code-refinement（代码细化）

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)

Analysis Dimensions

Plugin-Specific Patterns

Detection patterns for plugin and skill codebases where standard code quality heuristics miss structural issues.

Delegation Stub Bodies

A skill that declares "delegates to X" but still carries the full template body is doing double duty. The delegating skill should be a thin wrapper (under 30 lines) that routes to the target. Flag any delegating skill whose body exceeds 50 lines.

Module Explosion

Flag skills with 10+ module files where 40% or more of content overlaps. Signal: two modules covering the same API surface from different angles (e.g., both describing the same config options or the same CLI flags).

Oversized Single Modules

Flag individual module files exceeding 500 lines as candidates for splitting or trimming. Large modules defeat progressive loading by forcing full-file reads for partial information.

Dead Python References

Skills referencing Python commands (python -m module.name or python -c "from module import...") where the referenced module does not exist in the plugin's src/ directory. These are stale references to renamed or removed code.

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:proof-of-work

Workflow

Step 1: Establish Context (refine:context-established)

Detect project characteristics:

# Language detection
find . -not -path "*/.venv/*" -not -path "*/__pycache__/*" \
  -not -path "*/node_modules/*" -not -path "*/.git/*" \
  \( -name "*.py" -o -name "*.ts" -o -name "*.rs" -o -name "*.go" \) \
  | head -20

# Framework detection
ls package.json pyproject.toml Cargo.toml go.mod 2>/dev/null

# Size assessment
find . -not -path "*/.venv/*" -not -path "*/__pycache__/*" \
  -not -path "*/node_modules/*" -not -path "*/.git/*" \
  \( -name "*.py" -o -name "*.ts" -o -name "*.rs" \) \
  | xargs wc -l 2>/dev/null | tail -1

Step 2: Dimensional Scan (refine:scan-complete)

Load relevant modules and execute analysis per tier level. For dimension 7 (Additive Bias), run Skill(imbue:justify) to compute the bias score, check Iron Law compliance, and flag unnecessary additions or workarounds.

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

Cross-Plugin Dependencies

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