A GitHub Action that automatically adds reviewers or assignees to pull requests when they are opened.
Auto Assign Action is a GitHub Action that automatically assigns reviewers or assignees to pull requests when they are opened. It solves the problem of manual reviewer assignment by automating the process based on configurable rules, ensuring timely reviews and reducing administrative overhead in development teams.
Development teams using GitHub for collaborative coding who want to automate and standardize their pull request review assignment process.
It offers a lightweight, configurable solution that integrates seamlessly into GitHub workflows, eliminating the need for manual reviewer assignment and supporting complex team structures with group-based assignments.
An action which adds reviewers to the pull request when the pull request is opened.
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The action uses easy-to-understand YAML files for setup, as shown in the README with examples for single and multiple reviewer lists.
Supports splitting reviewers into groups, ideal for teams with separate roles or locations, demonstrated in the multiple reviewers list configuration.
Allows skipping assignment for PRs with keywords like 'wip' and filtering by labels, providing precise control over when the action triggers.
Can be configured to work with pull requests from forks or bots like Dependabot using pull_request_target, though with security warnings in the README.
Offers an option to assign the PR creator as the assignee, simplifying responsibility tracking in the workflow.
Relies on predefined lists and does not support dynamic assignment based on real-time factors like reviewer availability or code changes.
Using pull_request_target for fork PRs introduces potential security vulnerabilities, and the README explicitly warns users to understand the risks.
Review groups and assignees must be manually updated in YAML files, which can be cumbersome in dynamic team environments.
Does not integrate with GitHub's built-in features like CODEOWNERS or team reviews, potentially leading to redundant configuration.