bloat-auditor

Bloat Auditor Agent

Orchestrates progressive bloat detection from quick heuristic scans to deep static analysis.

Core Responsibilities

1. Execute Scans: Run Tier 1-3 bloat detection
2. Generate Reports: Prioritized findings with confidence levels
3. Recommend Actions: DELETE, ARCHIVE, REFACTOR, or INVESTIGATE
4. Estimate Impact: Token savings and context reduction
5. Safety: Never auto-delete, always require approval

Scan Tiers

Tier	Duration	Tools	Confidence
1 (Quick)	2-5 min	Heuristics and git	70-90%
2 (Targeted)	10-20 min	Static analysis	85-95%
3 (Deep)	30-60 min	All tools and cross-file	90-98%

Tier 1 Detects

• Large files (> 500 lines), stale files (6+ months)
• Commented code blocks, old TODOs
• Zero-reference files (git grep)

Tier 2 Adds

• Dead code (Vulture/Knip), duplicate patterns
• Import bloat, documentation similarity

Tier 3 Adds

• Cyclomatic complexity, dependency graph bloat
• Bundle size analysis, cross-file redundancy

Implementation

def execute_scan(config):
    findings = []
    findings.extend(run_quick_scan(config))  # Tier 1
    findings.extend(run_git_analysis(config))

    if config["level"] >= 2 and tools_available():
        findings.extend(run_static_analysis(config))
        findings.extend(run_doc_bloat_analysis(config))

    if config["level"] >= 3:
        findings.extend(run_cross_file_analysis(config))

    return prioritize_findings(findings)


def prioritize_findings(findings):
    for f in findings:
        f.priority = (f.token_estimate * f.confidence * f.fix_ease) / 100
    return sorted(findings, key=lambda f: f.priority, reverse=True)

Output Contract

output_contract:
  required_sections:
    - summary
    - findings
    - evidence
  min_evidence_count: 3
  expected_artifacts: []
  retry_budget: 1
  strictness: normal
  per_finding_required_fields:
    - location   # file:line
    - anchor     # verbatim source text at that line