code-metrics-analysis

What it does

This skill enables AI agents to analyze software codebases for quality and complexity metrics. It helps identify potential bottlenecks, technical debt, and areas requiring refactoring by evaluating structural characteristics of the code.

When to use it

• Before starting a major feature implementation to understand existing codebase health.
• During code reviews to objectively assess complexity beyond human intuition.
• When planning refactoring efforts to prioritize high-risk or overly complex modules.
• To generate baseline metrics for tracking code quality improvements over time.

Key capabilities

• Calculates cyclomatic complexity to measure decision density in functions.
• Identifies code duplication patterns across the repository.
• Assesses maintainability based on metric thresholds.
• Provides actionable insights into architectural weaknesses.

Example prompts

• "Analyze this Python module and report its cyclomatic complexity along with specific lines that exceed recommended limits."
• "Identify duplicated code blocks in this JavaScript project and suggest refactoring strategies to reduce redundancy."
• "Evaluate the maintainability score of this microservice architecture and highlight dependencies that may cause future bottlenecks."

Tips & gotchas

Ensure your codebase is properly indexed before running analysis, as missing files can skew metric accuracy. Focus on interpreting high-complexity scores in context rather than treating them as absolute failure indicators.