/bloat-detector

name: bloat-detector
description: Detects codebase bloat via dead code, duplication, complexity, and doc bloat scans. Use when codebase feels large or before a release.
alwaysApply: false
category: conservation
tags:
- bloat
- cleanup
- static-analysis
- technical-debt
- optimization
tools: []
modules:
- modules/quick-scan.md
- modules/git-history-analysis.md
- modules/growth-analysis.md
- modules/code-bloat-patterns.md
- modules/ai-generated-bloat.md
- modules/documentation-bloat.md
- modules/static-analysis-integration.md
- modules/remediation-types.md
progressive_loading: true
estimated_tokens: 400
usage_patterns:
- "bloat"
- "dead code"
- "unused"
- "cleanup"
- "unbloat"
- "prune"
model_hint: standard

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

Ecosystem-Level Detection

Patterns that span plugin boundaries or manifest configuration, discovered through ecosystem-wide audits.

`alwaysApply` Accumulation

Flag plugins with 3+ skills where `alwaysApply: true`. Each always-on skill injects its full text into every session, creating a baseline token floor before the user types anything. Sum the `estimated_tokens` fields to report total per-session cost.

Hook Registration Gaps

Compare hooks declared in `plugin.json` or `openpackage.yml` against entries in `hooks.json`. A hook present in `hooks.json` but absent from the manifest is invisible to the plugin loader and cannot be audited, versioned, or disabled through normal plugin management.

Boilerplate Footer Detection

Scan skill files for identical multi-line text blocks repeated across 10+ files (e.g., generic troubleshooting sections like "Command not found / Permission errors / Unexpected behavior"). These are copy-paste artifacts that inflate token cost without adding skill-specific value.

ToC Bloat in Skills

Skills loaded into model context gain nothing from HTML-style Tables of Contents. Detect `## Table of Contents` followed by bulleted anchor-link lists. These waste tokens since the model reads sequentially, not via hyperlinks.

Unregistered Module Subdirectories

Compare files on disk in `skills/*/modules/` against the `modules:` list in each skill's SKILL.md frontmatter. Files that exist on disk but are not listed in the manifest are invisible to progressive loading and may be dead weight or missing from the load path.

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
• `/bloat-scan` command - User-facing slash command that invokes this

skill; there is no separate `bloat-scan` skill

Exit Criteria

• [ ] At least one tier of scan (Tier 1, 2, or 3) completed and

findings listed per bloat category (Code, AI-Generated, Documentation, Dependencies, Git History)

• [ ] Each finding assigned a confidence level (HIGH/MEDIUM/LOW) and

a priority score computed from the formula `(Token_Savings × 0.4) + (Maintenance × 0.3) + (Confidence × 0.2)

• (Ease × 0.1)`
• [ ] No auto-deletions executed without explicit user approval;

every proposed removal documented as DELETE, REFACTOR, CONSOLIDATE, or ARCHIVE per `modules/remediation-types.md`

• [ ] Ecosystem-level patterns checked (alwaysApply accumulation,

hook registration gaps, boile