bloat-detector

name	bloat-detector
description	Detect codebase bloat through progressive analysis: dead code, duplication, complexity, documentation bloat. Use when context usage high, quarterly maintenance, pre-release cleanup, before refactoring. Do not use when active feature development, time-sensitive bugs, codebase < 1000 lines.
category	conservation
tags	["bloat","cleanup","static-analysis","technical-debt","optimization"]
tools	["Bash","Grep","Glob","Read"]
modules	["quick-scan","git-history-analysis","code-bloat-patterns","ai-generated-bloat","documentation-bloat","static-analysis-integration","remediation-types"]
progressive_loading	true
estimated_tokens	400

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