The prompt
You are a senior Python developer and code reviewer with deep expertise in
Python best practices, PEP8 standards, type hints, and performance optimization.
Do not change the logic or output of the code unless it is clearly a bug.
I will provide you with a Python code snippet. Review and enhance it using
the following structured flow:
---
📝 STEP 1 — Documentation Audit (Docstrings & Comments)
- If docstrings are MISSING: Add proper docstrings to all functions, classes,
and modules using Google or NumPy docstring style.
- If docstrings are PRESENT: Review them for accuracy, completeness, and clarity.
- Review inline comments: Remove redundant ones, add meaningful comments where
logic is non-trivial.
- Add or improve type hints where appropriate.
---
📐 STEP 2 — PEP8 Compliance Check
- Identify and fix all PEP8 violations including naming conventions, indentation,
line length, whitespace, and import ordering.
- Remove unused imports and group imports as: standard library → third‑party → local.
- Call out each fix made with a one‑line reason.
---
⚡ STEP 3 — Performance Improvement Plan
Before modifying the code, list all performance issues found using this format:
| # | Area | Issue | Suggested Fix | Severity | Complexity Impact |
|---|------|-------|---------------|----------|-------------------|
Severity: [critical] / [moderate] / [minor]
Complexity Impact: Note Big O change where applicable (e.g., O(n²) → O(n))
Also call out missing error handling if the code performs risky operations.
---
🔧 STEP 4 — Full Improved Code
Now provide the complete rewritten Python code incorporating all fixes from
Steps 1, 2, and 3.
- Code must be clean, production‑ready, and fully commented.
- Ensure rewritten code is modular and testable.
- Do not omit any part of the code. No placeholders like “# same as before”.
---
📊 STEP 5 — Summary Card
Provide a concise before/after summary in this format:
| Area | What Changed | Expected Impact |
|-------------------|-------------------------------------|------------------------|
| Documentation | ... | ... |
| PEP8 | ... | ... |
| Performance | ... | ... |
| Complexity | Before: O(?) → After: O(?) | ... |
---
Here is my Python code:
${paste_your_code_here}
How to use this prompt
Copy the prompt above or click an "Open in" button to launch it directly in your preferred AI. You can then customize the wording to match your exact use case — for example replacing placeholders like [your topic] with real context.
Which AI model works best
Claude Opus 4 and Sonnet 4.6 generally outperform ChatGPT and Gemini on coding tasks — better reasoning, better at handling long context (full files, multi-file projects), and more honest about uncertainty. ChatGPT is faster for quick snippets; Gemini is best when code involves screenshots or visual context.
How to customize this prompt
Swap the language mentioned in the prompt (Python, JavaScript, etc.) for whichever stack you're on. For debugging or code review, paste your actual code right after the prompt. For generation tasks, specify the framework (React, Vue, Django, FastAPI) and any constraints (max lines, no external libraries, must be async).
Common use cases
- Writing production code with strict style requirements
- Reviewing pull requests and catching bugs before merge
- Converting between languages (Python → TypeScript, for example)
- Generating unit tests for existing functions
- Explaining unfamiliar codebases to new team members
Variations
Adapt the tone (more casual, more technical), change the output format (bullet points vs. paragraphs), or add constraints (word limits, target audience).
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