bloat-detector
Scannednpx machina-cli add skill athola/claude-night-market/bloat-detector --openclawTable 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
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
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-runfor previews - Backup branches - created before bulk changes
Related
bloat-auditoragent - Executes scansunbloat-remediatoragent - Safe remediationcontext-optimizationskill - MECW principles
Source
git clone https://github.com/athola/claude-night-market/blob/master/plugins/conserve/skills/bloat-detector/SKILL.mdView on GitHub Overview
Bloat-detector systematically finds and eliminates codebase bloat across multiple tiers, focusing on dead code, duplication, complexity, and documentation bloat. It helps teams trim unnecessary artifacts to improve maintainability and future refactoring readiness.
How This Skill Works
It employs progressive loading of analysis tiers: Tier 1 quick scan, Tier 2 targeted analysis with static analysis and git churn data, and Tier 3 deep audit with cross-file redundancy and readability metrics. It integrates with common tools (Bash, Grep, Glob, Read) and supports module-based remediation workflows.
When to Use It
- Context usage > 30%
- Quarterly maintenance
- Pre-release cleanup
- Before major refactoring
- Codebase < 1000 lines
Quick Start
- Step 1: /bloat-scan
- Step 2: /bloat-scan --level 2 --focus code
- Step 3: /bloat-scan --level 3 --report audit.md
Best Practices
- Run Tier 1 quick scan first to surface large files, stale code, basic duplication, and dead code indicators
- Proceed to Tier 2 targeted analysis with static analysis (Vulture/Knip) and git churn hotspots
- Use Tier 3 deep audit for cross-file redundancy and readability metrics when tools are available
- Always perform a dry run and respect auto-exclusions (.venv, __pycache__, .git, node_modules, dist, build, vendor)
- Follow remediation guidance (DELETE, REFACTOR, CONSOLIDATE, ARCHIVE) from the remediation-types module
Example Use Cases
- Cleaning a legacy app with a large amount of dead code and God classes
- Reducing dependency bloat and unused imports in a monorepo
- Identifying AI-generated bloat patterns such as tab-completion bloat and hallucinated deps
- Reducing documentation bloat by removing redundancy and verbose content
- Pre-refactor cleanup to lower churn and improve readability before major changes
