Der 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.
────────────────────────────────────────
PHASE 1 — DISCOVERY (scan everything)
────────────────────────────────────────
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
────────────────────────────────────────
PHASE 2 — VERIFICATION (don't shoot living code)
────────────────────────────────────────
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.
────────────────────────────────────────
PHASE 3 — TRIAGE (prioritize the cleanup)
────────────────────────────────────────
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
────────────────────────────────────────
OUTPUT FORMAT
────────────────────────────────────────
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.
So nutzt du diesen Prompt
Kopiere den Prompt oben oder klicke einen "Öffnen in"-Button um ihn direkt in deiner bevorzugten KI zu starten. Du kannst den Text dann an deinen Anwendungsfall anpassen — z.B. Platzhalter wie [dein Thema] durch echten Kontext ersetzen.
Welches KI-Modell funktioniert am besten
Claude Opus 4 und Sonnet 4.6 performen bei Coding-Aufgaben meist besser als ChatGPT und Gemini — stärkeres Reasoning, besser mit langem Kontext (ganze Dateien, Multi-File-Projekte), und ehrlicher über Unsicherheit. ChatGPT ist schneller für Quick-Snippets; Gemini ist am besten wenn Code mit Screenshots oder visuellem Kontext zu tun hat.
Diesen Prompt anpassen
Tausche die im Prompt erwähnte Sprache (Python, JavaScript, etc.) gegen deinen Stack. Für Debugging oder Code-Review fügst du deinen echten Code direkt nach dem Prompt ein. Bei Generierungs-Aufgaben spezifiziere das Framework (React, Vue, Django, FastAPI) und Einschränkungen (max. Zeilen, keine externen Libraries, muss async sein).
Typische Anwendungsfälle
- Production-Code mit strikten Style-Vorgaben schreiben
- Pull Requests reviewen und Bugs vor dem Merge finden
- Zwischen Sprachen konvertieren (Python → TypeScript z.B.)
- Unit-Tests für bestehende Funktionen generieren
- Unbekannte Codebases für neue Team-Mitglieder erklären
Variationen
Passe den Tonfall an (lockerer, technischer), ändere das Ausgabeformat (Aufzählungen vs. Absätze) oder füge Einschränkungen hinzu (Wortlimits, Zielgruppe).
Verwandte Prompts