# AI Report Generator for Codebase Onboarding & Risk URL: https://codebasechat.com/tools/ai-report-generator Type: tool Locale: en Published: 2026-07-01 Updated: 2026-07-03 --- > Generate an onboarding-time and risk estimate for any codebase in seconds - lines of code, test coverage, team size, and age. No signup, no server calls. ## An AI report generator for codebase onboarding and risk Plug in lines of code, test coverage, team size, and age. Get a plain-English report you can paste into a PR description or a Slack thread - no signup, no server round-trip. ## AI Codebase Report Generator Enter your codebase's stats below. The report updates live and you can copy it as plain text. *[Interactive widget — see the live page for the full experience]* ## What goes into the estimate ### Size and coverage Onboarding time scales with lines of code, and gets noticeably worse below 50% test coverage - reviewers and new hires end up reading implementation line by line instead of trusting the suite to tell them what still works. ### Team bandwidth Teams under 5 active contributors have no informal safety net - a question with no obvious owner sits in a DM for a day. Past about 15 contributors, mentorship bandwidth stops being the bottleneck and the codebase itself takes over as the limiting factor. ### Age and typing Codebases past 3-7 years accumulate tribal knowledge that never made it into comments or docs - naming conventions, deprecated modules nobody deleted, workarounds for a bug that got fixed elsewhere. Static typing (TypeScript, Go, Rust, Java) trims ramp time versus dynamic languages because the types double as documentation. ## Read the room before you assign the ticket A risk score is a starting point for a conversation with your team about where the real bottlenecks are, not a verdict carved in stone - use it to decide what to fix before the next hire starts, not to rank engineers. ## Common questions ### Is this free to use? Yes. The report generator runs entirely in your browser - there is no signup, no paywall, and no data sent to a server to compute the estimate. ### Where do these benchmarks come from? The formula is a transparent heuristic, not a peer-reviewed model. The review-size guidance (keep PRs under roughly 400 lines, effectiveness drops after about 60 minutes of continuous reading) comes from Google's engineering practices documentation and the SmartBear/Cisco code review study. The size of the coverage and tech-debt penalties is informed by Stripe's Developer Coefficient report, which found engineers spend a large share of their week dealing with bad code and maintenance rework. We call it a heuristic on purpose - treat the number as directional, not exact. ### Is my codebase data sent anywhere? No. Every input stays in your browser and the calculation runs in JavaScript on your device. The only network activity is an anonymous tool-run ping used to count how often the widget gets used, with no codebase details attached. ### Does this replace a real onboarding plan? No. It gives you a starting number to plan against - a rough day count and a risk level. Pair it with an actual onboarding checklist, an assigned mentor, and a first-week reading list built from your own repo. ### Why did my risk score come out higher than I expected? Three factors drive it up fastest: test coverage under 30%, a codebase older than 7 years, and a high lines-of-code-per-engineer ratio. Check those three first - they are usually the ones you can actually fix this quarter. ### What if my repo is a monorepo with several languages? Run the numbers for the language and area a new hire will actually touch first, not the whole monorepo. A 2-million-line monorepo where a junior only works in one 40K-line service should be scored on that service, not the entire repo. ### Can I use this for code review capacity planning too? Partially. The risk score correlates with review burden - a high-risk codebase usually means slower, more expensive reviews - but it is not a substitute for actually measuring your team's PR cycle time in your own metrics dashboard. ### How is the risk score different from the onboarding estimate? Onboarding time answers how long until one new engineer is productive. The risk score answers how fragile the codebase is in general - low test coverage and an aging, concentrated codebase raise risk even for engineers who have already been there for years. ## Turn this report into an onboarding deck Skywork turns rough notes into slides, docs, and diagrams - handy for the onboarding packet nobody has time to design by hand when a new engineer starts next Monday. *Call to action: Try Skywork* ## FAQ ### Is this free to use? Yes. The report generator runs entirely in your browser - there is no signup, no paywall, and no data sent to a server to compute the estimate. ### Where do these benchmarks come from? The formula is a transparent heuristic, not a peer-reviewed model. The review-size guidance (keep PRs under roughly 400 lines, effectiveness drops after about 60 minutes of continuous reading) comes from Google's engineering practices documentation and the SmartBear/Cisco code review study. The size of the coverage and tech-debt penalties is informed by Stripe's Developer Coefficient report, which found engineers spend a large share of their week dealing with bad code and maintenance rework. We call it a heuristic on purpose - treat the number as directional, not exact. ### Is my codebase data sent anywhere? No. Every input stays in your browser and the calculation runs in JavaScript on your device. The only network activity is an anonymous tool-run ping used to count how often the widget gets used, with no codebase details attached. ### Does this replace a real onboarding plan? No. It gives you a starting number to plan against - a rough day count and a risk level. Pair it with an actual onboarding checklist, an assigned mentor, and a first-week reading list built from your own repo. ### Why did my risk score come out higher than I expected? Three factors drive it up fastest: test coverage under 30%, a codebase older than 7 years, and a high lines-of-code-per-engineer ratio. Check those three first - they are usually the ones you can actually fix this quarter. ### What if my repo is a monorepo with several languages? Run the numbers for the language and area a new hire will actually touch first, not the whole monorepo. A 2-million-line monorepo where a junior only works in one 40K-line service should be scored on that service, not the entire repo. ### Can I use this for code review capacity planning too? Partially. The risk score correlates with review burden - a high-risk codebase usually means slower, more expensive reviews - but it is not a substitute for actually measuring your team's PR cycle time in your own metrics dashboard. ### How is the risk score different from the onboarding estimate? Onboarding time answers how long until one new engineer is productive. The risk score answers how fragile the codebase is in general - low test coverage and an aging, concentrated codebase raise risk even for engineers who have already been there for years.