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AlabamaMike
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forensic-complexity-trends

Analyze code complexity trends from monthly Git history samples.

3|2|Updated Oct 27, 2025
Search Tags:#refactoring#complexity#time-series#code-quality#analyze#hotspots#git-history
One-click install
npx skills add https://github.com/AlabamaMike/forensic-skills --skill forensic-complexity-trends
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: forensic-complexity-trends
Source: https://github.com/AlabamaMike/forensic-skills/tree/main/.claude/skills/forensic-complexity-trends
Command: npx skills add https://github.com/AlabamaMike/forensic-skills --skill forensic-complexity-trends

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps teams monitor how code complexity evolves across git history, enabling early detection of deterioration, validation of refactoring ROI, and informed maintenance planning.

Core Features & Use Cases

  • Time-series sampling of complexity metrics from git history (LOC, indentation depth, function count, long functions) to identify improving, stable, or deteriorating files.
  • Classification and volatility analysis to surface hotspots and inform refactoring decisions.
  • Integration guidance with other forensic skills (hotspot finder, knowledge mapping, ROI) to translate trends into actionable priorities.
  • Use Case: Before releasing a major feature, verify that complexity is trending downward or stable in critical modules, and plan refactorings accordingly.

Quick Start

Ensure you have a Git-enabled repository with a full history. Collect monthly samples for the last 12 months, compute a baseline and current complexity, and generate a trend report to identify deteriorating areas and recommended actions.

Frequently Asked Questions about forensic-complexity-trends

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I track code complexity trends over time using git history?▼

Code complexity trends are tracked by sampling metrics like LOC, indentation depth, and function count from git history monthly over a defined period, classifying files as improving, stable, or deteriorating to identify hotspots.

Can I measure refactoring ROI by analyzing git history?▼

Measuring refactoring ROI involves analyzing git history to compare baseline and current complexity metrics, validating whether specific files show downward complexity trends and reduced volatility after refactoring efforts.

What code complexity metrics are used to identify deteriorating files?▼

Metrics used to identify deteriorating files include lines of code, indentation depth, function count, and long functions, sampled over time to detect inflection points, volatility, and changes in trend within git history.

Does this complexity trend analysis require a full Git repository history?▼

Yes, this complexity trend analysis requires a Git-enabled repository with comprehensive history to collect monthly samples and accurately compute baseline versus current complexity metrics over the defined period.

How do I detect code complexity hotspots before a major release?▼

Detecting code complexity hotspots before a major release involves analyzing time-series complexity trends to find deteriorating or volatile files, verifying stability, and planning refactoring actions for critical modules.

What's the best way to classify code complexity volatility in a repository?▼

Classifying code complexity volatility requires computing monthly time-series samples from git history, comparing baselines against current metrics, and checking for inflection points to surface unstable files requiring maintenance.

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