AI CODE SPRAWL

Half your code is AI-generated and nobody reviewed the architecture.

Coding agents shipped faster than anyone could review the design. Mapnostics reads the structure that actually shipped — module boundaries, dependency hotspots, and a health score across six measured dimensions.

Analyze my codebase →See the Code Map

mapnostics.com/repo/acme-platform/code-map

Code Map

Visualize how files in acme-platform connect and depend on each other

Last scanned Aug 31, 09:31 AMScan Dependencies

System Structure

124 files mapped across 305 dependency connections

Most connected

types.ts (39 connections)

4 files with flagged findings (dead code, zombie, or symbol) or a low health score

types.ts is a high-dependency node — changes here have wide blast radius

Click any node to highlight its connections · click ↗ to open file details

Highest impacttypes.ts39 connections — show it on the map →

Ownership

Who wrote the code, from GitHub blame

31 AugScan blame

112 files · 4 contributors

dana-okafor48 files

sam-whitfield31 files

priya-raman22 files

lee-tanaka11 files

History

How recently each file changed, from commit history

31 AugScan history

118 files · 37 changed in the last 30 days · 9 untouched for over a year

checkout/page.tsx14 of 96 in 30d

orders/route.ts11 of 74 in 30d

CheckoutStepper.tsx9 of 61 in 30d

supabase/server.ts6 of 88 in 30d

CustomerDetailDialog.tsx5 of 40 in 30d

Database

Tables and columns from your migrations, plus sensitive-column candidates

31 AugScan schema

14 tables · 3 sensitive-column candidates

customers.emailPII candidate

customers.phonePII candidate

orders.card_last4Financial candidate

Name-pattern match only, not a compliance assessment — verify each column before treating it as a finding.

Where regulated-shaped data is touched

customers.email4 files

orders.card_last42 files

Where the candidate columns above are actually read or written — not a claim about which regulations apply or whether they're satisfied.

Routes

URLs, methods and middleware coverage from the file tree

9 API routes · 15 pages

2 routes with no recognised auth evidence and no middleware coverage

3 of 9 API routes show rate-limiting evidence

Evidence only — a route with none may throttle through a helper this does not recognise, and this codebase declares no policy that every route must be rate-limited.

Performance patterns

Nested loops and sequential awaits — structural shape, not a speed claim

1 nested loop · 3 sequential await-in-loops

lib/orders/repository.ts:88await at :91

lib/payments/stripe.ts:42await at :44

scripts/seed-media.ts:31await at :33

lib/reports/rollup.ts:57nested at :59

Structural shape only — not a claim either pattern is actually slow. Some sequential loops here are deliberate (rate-limited API calls).

Find a file, filter or hotspotFlagged4Zombie6High impact12Loops4API9Lib40UI74

LIBtypes.ts

lib/types.ts

impact 39intent

health100

TSXpage.tsx

app/checkout/page.tsx

intent

health92

LIBserver.ts

lib/supabase/server.ts

impact 21intent

health100

APIroute.ts

app/api/orders/route.ts

zombie 1loops 2

health64

TSXCheckoutStepper.tsx

components/checkout/CheckoutStepper.tsx

dead 3impact 7

health48

LIBrepository.ts

lib/orders/repository.ts

loops 1intent

health86

+−⤢⛶

FilesModulesCapabilitiesWorkflows

How to track the technical debt AI-generated code leaves behind

✓

The architecture that actually shipped

A dependency graph read from the code as it stands now, so you can see the structure the agents produced — not the one anyone planned.

Learn more →

✓

Find the hotspots first

The most-connected files are ranked, so the parts of the codebase where a change has the widest blast radius are the parts you review first.

Learn more →

✓

A health score across six dimensions

One number from 0 to 100, deducted for dead code, zombie code, oversized files, low test coverage, exposed secrets and duplication — the debt AI volume tends to leave behind.

Learn more →

The Intent Map goes one layer deeper — what each file is for, with a block separating what was measured from what was inferred. Bringing new engineers onto this codebase? See developer onboarding. And pasted code pastes its keys along with it — the secrets detection tool catches them before production.

See your real architecture in under 2 minutes.

Analyze my codebase →

No credit card required · Free tier available

Common questions

How do I review AI-generated code at scale?

Read the dependency graph and health score of the code that actually shipped — the module boundaries, dependency hotspots and dead code — rather than reviewing agent output diff by diff.

Can AI-generated code create technical debt?

Yes, especially when volume outpaces review: duplicated logic, oversized files and dead code accumulate the same way they would from any team, just faster.

How do I find the riskiest parts of an AI-heavy codebase?

The most-connected files in the dependency graph carry the widest blast radius — start your review there.