Derived from analysing millions of lines of code, looking at hundreds of differernt datapoints including popularity of repositories, types of comments, contributions, specific to each language.
Screen developers of all levels – senior, mid-level, and junior – across all programming tech stacks, frameworks and languages.
API access, Zapier Integration, and ATS integration, making it easy to integrate GitScore with your existing systems.
A Comprehensive Overview of a Developer's Skillset
Sync candidate data, including their code analysis and GitHub profile statistics. This seamless data synchronization ensures that all relevant information is readily available and up-to-date within your ATS.
Create custom, automated workflows. For example, you can set triggers to automatically notify your team when a candidate's GitScore reaches a specific threshold or when new code analysis results are available.
By centralizing all candidate data, including code analysis and GitHub statistics, within your ATS, team members can easily access, discuss, and evaluate each candidate's technical skills.
Tailor the platform to your unique recruitment needs. Whether you require specific API access or need to connect GitScore with multiple tools, the platform can be customized to meet your requirements.
Each language activity is broken down and this feature delves deeper helping you uncover valuable insights that contribute to a comprehensive understanding of their skills and proficiency in context of the ecosystem's standards. The stars represent Predictive Indicators of the top 1%
Profile Statistics Highlights:
- Popularity of repositories
- Total Followers - Type of CommentsAll Predictive Indicators across all languages and charts are summarised here also with a further details on why these factors are Predictive Indicators of the 1% and which industry these Indicators are most relevant
Indicator Factors:
- Releveant Industries- Relevance of each Indicator
Language Distribution: GitScore calculates the percentage of code written in each language across the developer's repositories, providing an overview of their primary and secondary languages.
Language Experience: By analyzing the commit history, GitScore estimates the number of years the developer has been working with each programming language, providing a measure of their experience.
GitScore's AI-driven code analysis focuses on evaluating code quality and security to ensure that the developers you hire are skilled in writing clean, maintainable, and secure code. Here's how the AI analyzes code quality and security:
Coding Standards: The AI checks if the code adheres to established coding standards and style guidelines for the specific programming language used.
Code Complexity: By measuring cyclomatic complexity and other complexity metrics, the AI can assess the code's maintainability and potential for bugs.
Modularity and Reusability: The AI assesses how well the code is organized into modular, reusable components.
Here are some nice things our users have said about the platform
Senior Techincal Recruiter
GitScore has transformed the way we screen and select developers for our projects. The in-depth analysis of code quality, language proficiency, and overall GitHub profile statistics have allowed us to make more informed decisions, saving both time and resources.
CEO
As a non-technical founder, GitScore has been a game-changer in helping me understand the skills and expertise of potential developers. The platform's easy-to-understand dashboard views and comprehensive analysis have made it simple to find the right talent for our growing team.🔥
I.T. Project Manager
GitScore's API access and integration capabilities have seamlessly connected with our existing Applicant Tracking System, greatly enhancing our recruiting workflow. The platform provides valuable insights into developers' skills, allowing us to efficiently identify and hire the best candidates for our projects.🚀