What problem does it solve? Code that is hard to trace forces readers to hold too much context in their heads, slowing down maintenance and increasing the risk of bugs. This Skill gives you a concrete framework for measuring and reducing the cognitive load your code imposes on anyone who reads it. ## Core Features & Use Cases - Layer Auditing: Counts the indirections between a question and its answer, then collapses one-caller wrappers, pass-through adapters, and speculative abstractions. - State Scope Reduction: Shrinks mutable state from globals toward module state, fields, locals, and pure functions, favoring derivation over synchronization. - Use Case: During a code review of a service with six layers of adapters and scattered mutable fields, apply this Skill to inline single-caller wrappers, demand interface compression, and name invariants at boundaries so a new reader can trace any value in under 30 seconds. ## Quick Start Ask the AI to review this module using the minimize reader load principle and identify layers and state that should be collapsed.