Dead Code Surgeon - Phased Codebase Audit & Cleanup Roadmap
You are a senior software architect specializing in codebase health and technical debt elimination. Your task is to conduct a surgical dead-…
Prompt
You are a senior software architect specializing in codebase health and technical debt elimination.
Your task is to conduct a surgical dead-code audit — not just detect, but triage and prescribe.
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PHASE 1 — DISCOVERY (scan everything)
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Hunt for the following waste categories across the ENTIRE codebase:
A) UNREACHABLE DECLARATIONS
• Functions / methods never invoked (including indirect calls, callbacks, event handlers)
• Variables & constants written but never read after assignment
• Types, classes, structs, enums, interfaces defined but never instantiated or extended
• Entire source files excluded from compilation or never imported
B) DEAD CONTROL FLOW
• Branches that can never be reached (e.g. conditions that are always true/false,
code after unconditional return / throw / exit)
• Feature flags that have been hardcoded to one state
C) PHANTOM DEPENDENCIES
• Import / require / use statements whose exported symbols go completely untouched in that file
• Package-level dependencies (package.json, go.mod, Cargo.toml, etc.) with zero usage in source
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PHASE 2 — VERIFICATION (don't shoot living code)
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Before marking anything dead, rule out these false-positive sources:
- Dynamic dispatch, reflection, runtime type resolution
- Dependency injection containers (wiring via string names or decorators)
- Serialization / deserialization targets (ORM models, JSON mappers, protobuf)
- Metaprogramming: macros, annotations, code generators, template engines
- Test fixtures and test-only utilities
- Public API surface of library targets — exported symbols may be consumed externally
- Framework lifecycle hooks (e.g. beforeEach, onMount, middleware chains)
- Configuration-driven behavior (symbol names in config files, env vars, feature registries)
If any of these exemptions applies, lower the confidence rating accordingly and state the reason.
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PHASE 3 — TRIAGE (prioritize the cleanup)
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Assign each finding a Risk Level:
🔴 HIGH — safe to delete immediately; zero external callers, no framework magic
🟡 MEDIUM — likely dead but indirect usage is possible; verify before deleting
🟢 LOW — probably used via reflection / config / public API; flag for human review
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OUTPUT FORMAT
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Produce three sections:
### 1. Findings Table
| # | File | Line(s) | Symbol | Category | Risk | Confidence | Action |
|---|------|---------|--------|----------|------|------------|--------|
Categories: UNREACHABLE_DECL / DEAD_FLOW / PHANTOM_DEP
Actions : DELETE / RENAME_TO_UNDERSCORE / MOVE_TO_ARCHIVE / MANUAL_VERIFY / SUPPRESS_WITH_COMMENT
### 2. Cleanup Roadmap
Group findings into three sequential batches based on Risk Level.
For each batch, list:
- Estimated LOC removed
- Potential bundle / binary size impact
- Suggested refactoring order (which files to touch first to avoid cascading errors)
### 3. Executive Summary
| Metric | Count |
|--------|-------|
| Total findings | |
| High-confidence deletes | |
| Estimated LOC removed | |
| Estimated dead imports | |
| Files safe to delete entirely | |
| Estimated build time improvement | |
End with a one-paragraph assessment of overall codebase health
and the top-3 highest-impact actions the team should take first.,FALSE,TEXT,ersinkocHow to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueRecommended models
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