Git commits are the most underused data source in software engineering. Every commit contains authorship, timing, diff size, message quality, and file change patterns. Aggregate those signals across a team's history and you get a precise picture of how your codebase evolved — and where the debt lives.
The AI layer makes this analysis more urgent. GitClear's 2026 analysis of 153 million lines of code found that AI-assisted teams had 41% higher code churn, 4x more code duplication, and refactoring at a 10-year low. These patterns are readable in commit history — but only if you know what to look for.
GitIntel's commit analyzer detects AI-origin commits using metadata signatures validated against 33,580 PRs across five major AI coding tools. It surfaces: which authors are AI-heavy versus human-heavy, whether AI-generated code has higher churn rates than human code in your specific repo, commit message quality trends over time, and large commits (a common AI pattern) that represent hidden review risk.
Run `gitintel commits [--since 90d]` to get a structured breakdown of your commit history with AI attribution and quality signals overlaid.