Vibe Coding Cleanup Specialist Fix AI-Generated Code Issues

You built your app with Cursor and Claude. It works locally. Now it’s in production and things are breaking. A vibe coding cleanup specialist fixes AI-generated code issues by running systematic audits applying automated refactoring and hardening CI gates so the next deploy does not wake you up at 3 AM. I have seen this pattern dozens of times. The code looks clean on the surface. Under the hood it leaks connections ignores timeouts and trusts user input.

What are the common anti-patterns in AI-generated backend code

The most frequent anti-pattern is missing timeouts on outbound calls. The model writes httpx.get(url) and moves on. In production that call hangs forever and exhausts your worker pool. I have been bitten by this exact issue on a payment webhook handler. The fix is a one-liner but you have to know to look for it.

# AI wrote this
async def fetch_user_data(user_id: str) -> dict:
    async with httpx.AsyncClient() as client:
        resp = await client.get(f"https://api.example.com/users/{user_id}")
        return resp.json()

# Production needs this
async def fetch_user_data(user_id: str) -> dict:
    timeout = httpx.Timeout(connect=2.0, read=5.0)
    async with httpx.AsyncClient(timeout=timeout) as client:
        resp = await client.get(f"https://api.example.com/users/{user_id}")
        resp.raise_for_status()
        return resp.json()

Other patterns I see weekly. Raw SQL built with f-strings. Pydantic models with extra="allow" that silently swallow typos. Broad except Exception blocks that hide bugs. Async functions that call blocking libraries like requests or boto3 without to_thread. The AI does not know your infrastructure constraints. It writes code that passes a smoke test.

How to run a systematic audit checklist for security and performance

Start with a checklist you can run in 30 minutes. I keep a markdown file in every repo called AUDIT.md. It forces me to check the same things every time.

Timeouts and retries on every external call. Database queries included.
Input validation at the boundary. FastAPI depends on Pydantic. Use model_config = ConfigDict(extra="forbid") on every request model.
Secrets management. No keys in code. No keys in .env committed to git. Check with git log --all --full-history -- "**/.env".
Connection pooling configured for your actual load. The default SQLAlchemy pool size of 5 kills throughput under burst traffic.
Logging structure. JSON logs with request_id correlation. No print statements.
Rate limiting on public endpoints. Even internal endpoints need guards.

Run bandit -r . and semgrep --config=auto . as a baseline. They catch the obvious security issues. For performance, enable slow_query_log in Postgres and watch for sequential scans on tables the AI created without indexes. I wrote a deeper dive on this process in Fix AI Generated Code: Practical Steps for Backend Engineers.

Can automated refactoring workflows with AST tooling help

Yes. Once you identify a pattern you can fix it across the codebase with libcst or ruff custom rules. I use libcst to add missing timeouts to every httpx.AsyncClient instantiation.

import libcst as libcst
from libcst.metadata import PositionProvider

class AddTimeoutTransformer(libcst.CSTTransformer):
    METADATA_DEPENDENCIES = (PositionProvider,)

    def leave_Call(self, original_node, updated_node):
        if (
            isinstance(updated_node.func, libcst.Attribute)
            and updated_node.func.attr.value == "AsyncClient"
            and not any(
                kw.arg.value == "timeout" for kw in updated_node.keywords
                if isinstance(kw.arg, libcst.Name)
            )
        ):
            timeout_arg = libcst.Arg(
                keyword=libcst.Name("timeout"),
                value=libcst.Call(
                    func=libcst.Attribute(
                        value=libcst.Name("httpx"),
                        attr=libcst.Name("Timeout"),
                    ),
                    args=[
                        libcst.Arg(keyword=libcst.Name("connect"), value=libcst.SimpleNumber("2.0")),
                        libcst.Arg(keyword=libcst.Name("read"), value=libcst.SimpleNumber("5.0")),
                    ],
                ),
            )
            return updated_node.with_changes(keywords=[*updated_node.keywords, timeout_arg])
        return updated_node

Run it with python -m libcst.tool codemod add_httpx_timeout.py .. It touches 40 files in seconds. Review the diff. Commit. The same approach works for adding raise_for_status calls, fixing extra="allow" to extra="forbid", and replacing requests with httpx in async contexts. Trade-off: codemods can introduce subtle bugs if your pattern matching is too greedy. Always run the full test suite after each codemod run.

What test generation strategies work for untested AI code

You have 0% coverage. Writing tests by hand takes weeks. Use the AI to generate the first pass then harden them. My workflow:

Export the OpenAPI schema from your running FastAPI app: curl http://localhost:8000/openapi.json > openapi.json.
Feed it to a test generator. I use a local script that calls schemathesis to generate property-based tests.
Run in CI with --hypothesis-max-examples=100 per endpoint.
Capture failures as fixtures. When schemathesis finds a 500, save the request payload as a pytest fixture.
Write the regression test by hand for that specific fixture.

import schemathesis
from hypothesis import settings

schema = schemathesis.openapi.from_path("../../openapi.json")

@schema.parametrize()
@settings(max_examples=100, deadline=None)
def test_api_contract(case):
    response = case.call_wsgi(app)
    case.validate_response(response)

This catches the crash bugs fast. It does not catch business logic bugs. For those you still need to write scenario tests. The AI can draft them if you give it a few examples of your existing test style. Paste three hand-written tests into the prompt. Ask for five more following the same patterns. Review every line.

How should CI/CD gates be configured to prevent regression

The gate must fail fast and loud. My minimal GitHub Actions workflow for a FastAPI service:

name: CI
on: [push, pull_request]
jobs:
  lint-and-test:
    runs-on: ubuntu-latest
    services:
      postgres:
        image: postgres:16
        env: { POSTGRES_PASSWORD: test }
        ports: [5432:5432]
        options: >-
          --health-cmd "pg_isready"
          --health-interval 10s
          --health-timeout 5s
          --health-retries 5
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with: { python-version: "3.12" }
      - name: Install deps
        run: |
          pip install -r requirements.txt
          pip install ruff bandit schemathesis
      - name: Ruff check
        run: ruff check . --output-format=github
      - name: Bandit security scan
        run: bandit -r . -f json -o bandit.json || true
      - name: Run tests
        run: pytest -x -q --cov=app --cov-fail-under=80
      - name: Schemathesis contract tests
        run: schemathesis run --hypothesis-max-examples=50 openapi.json
      - name: Upload bandit results
        uses: actions/upload-artifact@v4
        with: { name: bandit-report, path: bandit.json }

Key settings: ruff check blocks merge on any lint error. pytest --cov-fail-under=80 enforces a floor. schemathesis runs contract tests against the real OpenAPI spec. The || true on bandit lets you see findings without blocking the first few runs. Once clean, remove || true and fail on high-severity findings. This pipeline catches the regressions the AI introduces when it rewrites a handler next week. For a more complete security gate checklist see ai generated code platform security checklist.

When not to use this approach

If your app is a throwaway prototype that will be rewritten in a month, skip the codemods and contract tests. Just add timeouts and validation manually. If the team is two people and you have zero test infrastructure, start with ruff and bandit in pre-commit hooks. CI can wait. The goal is not perfect code. The goal is code that fails visibly and predictably so you can iterate.

FAQ

How long does a vibe coding cleanup specialist engagement take

Most cleanup engagements run two to four weeks. The first week is audit and quick wins. The second week is codemods and test infrastructure. Remaining time is hardening CI and knowledge transfer.

Can I fix AI-generated code issues without hiring a specialist

Yes. Follow the audit checklist above. Run bandit and schemathesis yourself. Write codemods for your top three patterns. The specialist accelerates the process and catches the patterns you do not know to look for.

What is the biggest risk of leaving AI code unaudited

Silent data corruption. The AI uses extra="allow" on a Pydantic model. A typo in a field name passes validation and writes garbage to the database. You discover it three months later when reports break.

Does this apply to frontend code too

The principles apply. The tooling changes. Use eslint with typescript-eslint instead of ruff. Use playwright for contract testing instead of schemathesis. The audit mindset is identical.

Key Takeaways

AI code works locally and fails in production due to missing timeouts validation and connection management.
A 30-minute audit checklist catches 80% of the high-severity issues.
AST codemods fix systematic patterns across the codebase in minutes.
Contract testing with schemathesis gives you coverage fast without hand-writing thousands of tests.
CI gates must enforce lint security scan coverage and contract tests on every push.
If you want hands-on help implementing this pipeline I am available for short-term engagements.