The problem

AI agents often produce bloated code, introduce premature abstractions, or refactor unrelated files during a simple bug fix. This scope creep makes PR reviews difficult and introduces hidden technical debt.

What it does

• Evaluates code against four core principles: intentionality, simplicity, surgical precision, and goal-driven verification.
• Identifies complexity hotspots like N+1 queries, O(n^2) loops, and inefficient React render patterns.
• Produces a structured PASS/FAIL/WARN scorecard for every code change or proposal.
• Enforces a strict "HOLD" verdict if any principle is failed, preventing speculative or messy code from shipping.

Why this beats prompting it yourself

Manual prompting often yields generic feedback or missing edge cases. This skill follows a rigorous audit protocol with line-level evidence and specific remediation steps that stay consistent across every session. It treats code review as a gate rather than a suggestion.

Use cases

• Reviewing a PR before submittal to ensure it only touches relevant files.
• Auditing AI-generated code to strip out "just in case" abstractions.
• Scanning for performance bottlenecks in data-heavy loops or UI components.
• Verifying that a fix actually meets the pre-defined success criteria.

Known limitations

Complexity hotspot detection is focused on patterns; it may flag false positives if performance trade-offs were made intentionally without comments.