Pythonista Reviewing

Code Review Best Practices

Core Philosophy

Review at PR level, not file-by-file. Focus on egregious structural issues, not minutia. Look for cross-file patterns that indicate architectural problems.

Quick Start

# ALWAYS filter out lock files first (often 10k+ lines of noise)
git diff main...HEAD -- . ':!uv.lock' ':!poetry.lock' > /tmp/pr-diff.txt
wc -lc /tmp/pr-diff.txt  # Most diffs fit in 256KB after this

# If still too big, filter more
git diff main...HEAD -- . ':!uv.lock' ':!docs/*' > /tmp/code-diff.txt

What to Look For - Egregious Issues Only

Quick Checklist

• Code duplication - Same logic in 2+ places?
• Repeated patterns - Same code structure 3+ times?
• God functions - Functions over 100 lines?
• Weak types - Any, object, raw dict/list, missing annotations?
• Type-deficient patterns - hasattr, getattr instead of proper types?
• Meaningless tests - Tests that just verify assignment?
• Dead code - Unused functions, commented code?

What NOT to Flag

• Variable naming (unless truly confusing)
• Line length, comment style, whitespace, import order

These are auto-fixable or minor. Focus on structural problems.

LLM-Based Full Diff Review

Why use external LLM tools? Claude Code has a 25K context limit per tool call. For reviewing entire PR diffs (50K-200K+ tokens), use models with larger context windows.

Benefits:

1. Cross-file pattern detection in one pass
2. Different models catch different issues
3. Faster than file-by-file review

Workflow

# 1. Extract changes (handles lock file exclusion automatically)
./scripts/extract-changes.sh              # PR: current branch vs main
./scripts/extract-changes.sh abc123       # Specific commit
./scripts/extract-changes.sh auth/login   # Path pattern (fuzzy match)

# 2. Run code review with large-context model
./scripts/llm-review.sh -m gpt-4o
./scripts/llm-review.sh -m claude-3-5-sonnet-latest

# 3. Run specialized reviews
./scripts/llm-review-tests.sh -m gpt-4o      # Test quality
./scripts/llm-review-types.sh -m gpt-4o      # Type hints

Setup

Requires Simon Willison's llm tool:

pip install llm
llm keys set openai          # For GPT-4
pip install llm-claude-3     # For Claude
llm keys set claude

See references/llm-tooling.md for full setup and usage guide.

Using LLM Findings

LLM review provides hints, not final answers:

1. Identify areas to investigate deeper
2. Cross-check with different models
3. Use Claude Code to implement actual fixes

Human-in-the-Loop After Code Review

CRITICAL: After completing a code review, ALWAYS:

1. Present findings - Output the full review report
2. List suggested actions - Number each potential fix
3. Ask for approval - Use AskUserQuestion before executing
4. Wait for explicit approval - User may reject or approve selectively

Example flow:

[Code review findings output]

## Suggested Actions
1. Add format-duration validator to Schema
2. Add tests for format-duration validation

Which items should I proceed with?

Reference Files

• references/what-to-flag.md - Duplication, weak types, god functions
• references/code-bloat-patterns.md - AI-generated bloat patterns
• references/llm-tooling.md - LLM tool setup and usage

Remember: The ENTIRE POINT of full diff review is cross-file patterns. Don't chunk unless you absolutely must!

Related Skills

• /pythonista-testing - Test code review
• /pythonista-typing - Type issues to flag
• /pythonista-patterning - Pattern discovery
• /pythonista-debugging - Root cause analysis

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