Sourcing GitHub candidates with SeekOut

SeekOut's GitHub search lets you find software developer candidates in a completely new way.

This feature is available for users with Professional and Enterprise licenses.

SeekOut's powerful and efficient search engine lets you find software developer candidates from GitHub. To access GitHub search, go to SeekOut search and select the GitHub tab at the top of the page.

Check out this extended webinar covering the GitHub talent pool, or keep scrolling to read on.

We analyze the code contributions for every GitHub user to determine their subject matter expertise and proficiency with different programming languages. We also compute their Coder Score, a measure of technical ability based on their actual GitHub contributions.

Next, we merge their GitHub profile with public profile data so you can search using fields that aren't typically found on a GitHub profile -- title, location, education, and more.

What makes SeekOut's GitHub search special?

It's difficult to use GitHub on its own as a source for untapped talent.

  • It's hard to tell the difference between a GitHub member who has done interesting work and one who has just forked several repositories but never made an impact with his or her code. 

  • Most GitHub profiles are incredibly sparse, without any mention of location, current employer, or other basic data about the candidate. 

  • Without lots of clicking around, it's hard to see what a GitHub user is interested in, let alone what kind of code they've written.

SeekOut's GitHub search solves these problems by doing the following:

  • Indexing the descriptions and metadata of all repositories (software projects) on GitHub to know what they are about, how widely used, and how influential they are.

  • Analyzing each code contribution on GitHub to understand how impactful it was (e.g., Was it made to an important project or in a branch with just one contributor? Was it reused by others? Etc.)

  • Rating each GitHub member's overall coding ability as well as their proficiency with each individual programming language.

  • Merging the data in their GitHub profile with other sources, such as their publicly available LinkedIn profile. This adds relevant info about their skills, experience, & education. By combining these sources, you can search based on deep knowledge of the candidate's technical expertise as well as shallower characteristics like location, company, education, etc.

An effective way to source GitHub candidates with SeekOut is to follow these steps:

Step 1: Choose a Power Filter

SeekOut's Power Filters let you focus your search on particular software experience categories. 

Screenshot of Power Filter options for software development experience

You'll find power filters for mobile development, full-stack engineers, UX design, machine learning, and over 50 more in-demand areas. Click More Power Filters to see all of the available power filters.

Step 2: Enter Keywords

Once you've chosen your Power Filter, enter any keywords required for the role.

Screenshot of keyword options in GitHub search

Step 3: Choose a Coder Score

SeekOut analyzes code contributions from GitHub users to determine how well-accepted or influential their code is. Using that analysis, we assign each member a Coder Score of one to five stars based on their demonstrated contributions.

Click on a coder score to filter your search results to only show candidates who match that score. You can also use the slider to look for candidates within a range of coder scores.

Screenshot of coder score selector

Because we measure demonstrated ability based on actual GitHub contributions, you can be confident a developer with a 3-, 4-, or 5-star rating is very strong. Developers with lower scores, however, may still be very good or even great, but they don't do a lot of open-source development. 

Step 4: Language Expertise

Filter your search by specific programming language expertise or exposure, then refine it even further by selecting a coder score range for each language.

Screenshot of language expertise selector