About
Continue brings quality control to your software development workflow by running source-controlled AI checks on every pull request. Instead of relying on inconsistent manual reviews or broad, opinionated AI tools, Continue lets teams define their own engineering standards as markdown files committed directly to their repository. These standards are then enforced automatically as native GitHub status checks on every PR, complete with suggested fixes when code misses the mark. The platform is designed for teams that want to scale code quality without scaling review overhead. As your development velocity increases, Continue keeps pace — no PR goes unchecked regardless of team size or throughput. Unlike generic AI reviewers, Continue only enforces what you explicitly tell it to catch, eliminating surprise bugs and unsolicited suggestions. Continue supports integration with popular developer tools including GitHub, Sentry, Snyk, and Linear, and can run background agents for continuous automation tasks. It's purpose-built for engineering teams who want to focus on architecture and direction rather than mechanical code review enforcement. Whether you're a startup standardizing early practices or an enterprise scaling a large engineering org, Continue helps maintain quality across your entire software factory.
Key Features
- Markdown-Defined Engineering Standards: Write your code quality checks as markdown files stored directly in your repository, keeping standards version-controlled alongside your codebase.
- Native GitHub Status Checks: Checks run as native GitHub status checks on every pull request, providing inline visibility and blocking merges when standards aren't met.
- Suggested Fixes on Failures: When code misses the mark, Continue automatically generates suggested fixes so developers can resolve issues quickly without back-and-forth.
- Consistent, Scoped Enforcement: Only enforces what you explicitly define — no surprise opinions or generic feedback, just precise enforcement of your team's actual standards.
- Scales with Development Velocity: Every PR is checked automatically regardless of how fast your team ships, ensuring quality control never becomes a bottleneck.
Use Cases
- Enforcing consistent coding standards across a large engineering team without manual review overhead.
- Automatically catching security anti-patterns or banned dependencies before code is merged.
- Onboarding new developers by encoding team conventions as enforceable checks rather than undocumented norms.
- Scaling code quality assurance as a startup's engineering team grows and PR volume increases.
- Running continuous background checks integrated with tools like Sentry and Snyk to catch issues in every pull request.
Pros
- Standards as Code: Defining checks as markdown in your repo means quality standards are version-controlled, reviewable, and evolve with your codebase.
- Non-Opinionated Enforcement: Only catches what you explicitly configure, avoiding the noise and frustration of generic AI review tools that inject unsolicited opinions.
- Seamless GitHub Integration: Runs as native GitHub status checks, fitting naturally into existing PR workflows without requiring developers to learn new tools.
Cons
- Requires Upfront Standard Definition: Teams must invest time writing quality check definitions before getting value — there's no out-of-the-box ruleset to start with immediately.
- GitHub-Centric: Currently focused on GitHub integration, which may limit adoption for teams using GitLab, Bitbucket, or other source control platforms.
Frequently Asked Questions
How do I define checks in Continue?
You write checks as markdown files and commit them to your repository. Continue reads these files and enforces them as automated checks on every pull request.
Does Continue replace human code review?
No — Continue handles mechanical enforcement of your engineering standards, but humans remain in control of decisions. It reduces review burden so engineers can focus on architecture and direction.
How does Continue integrate with GitHub?
Continue runs as native GitHub status checks on pull requests. When a PR violates a defined standard, it appears as a failed check with suggested fixes directly in the GitHub UI.
What kind of checks can I create?
You can create checks for code security, anti-patterns, documentation standards, dependency policies, and any other engineering standards your team wants to enforce consistently.
Is Continue suitable for large engineering teams?
Yes — Continue is designed to scale with your development velocity, ensuring every PR is checked consistently regardless of team size or how many PRs are opened per day.
