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).
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
/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 consolidationmodules/algorithm-efficiency.md(~400 tokens): Complexity analysis and optimizationmodules/clean-code-checks.md(~450 tokens): Clean code, anti-slop, error handlingmodules/architectural-fit.md(~400 tokens): Paradigm alignment and coupling
Load all for comprehensive refinement. For focused work, load only relevant modules.
Required TodoWrite Items
refine:context-established— Scope, language, framework detectionrefine:scan-complete— Findings across all dimensionsrefine:prioritized— Findings ranked by impact and effortrefine:plan-generated— Concrete refactoring plan with before/afterrefine:evidence-captured— Evidence appendix perimbue: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
| 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
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
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agent-ops-spec
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agent-ops-testing
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agent-ops-state
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