Your agents are already in production. Is anyone checking their work?
If your team is using any AI coding assistant — Claude Code, Cursor, Codex, Copilot — agents are already writing code that's ending up in your codebase. The question isn't whether to use them. It's whether you have any systematic way to check what they produce before it reaches production.
Most teams don't. They rely on code review, which worked fine when engineers wrote every line themselves. It scales poorly when agents are generating hundreds of lines per session.
That's the gap aislop fills.
What is aislop?
aislop is a free, open-source CLI that scans your codebase and scores it against 50+ rules specifically designed to catch the patterns AI coding agents leave behind. It runs in under a second, requires no LLM at runtime, and works across TypeScript, JavaScript, Python, Go, Rust, Ruby, PHP, and Java.
You run it. You get a score out of 100. You see exactly what's wrong and where.
npx aislop scan
That's it to get started. No account, no config, no setup.
Why does AI-generated code need its own quality gate?
AI-generated code fails differently than human-written code. It compiles. It passes tests. It looks reasonable in a 30-second review. But it has structural tells that accumulate into real problems over time:
Narrative comments that describe what the code does line by line, instead of why — a sign the model was narrating, not documenting. These add noise without adding information.
Swallowed exceptions where the catch block exists but nothing happens inside it. The error is handled in appearance only. In production, it disappears silently.
Generic naming — data2, result, temp — that makes sense in isolation but makes a whole file harder to read and reason about as the codebase grows.
as any and @ts-ignore in TypeScript where the agent hit a type complexity it didn't want to resolve. These aren't just style issues — they actively remove the safety guarantees your type system is supposed to provide.
TODO stubs left where the hard logic should be. The function exists. The important part doesn't.
None of these are bugs your tests will catch. They're quality problems that accumulate quietly until your codebase is meaningfully harder to maintain — and until the agents reading that code to generate their next PR are learning from bad examples.
The three concrete benefits
1. You stop relying on reviewer attention to catch mechanical issues
When aislop runs before review, the structural problems are already surfaced. Reviewers can focus on logic, architecture, and business context — the things only a human can evaluate — instead of hunting for unused imports and TODO stubs in a 400-line diff.
2. You set one consistent standard across every agent on your team
Different engineers prompt differently. Different agents produce differently. Without a shared quality gate, your codebase standards depend on whoever reviewed a given PR on a given day. aislop gives you one bar, enforced consistently on every merge.
3. You close the loop — sending bad code back to the agent that wrote it
When aislop finds something it can't auto-fix, npx aislop fix --claude hands the findings directly back to Claude Code, Codex, or Cursor for a second pass. The agent fixes its own output before a human reviews it. You get cleaner PRs without adding reviewer time.
How to set it up
Step 1: Run a baseline scan
npx aislop scan
This scores your current codebase, grouped by engine (formatting, linting, code quality, AI slop, security). Whatever number comes back is your starting point.
Step 2: Auto-fix the easy stuff
npx aislop fix
This applies all the mechanical fixes — unused imports, trivial comments, formatting issues — and re-scans so you see the new score. Anything requiring judgment is left for you or your agent.
Step 3: Gate it in CI
npx aislop init
The interactive wizard writes a .aislop/config.yml and optionally drops a GitHub Actions workflow file. Set your score threshold. From that point on, every PR runs through the gate automatically and can't merge below your bar.
Total setup time: under ten minutes.
The bottom line
AI coding assistants have made execution faster. They haven't made judgment automatic. aislop sits between the two — catching what agents consistently get wrong before it reaches your reviewers, your users, or the agents writing the next PR.
Your agents are already writing production code. Make every PR meet your standard.