Complexity Check Skill

Analyze and report code complexity metrics.

When to Use

• Code review process
• Identifying refactoring candidates
• Maintaining code quality
• Before major releases

Quick Reference

# Analyze Python code
./scripts/check_complexity.py

# Generate full report
./scripts/complexity_report.sh > complexity.txt

# Check Mojo code
./scripts/check_mojo_complexity.sh

Complexity Metrics

Cyclomatic Complexity (CC)

Measures decision points (if, for, while):

Nesting Depth

Maximum levels of nesting in function:

Function Length

Lines of code in function:

Refactoring Patterns

Extract Function (High CC)

# Before (CC: 15)
def process(data):
    if condition1:
        if condition2:
            if condition3:
                for item in data:
                    if item.valid:
                        # process

# After (CC: 5)
def process(data):
    if not is_valid(data):
        return
    filtered = filter_valid_items(data)
    return process_items(filtered)

Flatten Nesting

Replace nested ifs with early returns:

# Before (depth: 5)
fn process(data):
    if check1(data):
        if check2(data):
            if check3(data):
                # complex logic

# After (depth: 2)
fn process(data):
    if not check1(data): return
    if not check2(data): return
    if not check3(data): return
    # complex logic

Workflow

# 1. Analyze code
./scripts/check_complexity.py

# 2. Review high-complexity functions
grep "CC:" complexity.txt | grep -E "CC: [2-9][0-9]|CC: [1-9][0-9]{2}"

# 3. Plan refactoring
# ... extract functions, flatten nesting ...

# 4. Re-analyze
./scripts/check_complexity.py

# 5. Verify improved
git diff complexity.txt

Error Handling

Thresholds

Project thresholds:

• Max CC per function: 15
• Max nesting depth: 4
• Max function length: 50 LOC
• Minimum test coverage: 80%

References

• Related skill: phase-cleanup for refactoring guidelines
• Related skill: quality-run-linters for complete quality check