Every pull request in this organization receives an automated code review powered by AI before a human reviewer looks at it.
On top of existing static analysis for security and code quality, GitHub Copilot automatically reviews every pull request: it analyses the full changeset and posts inline feedback on potential bugs, security risks, performance issues, and code quality concerns. By the time a human reviewer opens the PR, a thorough first pass has already been completed, reducing review turnaround time and catching issues early.
In addition, developers can request a deeper, context-aware second opinion from Cursor on any PR, or ask it to fix the issues it finds and push commits directly. This layered approach with automated first pass, optional deep review, and AI-assisted fixes, helps maintain high code quality across the organization while keeping review cycles fast.
Our repositories include custom agent skills that are structured instructions that help AI coding agents understand the project's architecture, conventions, and multi-file workflows. Rather than relying on generic AI assistance, these skills encode domain-specific knowledge so that agents can perform tasks with a higher degree of accuracy and autonomy.
This means developers can confidently delegate complex, recurring tasks to AI agents, such as generating new components that follow established patterns, creating boilerplate across multiple files, or preparing pull request descriptions that match the team's standards.
Current skills cover areas including generating new AI Agent Tools, managing the LLM model lifecycle, generating PR descriptions, handling UI translations, and working with our Model Context Protocol (MCP) integration.
