Describe the codebase shape
Module count and dependency count set the raw size and surface area to map.
ONBOARDING FRICTION CHECK
Outline modules, entry points, dependencies, and test coverage to score how easy an unfamiliar codebase is to map before an AI coding session.
Planning guidance only. Validate important decisions with customer evidence and your delivery team.
YOUR INPUTS
Complete every field. The result updates only when you choose Calculate.
Module count and dependency count set the raw size and surface area to map.
A documented entry point and existing tests both make a codebase far easier to explore safely.
The four inputs combine into a 0-100 score with the biggest friction point called out first.
Continue learning: Working with AI coding agents
No — it's a quick gut-check on how much friction a new contributor or coding agent should expect before diving in.
Without a documented entry point, every new reader has to reverse-engineer where execution starts, which is the single biggest source of onboarding time.
The score is a heuristic based on raw counts — use the next-steps guidance to judge whether your organization already compensates for a high module count.
Yes — tests document expected behavior in a way comments can't, and they let you verify an AI-generated change safely once you've mapped the code.
| Feature | MVPHub | GitHub Copilot | Cursor |
|---|---|---|---|
| Structured onboarding-friction score | Included | Not included | Not included |
| Instant, no-install check | Included | Not included | Not included |
| Flags the single biggest gap first | Included | Not included | Not included |
| Works before you open an editor or install anything | Included | Not included | Not included |
Copilot and Cursor build rich in-editor context once you're inside the codebase. This tool is a quick, editor-independent pre-check on how much friction to expect before you start.
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