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code-refinement

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Table of Contents

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

#DimensionModuleWhat It Catches
1Duplication & Redundancyduplication-analysisNear-identical blocks, similar functions, copy-paste
2Algorithmic Efficiencyalgorithm-efficiencyO(n^2) where O(n) works, unnecessary iterations
3Clean Code Violationsclean-code-checksLong methods, deep nesting, poor naming, magic values
4Architectural Fitarchitectural-fitParadigm mismatches, coupling violations, leaky abstractions
5Anti-Slop Patternsclean-code-checksPremature abstraction, enterprise cosplay, hollow patterns
6Error Handlingclean-code-checksBare 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: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

TierTimeScope
1: Quick (default)2-5 minComplexity hotspots, obvious duplication, naming, magic values
2: Targeted10-20 minAlgorithm analysis, full duplication scan, architectural alignment
3: Deep30-60 minAll above + cross-module coupling, paradigm fitness, comprehensive plan

Cross-Plugin Dependencies

DependencyRequired?Fallback
pensive:sharedYesCore review patterns
imbue:evidence-loggingOptionalInline evidence in report
conserve:code-quality-principlesOptionalBuilt-in KISS/YAGNI/SOLID checks
archetypes:architecture-paradigmsOptionalPrinciple-based checks only (no paradigm detection)

Supporting Modules

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

Source

git clone https://github.com/athola/claude-night-market/blob/master/plugins/pensive/skills/code-refinement/SKILL.mdView on GitHub

Overview

Code Refinement analyzes living code quality across six dimensions: duplication, algorithmic efficiency, clean-code violations, architectural fit, anti-slop patterns, and robust error handling. It helps teams refactor for clarity, reduce AI slop, and plan targeted improvements during development and releases.

How This Skill Works

The skill uses modular analyzers to inspect six dimensions with dedicated modules (duplication-analysis, algorithm-efficiency, clean-code-checks, architectural-fit). It employs progressive loading to avoid analysis overload, aggregates findings, and ranks them by impact and effort to generate a concrete refinement plan with before/after guidance.

When to Use It

  • 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

Quick Start

  1. Step 1: Run a base refinement: /refine-code
  2. Step 2: Focus on a specific area, e.g., /refine-code --level 2 --focus duplication
  3. Step 3: Produce an actionable plan with before/after, e.g., /refine-code --level 3 --report refinement-plan.md

Best Practices

  • Start with a focused scope (e.g., duplication or efficiency) and load only relevant modules
  • Leverage progressive loading to manage complexity and speed
  • Capture evidence for findings using imbue:evidence-logging
  • Prioritize findings by impact and effort to create a practical plan
  • Generate a concrete refinement plan with before/after guidance

Example Use Cases

  • Deduplicating near-identical blocks in a data-processing module to reduce maintenance cost
  • Replacing an O(n^2) loop with an O(n) approach to improve performance
  • Breaking a long method into smaller, well-named functions to improve readability
  • Aligning a module with the appropriate architectural pattern to reduce coupling
  • Adding robust error handling to properly propagate failures instead of silent drops

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