---

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

Related

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

Claude Skill Registry (Data)

This repo contains the archived skill contents (the heavy, browsable skill files).

Canonical layout

• Category folders at repo root (e.g. development/, documents/, data/, ...)
• Each skill lives under a category: <category>/<skill>/SKILL.md + <category>/<skill>/metadata.json
• Case conflicts are resolved with {name}-{owner}-{repo} suffixes (fallback: -{short-hash}).

Archive status

• Live badges above are sourced from claude-skill-registry-core stats.json.
• Counts in this README are intentionally dynamic, not hardcoded.
• If the badges look stale, refresh the core build/index pipeline rather than editing numbers here.

Where the index + site live

• Core repo: https://github.com/majiayu000/claude-skill-registry-core
• Main repo (merged publish artifact): https://github.com/majiayu000/claude-skill-registry