Agent skill

code-refinement

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).

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Forks 31

Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/code-refinement

SKILL.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

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 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:

bash
# 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

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:evidence-logging 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)

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

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