What problem does it solve? Reviewing a large codebase's public API surface for design flaws is error-prone when done ad hoc, and agents lose coherence over long reviews. This protocol structures the audit into phased, checkpointed passes so every exported type, function, and trait is evaluated against consistent quality dimensions with human approval at each stage. ## Core Features & Use Cases - Surface Discovery: Enumerates all exported types, functions, constants, re-exports, and traits into a structured surface map before any analysis begins. - Six-Dimension Component Audit: Rates each component on minimal surface, type safety, composability, monosemicity, naming, and error handling with PASS/WARN/FAIL grades and remediation patterns. - Language-Specific Checklists: Provides dedicated audit checklists for Rust, Go, TypeScript, and Python covering idioms like newtypes, discriminated unions, and error wrapping. - Use Case: Before shipping a library, run the audit to produce a prioritized P0-P3 remediation plan identifying type safety gaps, redundant methods, and inconsistent naming across modules. ## Quick Start Ask the agent to run an API coherence audit on your repository's public modules and present the surface map for approval.