Skill: athola/nm-pensive-code-refinement (version 1.0.2)

--- name: code-refinement description: | Improve code quality: duplication, efficiency, clean code, architectural fit, and error handling version: 1.9.5 triggers: - refactoring - clean-code - algorithms - duplication - anti-slop - craft metadata: {"openclaw": {"homepage": "https://github.com/athola/claude-night-market/tree/master/plugins/pensive", "emoji": "\ud83e\udd9e", "requires": {"config": ["night-market.pensive:shared", "night-market.pensive:safety-critical-patterns", "night-market.imbue:proof-of-work"]}}} source: claude-night-market source_plugin: pensive --- > **Night Market Skill** — ported from [claude-night-market/pensive](https://github.com/athola/claude-night-market/tree/master/plugins/pensive). For the full experience with agents, hooks, and commands, install the Claude Code plugin. ## Table of Contents - [Quick Start](#quick-start) - [When to Use](#when-to-use) - [Analysis Dimensions](#analysis-dimensions) - [Progressive Loading](#progressive-loading) - [Required TodoWrite Items](#required-todowrite-items) - [Workflow](#workflow) - [Tiered Analysis](#tiered-analysis) - [Cross-Plugin Dependencies](#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 ## 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 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: ```bash # 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. ### 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](modules/code-quality-analysis.md) - 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


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)