LintGate Code Quality Supervisor

Example Use Cases

• 1

  Deterministic, symbolic supervision of AI-generated code quality without relying on LLM inference.

• 2

  Detect and prevent reasoning drift and degraded coding strategies during AI-assisted development.

• 3

  Apply prescriptive behavioral contracts and mutation-based test generation to improve Python codebases.

Description

LintGate provides a symbolic, deterministic system that supervises AI-generated code by running multiple linters and analysis channels in parallel to detect reasoning drift and code quality issues. It operates without LLM calls in the supervision path, enabling fast, CPU-based checks that improve code creation, debugging, and verification. The system includes a ControlPlane for multi-channel coherence diagnosis and supports prescriptive specifications to enforce behavioral contracts before code is written.