automation.

As a general overview of the automation, we scrape the persons profile, ask the AI a series of very specific questions using their profile as context and output the answers wherever you normally track your leads.

Gathering Info

Scraping the Profile

We use our web scraping node to read the profiles content in preparation for AI processing. This node takes in a link to any website and outputs text as if you copy pasted all the visible text on the screen.

Note: This does not work with private Linkedin profiles. We don’t use any fake credentials or bots to navigate the website. Just good old web scraping.

AI Gets Involved

Each of these steps utilizes a different AI node on AgentHub. In case you’re not familiar, AgentHub is a no-code AI automation builder. Drag and drop your way to extremely powerful solutions. If you’re curious about what AgentHub is or why it exists check out this blog post.

Summarizing

Let’s start off with summarizing their professional background as this is the most straightforward step. We pass the contents of their profile into an “Ask AI” node on AgentHub and request that the AI generate a short one sentence summary of their background.

You'll notice the prompt being used is quite generic, feel free to add extra instructions or even an example summary if you want to provide the AI with extra guidance for formatting purposes.

Scoring

Our scoring node forces the AI to output a number between 0 and 100 that it chooses based on custom criteria you define.

Here is our scoring description. Keep in mind it’s extremely generic as this demo automation was made for anyone to use. Add specific criteria that you’d want in a good score vs what would be missing to cause a bad one.

Categorizing

Categorization on AgentHub is a super popular feature. It allows you to define an arbitrary number of category labels + descriptions and have the AI select one for a given piece of content.

In this demo automation, I set the categories to common startup fields like AI, Biotech, Fintech etc. You can modify these to reflect the sorts of candidates you're expecting for your use case.

I wrote more about how categorization it works here

Extract the Candidates Name

Last but not least, we’ll use the ‘Extract Key Info’ node to have the AI return values from the input text. In this case the value we’re having it return is the candidate’s full name. This is useful for bookkeeping purposes and for when you want to reach out to the candidate.

You could modify this node to extract any sort of info from the profile like their current job title, their current company name etc.

Store Your AI's Analysis

Adding to Google Sheets

We have now finished extracting useful info from the Linkedin profile but we need to store it somewhere for future reference. For the sake of this demo we’ll store the info in Google Sheet as that’s the most popular flow. You could do any number of things with it however (email it, store it in airtable, store it in an excel file, post it to twitter...)

We’ll take the contents of all of these AI steps and pass them into the “Write to Google Sheets Node”. This demo is generating a new Google Sheet on each run (for the sake of letting people get started more quickly) but in practice you’ll want to specify an existing google sheet so it can keep adding to the same ongoing database.

Further tweaking ideas

• Extract their current company URL
• Categorize candidates into multiple categories
• Extract their Job Title
• Extract each work experience into a separate column

Happy Automating 👋

If you have any questions or want a custom automation for your business email me at max@agenthub.dev, follow me on Twitter or join the AgentHub Discord