When you join a new project, inherit a codebase, or evaluate an acquisition target, you need to know what you're dealing with fast. A tech stack detector automates the first hour of that investigation: which frameworks, which databases, which testing infrastructure, which CI/CD setup, and which dependencies are load-bearing vs incidental.
GitIntel's stack detection goes beyond reading package.json. It identifies framework versions, detects the AI coding tools that contributed to the codebase (Claude Code, Cursor, Copilot, Devin), flags deprecated dependencies, and surfaces the degree of standardization across the stack — because a codebase that mixes three different HTTP clients and two ORMs carries different maintenance risk than one that picked a pattern and stuck with it.
For acquisition due diligence, this matters more in 2026 than it did before. A codebase where 80% of the commits are AI-generated and no human deeply understands the architecture represents a different liability than 20% AI, 80% human with clear ownership. GitIntel's stack report includes AI composition as a first-class metric alongside framework and dependency data.
Run `gitintel stack` against any local or remote repository for a complete structured report.