code-refinement

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

Analysis Dimensions

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:evidence-logging

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:evidence-logging (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

Cross-Plugin Dependencies

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