code-refinement
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).
- License 许可
- GPL-3.0
- 最近更新
- 2026-08-29
- Stars
- ★ 2
- Forks
- 0
01 前言 Front Matter
- name
- code-refinement
- tags
- [ "refactoring", "clean-code", "algorithms", "duplication", "anti-slop", "craft" ]
- tools
- [ "Read", "Grep", "Glob", "Bash" ]
- modules
- [ "modules/duplication-analysis.md", "modules/algorithm-efficiency.md", "modules/clean-code-checks.md", "modules/architectural-fit.md" ]
- category
- code-quality
- complexity
- advanced
- 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).
- dependencies
- [ "pensive:shared", "pensive:safety-critical-patterns", "imbue:proof-of-work" ]
- usage_patterns
- [ "code-quality-improvement", "duplication-reduction", "algorithm-optimization", "clean-code-enforcement" ]
- estimated_tokens
- 350
- progressive_loading
- true
02 资源文件 5
- 参考文档modules/algorithm-efficiency.md4.2 KB
- 参考文档modules/architectural-fit.md4.7 KB
- 参考文档modules/clean-code-checks.md5.5 KB
- 参考文档modules/code-quality-analysis.md4.3 KB
- 参考文档modules/duplication-analysis.md3.6 KB
03 SKILL.md 正文
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 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: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
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