Code readability review based on "The Art of Readable Code" and Miller's Law (7±2 cognitive limits). Use when reviewing readability (可読性), understandability (理解しやすい/わかりやすい), clarity (明確), naming (命名), variable/function names (変数名/関数名), nesting depth (ネスト/深いネスト), function design (関数設計), comments (コメント), complexity (複雑), confusing code (難しい/難読), Miller's Law (ミラーの法則), cognitive load (認知負荷), AI-generated code issues, premature optimization, over-engineering (過剰設計), or unnecessary abstractions (不要な抽象化). Detects readability issues and suggests improvements based on cognitive science. Essential for readability-focused code reviews.
Limited to specific tools
Additional assets for this skill
This skill is limited to using the following tools:
references/ai-antipatterns.mdreferences/comments-clarity.mdreferences/control-flow.mdreferences/naming-structure.md"Code should be written to minimize the time it would take for someone else to understand it"
Human cognitive capacity is limited to 7±2 items
When code exceeds these limits:
Our brains literally cannot process too much complexity at once.
Recommended Limits:
| Context | Ideal | Maximum |
|---|---|---|
| Function arguments | 3 | 5 |
| Class methods | 5 | 7 |
| Conditional branches | 3 | 5 |
| Function length | 5-10 lines | 15 lines |
| Nesting depth | 2 | 3 |
This skill is organized into 4 specialized sections for efficient context usage:
File: references/naming-structure.md
Tokens: ~500
Focus: Variable/function naming, concrete vs abstract, searchability
Triggers: naming, 命名, variable name, 変数名, function name, 関数名, concrete, abstract
Coverage:
File: references/control-flow.md
Tokens: ~600
Focus: Control flow optimization, nesting reduction, Miller's Law application
Triggers: nesting, ネスト, control flow, Miller's Law, complexity, guard clause, early return
Coverage:
File: references/comments-clarity.md
Tokens: ~400
Focus: Comment strategy, intent communication, code self-documentation
Triggers: comments, コメント, documentation, intent, 意図, obvious, clarity
Coverage:
File: references/ai-antipatterns.md
Tokens: ~500
Focus: Detecting over-engineering patterns in AI-generated code
Triggers: AI, AI-generated, premature, over-engineering, unnecessary abstraction
Coverage:
User: "This function is hard to understand"
Readability Review Skill triggers →
"From a readability perspective, let's improve:
1. Function name clarity
2. Nesting reduction (currently 4 levels)
3. Extract complex conditions
4. Apply Miller's Law (max 5 parameters)
Let me suggest specific improvements..."
Function review
Variable naming
Code structure
AI code review
The Final Test: "Would a new team member understand this in < 1 minute?"