Code Quality Guard Rails for AI

AI-generated code often has hidden bugs, security issues, and poor tests. Guard Skills acts as automated quality gates for coding agents, catching failure modes before they ship. A SaaS could offer pre-configured checks, team dashboards, and CI/CD integration.

AI-generated code volume is exploding as teams adopt Copilot, Cursor, and autonomous agents like Devin, and the failure modes are well-documented — hallucinated logic, missing error handling, and tests that pass but don't actually cover the happy path. SonarQube and Semgrep exist for static analysis but aren't purpose-built around the specific anti-patterns AI models produce, which is the gap here. The $2k–10k MRR band is realistic for a dev-tools product selling to small engineering teams on a per-seat or per-repo model, though it's a ceiling that's hard to break without a clear land-and-expand motion into enterprise. The biggest risk is commoditization: the major AI coding tools are already bolting on quality checks natively, and if Cursor or GitHub Copilot ships a "review mode" that's good enough, the standalone value proposition collapses fast.