Triggers: refine, code quality, clean code, refactor, duplication, algorithm efficiency, complexity reduction, code smell, anti-slop, craft 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). DO NOT use when: reviewing for bugs (use pensive:bug-review). DO NOT use when: selecting architecture paradigms (use archetypes skills). This skill actively improves living code, complementing bloat detection (dead code removal) with quality refinement (living code improvement).
View on GitHubplugins/pensive/skills/code-refinement/SKILL.md
February 1, 2026
Select agents to install to:
npx add-skill https://github.com/athola/claude-night-market/blob/main/plugins/pensive/skills/code-refinement/SKILL.md -a claude-code --skill code-refinementInstallation paths:
.claude/skills/code-refinement/## Table of Contents - [Quick Start](#quick-start) - [When to Use](#when-to-use) - [Analysis Dimensions](#analysis-dimensions) - [Progressive Loading](#progressive-loading) - [Required TodoWrite Items](#required-todowrite-items) - [Workflow](#workflow) - [Tiered Analysis](#tiered-analysis) - [Cross-Plugin Dependencies](#cross-plugin-dependencies) # Code Refinement Workflow Analyze and improve living code quality across six dimensions. ## Quick Start ```bash /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 ## 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 Load modules based on refinement focus: - **`modules/duplication-analysis.md`** (~400 tokens): Duplication detection and consolidation - **`modules/algorithm-efficiency.md`** (~400 tokens): Complexity analysis and optimization - **`modules/clean-code-checks.md`** (~450 tokens): Clean code, anti-slop, error handling - **`modules/architectural-fit.md`** (~400 tokens): Paradigm alignment and coupling Load all for compreh