Scrubby vs Codacy
Codacy and Scrubby both show up in your code review pipeline, but they're approaching code quality from very different angles. Codacy is a mature static-analysis platform built on a large library of universal rules, with strong security scanning. Scrubby is a codebase intelligence layer built on knowledge specific to your repo — its domains, conventions, and history. They don't compete; they complement.
What Codacy does well
Codacy is one of the most comprehensive static-analysis platforms on the market. It supports dozens of languages, integrates with every major CI/CD platform, and exposes dashboards for tracking code quality metrics over time. For teams with compliance requirements or that need to enforce consistent standards across a large org, the rule library alone makes it worth evaluating.
- Extensive rule library. Code style, complexity, duplication, security, and more.
- Security scanning. A core feature, covering common vulnerabilities and compliance requirements.
- Mature platform. Dashboards, trend tracking, multi-language support, and broad CI integration.
- Configurable. Teams can tune which rules apply to which paths via configuration files.
If you need to demonstrate to a security or compliance team that your codebase is being scanned against a known rule set, Codacy is exactly the kind of tool that satisfies that requirement.
Where Codacy stops short
Codacy's rules are universal by design. They apply the same way to every codebase. That's a strength when you're trying to enforce a consistent baseline across many projects, but it's a structural limitation when you're trying to enforce your team's conventions:
- The rules are written in advance and applied uniformly. Codacy doesn't learn from your repo's history.
- Customization happens through manual config updates, not by observing how your team actually writes code.
- Analysis is local to each file or diff. There's no concept of cross-file co-change, domain boundaries, or convention extraction from real commits.
- It doesn't feed AI agents. There's no MCP server, no real-time codebase context for the AI editors your team is increasingly relying on.
That last one is becoming the bigger gap. As more code is generated by AI agents, the choke point moves upstream — from was the merged code clean to did the agent generating it have any idea what your codebase looks like.
What Scrubby does differently
Scrubby doesn't ship a rule library. It builds one, automatically, by indexing your repository and extracting conventions from the code your team has actually been writing. Then it applies that knowledge in two places:
- As a GitHub App on every PR. Scrubby reviews the changeset against your codebase's actual patterns. It flags missing co-changes, domain boundary crossings, and convention violations grounded in your project's real history.
- As an MCP server inside your AI editor. Claude Code, Cursor, Windsurf, VS Code, and Zed can all query Scrubby for codebase context before generating code. The result: PRs that fit on the first push.
Scrubby also strengthens with use. Connections that produce useful findings get reinforced. Connections that don't get weakened. The system converges on the relationships that actually matter for your repo, instead of being frozen at whatever the rule author thought made sense at config time.
Side-by-side
| Capability | Codacy | Scrubby |
|---|---|---|
| Static-analysis rule library | Yes (extensive) | No |
| Security scanning / SAST | Yes | No |
| Compliance reporting | Yes | No |
| Multi-language coverage | Dozens | JS/TS, Python, Ruby, Go, Java |
| Learns your codebase's conventions | No | Yes |
| Domain-aware reviews | No | Yes |
| Co-change detection | No | Yes |
| Convention extraction from git history | No | Yes |
| MCP server for AI editors | No | Yes |
| Strengthens with usage | No | Yes (Hebbian learning) |
The class of issue each one catches
Codacy catches universal issues.
SQL injection. Cyclomatic complexity above a threshold. Duplicated blocks. Insecure deserialization. Style violations. The kinds of issues that are wrong in any codebase, anywhere.
Scrubby catches codebase-specific issues.
"You added a service file at the top level, but every other service in this codebase lives under app/services/<domain>/." "This migration changes the schema but the corresponding model validation wasn't updated — that pair has changed together in 41 of the last 47 commits." "The error-handling pattern here diverges from the convention used everywhere else in this domain."
Codacy can't see the second category, because it has no idea what your codebase is supposed to look like. Scrubby can, because it spent the first index figuring exactly that out.
Do you need both?
Generally, yes. Codacy is a great answer to "how do we enforce a baseline of code quality and security across the org?" Scrubby is a great answer to "how do we make sure code (especially AI-generated code) actually fits this codebase?" They operate at different layers of the stack and don't conflict.
If you already have Codacy and you're starting to ship a lot of AI-generated code, Scrubby is the layer that catches the architectural and convention issues Codacy isn't designed for. If you don't yet have either and you have to choose, the answer depends on your pain: regulatory or security pressure points to Codacy, AI-generated tech debt and slow PR reviews point to Scrubby.
Static analysis grades your code against universal rules. Scrubby grades it against the rules your team actually follows.
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