What problem does it solve?
Code is read far more than it is written, yet common metrics like LOC and cyclomatic complexity miss the real cost: the work a reader must do to trace where values come from and what can change them. This Skill gives reviewers a concrete rubric for spotting and reducing that reader load.
Core Features & Use Cases
- Two-axis audit: Count layers of indirection between a question and its answer, and count hidden or mutable state the reader must hold in their head.
- Layer collapse guidance: Identify one-caller wrappers, pass-through adapters, and speculative abstractions that should be inlined, plus interfaces that fail to compress complexity.
- State scope shrinking: Prefer pure functions, locals over fields, fields over module state, and derived values over synchronized copies.
- Use Case: During a code review of a service with six adapter layers and scattered global flags, apply this Skill to pinpoint which layers to inline and which state to localize, then verify a new reader can answer "where does X come from?" in under 30 seconds.
Quick Start
Ask the AI to review the current module using the minimize-reader-load principle and list which layers and mutable state should be collapsed.