AI Code Health Check

AI 代码体检 — your coding agent wrote it. Who checks it?

Paste a public GitHub repository and get a five-dimension health score for its frontend code — the failure modes AI coding tools are known for: giant functions, duplicated logic, zero tests. No install, no sign-up, no API keys.

Try an example:

Public frontend repositories only (JS/TS/React/Next/Vue). Archives up to 20MB and 3,000 files. Scanning usually takes a few seconds.

Sample report — we scored this site first

This is the real scan of the repository that serves this page. It is not a flattering score, and that is the point.

XingMXTeam/maoxunxing-ai

22 source files · static analysis only

Watch66/100
Structure
0
Data
100
Logic
100
UI
88
E2E / Tests
40

structure is the weakest dimension at 0/100 — Function spans 626 lines (limit 50). Long generated functions are where bugs hide.

Fix first — biggest points at stake

  • function-too-long×23138 pts at stake
  • low-test-presence×160 pts at stake
  • file-too-long×648 pts at stake

Issues by dimension

Structure · 3

  • function-too-long Function spans 626 lines (limit 50). Long generated functions are where bugs hide.app/xiaohongshu-cover-maker/CoverMaker.tsx:39
  • file-too-long File has 1391 lines (limit 300). Split into smaller modules, each with a single responsibility.app/xiaohongshu-cover-maker/layouts.ts:301
  • function-too-long Function spans 396 lines (limit 50). Long generated functions are where bugs hide.app/habit-tracker-printable/HabitTrackerTool.tsx:79

UI · 1

  • raw-hex-colors 68 raw hex colors outside design-token files (limit 3). A raw hex looks right and silently forks the palette — use a token.app/habit-tracker-printable/HabitTrackerTool.tsx

E2E / Tests · 1

  • low-test-presence Only 0/22 source files have a sibling test (target ratio 0.5).

What the five dimensions measure

Structure
Oversized files and functions, tangled architecture — the shape AI-generated code rots into first.
Data
Hardcoded secrets, insecure URLs, un-namespaced storage keys — the mistakes agents make most confidently.
Logic
Leftover console.log, TODO pile-ups, forbidden patterns — unfinished work pretending to be done.
UI
Raw hex colors bypassing design tokens and basic accessibility discipline.
E2E / Tests
How much of the code has a sibling test. Untested generated code is a promise, not a result.

Total scores are banded for a quick read: 85+ Healthy · 65–84 Watch · 40–64 At risk · below 40 Critical. The band is a summary, not a grade — the five dimensions are the report.

Honest limits

Questions

Is the AI Code Health Check free?
Yes. Checking a public GitHub repository on this page is free, with no sign-up, no install and no API keys. The same scanner is built into the open-source frontend-harness-cli, which is also free to run locally or in CI.
How does the check work?
Paste a public GitHub repository URL. The service downloads a snapshot of the default branch, reads the source files as text, and scores five dimensions — structure, data, logic, UI and test presence — from deterministic rules. You get a total score, the weakest dimension, and the top issues with file and line references.
Is my code executed or stored?
No. The scan is purely static: repository code is never executed, compiled or installed, and the downloaded snapshot is deleted as soon as the report is generated. Nothing about your code is kept. The page only accepts public repositories, so it never asks for access to anything private.
Which repositories can it check?
Public GitHub repositories with frontend code (JavaScript, TypeScript, React, Next.js, Vue). The web version accepts snapshots up to 20 MB and 3,000 files; larger repositories should use the CLI locally, which has no such limits.
How is the score calculated?
Each dimension starts at 100 and loses points for concrete findings — for example oversized files and functions in structure, hardcoded secrets in data, leftover console.log statements in logic, raw hex colors in UI, and missing sibling tests in E2E. The rules and thresholds are the ones in the open-source CLI, so the score is fully reproducible: run it locally and you will get the same numbers.
Does a low score mean my code is bad?
Not by itself. The score is a diagnostic signal, not a quality verdict. It shows where AI-generated code tends to rot first — giant functions, duplicated patterns, zero tests — so you know where to look. Well-kept generated code scores high; the sample on this page scores 66 because we publish our own result, good or bad.
Can it check a private repository?
Not on this page, by design — it never asks for your GitHub credentials or access tokens. For private code, run the open-source frontend-harness-cli on your own machine, where the code never leaves your computer.
How is this different from SonarQube or an AI code reviewer?
Those tools usually review pull-request diffs and need repository access and setup. This check scores the health of a whole frontend repository in one paste, with zero setup, and its rules target the specific failure modes of AI-generated code. It answers a different question: not "is this diff OK?" but "is this codebase staying maintainable as an agent keeps writing it?"
My score is low. What should I do next?
Start with the weakest dimension in your report — the top issues are listed with file and line references, and fixing the worst few usually moves the score most. To stop the score sliding back as your coding agent keeps working, the frontend-harness-cli adds rubric gates that check every change before it lands.