name	bloat-detector
description	Detect codebase bloat through progressive analysis: dead code, duplication, complexity, documentation bloat. Triggers: bloat detection, dead code, code cleanup, duplication, technical debt, unused code 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

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)

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): - @module:quick-scan - Heuristics, no tools - @module:git-history-analysis - Staleness, churn, vibe coding signatures

Tier 2 (optional tools): - @module:code-bloat-patterns - Anti-patterns (God class, Lava flow) - @module:ai-generated-bloat - AI-specific patterns (Tab bloat, hallucinations) - @module:documentation-bloat - Redundancy, readability, slop detection - @module:static-analysis-integration - Vulture, Knip

Shared: - @module:remediation-types - DELETE, REFACTOR, CONSOLIDATE, ARCHIVE

Auto-Exclusions

Always excludes: .venv, __pycache__, .git, node_modules, dist, build, vendor

Also respects: .gitignore, .bloat-ignore

Safety

Never auto-delete - all changes require approval
Dry-run support - --dry-run for previews
Backup branches - created before bulk changes

bloat-auditor agent - Executes scans
unbloat-remediator agent - Safe remediation
context-optimization skill - MECW principles

来源：athola/claude-night-market · MIT 许可 · 许可原文 · MD 为来源页保存的历史版本

bloat-detector

SKILL.md