audit-context-building

Build deep architectural context before a security audit by performing ultra-granular, line-by-line code analysis that explicitly maps functions, flows, invariants, and assumptions to establish a stable mental model.

AI loses context and hallucinates during codebase analysis when lacking a stable, explicit mental model. Applying micro-scale first principles reasoning tracks cross-function and external call flows with full context propagation to prevent this.

Track cross-function data flows for vulnerability hunting by performing bottom-up, line-by-line code comprehension. This approach focuses on identifying invariants, assumptions, and state changes to ensure no critical detail is missed.

Yes, you can use line-by-line code analysis for smart contract auditing. It meticulously maps every function's logic, data flow, and state changes, ensuring no critical detail is missed before vulnerability or bug finding.

Identify invariants and reasoning hazards during threat modeling by governing the context-building phase with first principles and 5 Whys/5 Hows at a micro-scale, establishing an explicit mental model of the codebase architecture.

Yes, ultra-granular code comprehension works without external dependencies. It governs the AI's internal thinking process to build a stable mental model and track external call flows using only the provided codebase.