code-refinement

name	code-refinement
description	Analyze and improve living code quality: duplication, algorithmic efficiency, clean code principles, architectural fit, anti-slop patterns, and error handling robustness. Use when improving code quality, reducing AI slop, refactoring for clarity, optimizing algorithms, applying clean code principles. Do not use when removing dead/unused code (use conserve:bloat-detector). reviewing for bugs (use pensive:bug-review). selecting architecture paradigms (use archetypes skills). This skill actively improves living code, complementing bloat detection (dead code removal) with quality refinement (living code improvement).
category	code-quality
tags	["refactoring","clean-code","algorithms","duplication","anti-slop","craft"]
tools	["Read","Grep","Glob","Bash"]
usage_patterns	["code-quality-improvement","duplication-reduction","algorithm-optimization","clean-code-enforcement"]
complexity	advanced
estimated_tokens	350
progressive_loading	true
dependencies	["pensive:shared","pensive:safety-critical-patterns","imbue:proof-of-work"]
modules	["modules/duplication-analysis.md","modules/algorithm-efficiency.md","modules/clean-code-checks.md","modules/architectural-fit.md"]

Table of Contents

• Quick Start
• When to Use
• Analysis Dimensions
• Progressive Loading
• Required TodoWrite Items
• Workflow
• Tiered Analysis
• Cross-Plugin Dependencies

Code Refinement Workflow

Analyze and improve living code quality across six dimensions.

Quick Start

/refine-code
/refine-code --level 2 --focus duplication
/refine-code --level 3 --report refinement-plan.md

When To Use

• After rapid AI-assisted development sprints
• Before major releases (quality gate)
• When code "works but smells"
• Refactoring existing modules for clarity
• Reducing technical debt in living code

When NOT To Use

• Removing dead/unused code (use conserve:bloat-detector)
• Removing dead/unused code (use conserve:bloat-detector)

Analysis Dimensions

#	Dimension	Module	What It Catches
1	Duplication & Redundancy	duplication-analysis	Near-identical blocks, similar functions, copy-paste
2	Algorithmic Efficiency	algorithm-efficiency	O(n^2) where O(n) works, unnecessary iterations
3	Clean Code Violations	clean-code-checks	Long methods, deep nesting, poor naming, magic values
4	Architectural Fit	architectural-fit	Paradigm mismatches, coupling violations, leaky abstractions
5	Anti-Slop Patterns	clean-code-checks	Premature abstraction, enterprise cosplay, hollow patterns
6	Error Handling	clean-code-checks	Bare excepts, swallowed errors, happy-path-only

Progressive Loading