Code Review

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

# Check size - if under 256KB, analyze the whole thing
# 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.

Critical Rules Quick Reference

# Flag: Weak types
def process(data: Any) -> dict:  # Any, raw dict
def process(data: UserData) -> UserResponse:  # Specific types

# Flag: hasattr/getattr (type deficiency marker)
if hasattr(obj, "name"):  # Why don't you know the type?
if isinstance(obj, Named):  # Use protocol or union type

# Flag: God functions
def process_order(order_data):  # 200 lines doing everything
    validated = _validate(data)  # Extract responsibilities
    transformed = _transform(validated)

# Flag: Duplicate logic across files
# service_a.py and service_b.py have same 20-line validation
# Extract to shared utility

# Flag: Related classes without shared interface
class CacheDirectory:
    def list_entries(self): ...  # Names differ
class StatusDirectory:
    def get_all_entries(self): ...  # Should share ABC

Handling Large Diffs

Keep maximum context to spot cross-file patterns.

Step 1: Remove Lock Files (Always)

git diff main...HEAD -- . ':!uv.lock' > /tmp/diff.txt
wc -lc /tmp/diff.txt  # Under 256KB? STOP - analyze whole thing

Step 2: Remove Docs (If Still Too Big)

git diff main...HEAD -- . ':!uv.lock' ':!docs/*' > /tmp/code-diff.txt

Step 3: Remove Tests (Last Resort)

git diff main...HEAD -- . ':!uv.lock' ':!docs/*' ':!tests/*' > /tmp/prod.txt

Step 4: Chunk with LARGE Chunks (Rare)

# Only if >256KB after all filtering
# Use 15k lines to preserve cross-file patterns
# Small chunks (2k lines) defeat the purpose!

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

Reference Files

For detailed patterns and examples:

• 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 for full-diff review

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

Human-in-the-Loop After Code Review

CRITICAL: After completing a code review, ALWAYS:

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

Why this matters:

• LLM reviews often suggest unnecessary changes
• Some suggestions may be wrong or over-engineered
• User knows context that LLM doesn't
• Executing without approval wastes time on rejected work

Example flow:

[Code review findings output]

## Suggested Actions
1. Add format-duration validator to Schema
2. Add tests for format-duration validation
3. Move constant to config class

Which items should I proceed with?

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