91% of developers use AI tools. Your repo is accumulating technical debt RIGHT NOW.

The AI Code Quality Crisis

AI is writing your code. Who's checking it?

Studies show that 40-62% of AI-generated code contains security vulnerabilities. Every commit from Copilot, Claude, or ChatGPT could be introducing bugs, security holes, and technical debt into your codebase.

AI code with vulnerabilities

Fails under production load

Increase in code duplication

Code complexity increase

1 in 5 AI code samples reference fake libraries that don't exist

SlopBuster

Your AI code quality guardian

SlopBuster automatically reviews every PR for the 6 pillars of code quality: Security, Performance, Reliability, Cost Optimization, Operational Excellence, and Best Practices. See exactly how it catches what other tools miss.

Pillar 1: Security

Security

AI models frequently generate code with SQL injection, XSS, and other OWASP Top 10 vulnerabilities. SlopBuster catches these before they reach production.

SQL InjectionXSS AttacksHardcoded SecretsAuth Bypass

AI-Generated

2 issues

1# Get user by id

2def get_user(request):

3 user_id = request.args.get('id')

4 sql = "SELECT * FROM users WHERE id = " + user_id

5 return db.execute(sql)

Before: AI-Generated Code

Production-Ready

Fixed

1# Get user by id (repo standard: auth + safe query)

2def get_user(request):

3 user_id = validate_int(request.args.get('id'))

4 require_auth(request) # repo helper

5 return db.query_one(

6 "SELECT id, email, name FROM users WHERE id = :id",

7 {"id": user_id}

8 )

After: Production-Ready

SlopBuster findings:

BLOCKERSQL injection risk

HIGHMissing auth/tenant guard (returns any user)

DETECTED + SUGGESTED PATCH

Why?Uses repo helpers (auth + safe query) and prevents both injection and data exposure.

Security Improved

Production Ready

Pillar 2: Performance

Performance

AI-generated code often looks correct but introduces hidden performance traps. SlopBuster identifies N+1 queries, missing pagination, and inefficient algorithms.

N+1 QueriesMissing IndexesMemory LeaksBlocking Calls

AI-Generated

3 issues

1def get_users_with_orders():

2 users = db.query(User).all()

3 for user in users:

4 user.orders = db.query(Order).filter(

5 user_id=user.id).all() # N+1!

6 return users

Before: AI-Generated Code

Production-Ready

Fixed

1def get_users_with_orders(page: int = 1, size: int = 50):

2 q = (db.query(User)

3 .options(joinedload(User.orders))

4 .limit(size)

5 .offset((page - 1) * size))

6 return q.all()

After: Production-Ready

SlopBuster findings:

HIGHN+1 query pattern

HIGHNo pagination (unbounded .all())

MEDOver-fetching fields

DETECTED + SUGGESTED PATCH

Why?Prevents query explosion + protects API latency as data grows.

Performance Improved

Production Ready

Pillar 3: Reliability

Reliability

AI code often swallows errors or fails silently. SlopBuster ensures proper error handling, retries, and graceful degradation.

Silent FailuresMissing RetriesNo Circuit BreakersPoor Error Messages

AI-Generated

2 issues

1def process_payment(user_id, amount):

2 try:

3 return payment_api.charge(amount)

4 except Exception as e:

5 logger.error(e)

6 return None # Caller can't tell!

Before: AI-Generated Code

Production-Ready

Fixed

1def process_payment(user_id, amount, idempotency_key):

2 try:

3 return retry_with_backoff( # repo helper

4 lambda: payment_api.charge(

5 amount, timeout=3,

6 idempotency_key=idempotency_key),

7 retries=3)

8 except ConnectionError as e:

9 logger.error(f"payment_unavailable user={user_id}")

10 raise ServiceUnavailable() from e

After: Production-Ready

SlopBuster findings:

BLOCKERSilent failure (can't distinguish decline vs outage)

HIGHMissing timeout/retry policy

DETECTED + SUGGESTED PATCH

Why?Retries transient failures safely and keeps downstream behavior deterministic.

Reliability Improved

Production Ready

Pillar 4: Cost Optimization

Cost Optimization

AI assistants default to flagship models for every task. SlopBuster identifies where smaller models and caching deliver equal quality at a fraction of the cost.

Wrong Model SizeNo BatchingMissing CacheRedundant Embeddings

AI-Generated

2 issues

1def classify_ticket(ticket):

2 return client.messages.create(

3 model="claude-sonnet-4-20250514",

4 messages=[{"role": "user",

5 "content": ticket}]

6 ) # No caching, oversized model

Before: AI-Generated Code

Production-Ready

Fixed

1def classify_ticket(ticket_id, ticket_text):

2 cached = cache.get(f"classify:{ticket_id}")

3 if cached:

4 return cached

5 result = client.messages.create(

6 model="claude-haiku-3-5-20241022",

7 messages=[{"role": "user", "content": ticket_text}])

8 cache.set(f"classify:{ticket_id}", result, ttl=86400)

9 return result

After: Production-Ready

SlopBuster findings:

HIGHOversized model for a simple label task

HIGHMissing cache (same tickets get reclassified)

DETECTED + SUGGESTED PATCH

Why?Most cost waste is repeat work, not just model choice.

Cost Optimization Improved

Production Ready

Pillar 5: Operational Excellence

Operational Excellence

AI-generated code rarely includes logging, metrics, or tracing. SlopBuster ensures your code is observable and debuggable in production.

No LoggingMissing MetricsNo TracingPoor Alerting

AI-Generated

3 issues

1def process_order(order_id, items):

2 order = create_order(order_id)

3 for item in items:

4 add_item(order, item)

5 charge_payment(order)

6 send_confirmation(order)

7 # No logging, no tracing!

Before: AI-Generated Code

Production-Ready

Fixed

1def process_order(order_id, items, request_id):

2 logger.info("order_start", order_id=order_id,

3 request_id=request_id)

4 with tracer.start_span("process_order") as span:

5 span.set_attribute("order_id", order_id)

6 try:

7 order = create_order(order_id)

8 for item in items:

9 add_item(order, item)

10 charge_payment(order)

11 metrics.increment("orders_processed")

12 return order

13 except Exception as e:

14 metrics.increment("orders_failed")

15 raise

After: Production-Ready

SlopBuster findings:

HIGHNo structured logging or correlation IDs

HIGHMissing tracing spans for debugging

MEDNo metrics for alerting

DETECTED + SUGGESTED PATCH

Why?This is what lets on-call debug in minutes instead of hours.

Operational Excellence Improved

Production Ready

Pillar 6: Best Practices

Best Practices

AI code often has cryptic naming, code duplication, and poor typing. SlopBuster enforces your team's standards and coding conventions.

Poor NamingCode DuplicationMissing TypesNo Documentation

AI-Generated

3 issues

1def p(d, t): # Cryptic naming

2 if not d or "@" not in d:

3 raise Exception("bad")

4 if not t or len(t) < 2:

5 raise Exception("bad")

6 return {"email": d, "name": t}

Before: AI-Generated Code

Production-Ready

Fixed

1from validators import validate_email, validate_name

3def register_user(email: str, name: str) -> User:

4 """Register a new user after validation."""

5 validate_email(email) # repo shared validators

6 validate_name(name)

7 return User(email=email, name=name)

After: Production-Ready

SlopBuster findings:

HIGHCryptic naming (p, d, t)

HIGHDuplicate validation logic

MEDAmbiguous error messages

DETECTED + SUGGESTED PATCH

Why?Shared validators reduce drift and make behavior consistent.

Best Practices Improved

Production Ready

Stop AI Slop Before It Ships

Every PR reviewed. Every vulnerability caught. Every best practice enforced. Install SlopBuster in 2 minutes and start shipping quality code.

6 Quality Pillars

Instant PR Reviews

Auto-Fix Suggestions

Stop AI Slop Now See Full Demo

Free for open source. No credit card required.

SOC 2 CompliantGDPR ReadyEnterprise Grade