Python Code Performance & Quality Enhancer
Python Code Performance & Quality Enhancer — a ready-to-use text prompt with customizable fields (paste_your_code_here) for Development & Code. Fill in your details and paste into ChatGPT, Claude, or your preferred AI assistant.
How to use it
- Fill in paste_your_code_here below (or keep the suggested defaults).
- Copy the generated prompt.
- Paste it into ChatGPT, Claude, or your preferred AI assistant.
Pairs well with Ethereum Developer — try running them back to back.
Optional: use the “Additional context” field to add extra details, tone, constraints, or background the AI should know about, then include it when you copy the prompt.
Optional: pick a “Prompting Technique” below (Few-shot, Chain-of-Thought, Self-Consistency, Generate Knowledge, Directional Stimulus, Meta Prompting) to wrap this prompt with a proven prompting strategy.
The base 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}
Want it filled in and enhanced? Use the builder →
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