bloat-detector
Detect codebase bloat via progressive analysis: dead code, duplication, complexity, and doc bloat
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/athola/nm-conserve-bloat-detectorNight Market Skill — ported from claude-night-market/conserve. For the full experience with agents, hooks, and commands, install the Claude Code plugin.
Table of Contents
- Bloat Categories
- Quick Start
- When to Use
- Confidence Levels
- Prioritization
- Module Architecture
- Safety
Bloat Detector
Systematically detect and eliminate codebase bloat through progressive analysis tiers.
Bloat Categories
| Category | Examples |
|---|---|
| Code | Dead code, God classes, Lava flow, duplication |
| AI-Generated | Tab-completion bloat, vibe coding, hallucinated deps |
| Documentation | Redundancy, verbosity, stale content, slop |
| Dependencies | Unused imports, dependency bloat, phantom packages |
| Git History | Stale files, low-churn code, massive single commits |
Quick Start
Tier 1: Quick Scan (2-5 min, no tools)
/bloat-scan
Detects: Large files, stale code, old TODOs, commented blocks, basic duplication
Tier 2: Targeted Analysis (10-20 min, optional tools)
/bloat-scan --level 2 --focus code # or docs, deps
Adds: Static analysis (Vulture/Knip), git churn hotspots, doc similarity
Tier 3: Deep Audit (30-60 min, full tooling)
/bloat-scan --level 3 --report audit.md
Adds: Cross-file redundancy, dependency graphs, readability metrics
When To Use
| Do | Don't |
|---|---|
| Context usage > 30% | Active feature development |
| Quarterly maintenance | Time-sensitive bugs |
| Pre-release cleanup | Codebase < 1000 lines |
| Before major refactoring | Tools unavailable (Tier 2/3) |
When NOT To Use
- Active feature development
- Time-sensitive bugs
- Codebase < 1000 lines
Confidence Levels
| Level | Confidence | Action |
|---|---|---|
| HIGH | 90-100% | Safe to remove |
| MEDIUM | 70-89% | Review first |
| LOW | 50-69% | Investigate |
Prioritization
Priority = (Token_Savings × 0.4) + (Maintenance × 0.3) + (Confidence × 0.2) + (Ease × 0.1)
Module Architecture
Tier 1 (always available):
- See
modules/quick-scan.md- Heuristics, no tools - See
modules/git-history-analysis.md- Staleness, churn, vibe coding signatures - See
modules/growth-analysis.md- Growth velocity, forecasts, threshold alerts
Tier 2 (optional tools):
- See
modules/code-bloat-patterns.md- Anti-patterns (God class, Lava flow) - See
modules/ai-generated-bloat.md- AI-specific patterns (Tab bloat, hallucinations) - See
modules/documentation-bloat.md- Redundancy, readability, slop detection - See
modules/static-analysis-integration.md- Vulture, Knip
Shared:
- See
modules/remediation-types.md- DELETE, REFACTOR, CONSOLIDATE, ARCHIVE
Auto-Exclusions
Metadata
Not sure this is the right skill?
Describe what you want to build — we'll match you to the best skill from 16,000+ options.
Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-athola-nm-conserve-bloat-detector": {
"enabled": true,
"auto_update": true
}
}
}Related Skills
extract
Analyze a codebase and build a knowledge base of business logic, architecture, data flow, and engineering patterns. The foundation for gauntlet challenges and agent integration
discourse
>- Scan community discussion channels (HN, Lobsters, Reddit, tech blogs) for experience reports and opinions on a topic
synthesize
>- Merge, deduplicate, rank, and format research findings from multiple channels into a coherent report. Use after research agents return their results
workflow-monitor
Detect workflow failures and inefficient patterns, then create GitHub issues for improvement via /fix-workflow
architecture-paradigm-hexagonal
Hexagonal (Ports and Adapters) architecture isolating domain logic from infrastructure