---

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

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 comprehensive refinement. For focused work, load only relevant modules.

Required TodoWrite Items

1. refine:context-established — Scope, language, framework detection
2. refine:scan-complete — Findings across all dimensions
3. refine:prioritized — Findings ranked by impact and effort
4. refine:plan-generated — Concrete refactoring plan with before/after
5. refine:evidence-captured — Evidence appendix per imbue:proof-of-work

Workflow

Step 1: Establish Context (refine:context-established)

Detect project characteristics:

# Language detection
find . -name "*.py" -o -name "*.ts" -o -name "*.rs" -o -name "*.go" | head -20

# Framework detection
ls package.json pyproject.toml Cargo.toml go.mod 2>/dev/null

# Size assessment
find . -name "*.py" -o -name "*.ts" -o -name "*.rs" | xargs wc -l 2>/dev/null | tail -1

Step 2: Dimensional Scan (refine:scan-complete)

Load relevant modules and execute analysis per tier level.

Step 3: Prioritize (refine:prioritized)

Rank findings by:

• Impact: How much quality improves (HIGH/MEDIUM/LOW)
• Effort: Lines changed, files touched (SMALL/MEDIUM/LARGE)
• Risk: Likelihood of introducing bugs (LOW/MEDIUM/HIGH)

Priority = HIGH impact + SMALL effort + LOW risk first.

Step 4: Generate Plan (refine:plan-generated)

For each finding, produce:

• File path and line range
• Current code snippet
• Proposed improvement
• Rationale (which principle/dimension)
• Estimated effort

Step 5: Evidence Capture (refine:evidence-captured)

Document with imbue:proof-of-work (if available):

• [E1], [E2] references for each finding
• Metrics before/after where measurable
• Principle violations cited

Fallback: If imbue is not installed, capture evidence inline in the report using the same [E1] reference format without TodoWrite integration.

Tiered Analysis

Tier	Time	Scope
1: Quick (default)	2-5 min	Complexity hotspots, obvious duplication, naming, magic values
2: Targeted	10-20 min	Algorithm analysis, full duplication scan, architectural alignment
3: Deep	30-60 min	All above + cross-module coupling, paradigm fitness, comprehensive plan

Cross-Plugin Dependencies

Dependency	Required?	Fallback
pensive:shared	Yes	Core review patterns
imbue:proof-of-work	Optional	Inline evidence in report
conserve:code-quality-principles	Optional	Built-in KISS/YAGNI/SOLID checks
archetypes:architecture-paradigms	Optional	Principle-based checks only (no paradigm detection)

Supporting Modules

• Code quality analysis - duplication detection commands and consolidation strategies

When optional plugins are not installed, the skill degrades gracefully:

• Without imbue: Evidence captured inline, no TodoWrite proof-of-work
• Without conserve: Uses built-in clean code checks (subset)
• Without archetypes: Skips paradigm-specific alignment, uses coupling/cohesion principles only