SlopCop: Forensics for your codebase

Sign in<br>Subscribe

SlopCop started from a familiar problem: agents can write code quickly, but they do not always write code that belongs in the repo.<br>They miss local conventions. They recreate helpers that already exist. They add comments nobody asked for. They produce code that works today but is awkward to read, hard to extend, or worse than it needs to be at scale.<br>That is the “slop” SlopCop looks for: code that adds technical debt; that quietly makes the next change a little bit harder, and the next after that.<br>SlopCop's goal is to make agents part of the solution that they're causing. It sends specialist agents into the codebase with static analysis tools, then turns the findings into a case file: complexity hotspots, duplicated structures, weak tests, failure handling that hides problems, out of date or unhelpful comments. SlopCop concludes with concrete recommendations for what to fix next.<br>When to use SlopCop<br>SlopCop can be fun to run on a repo just to see how far up it lands on the Most Wanted wall. It is good for curiosity, comparisons, and a bit of bragging rights.<br>But it is also meant to be useful. If you are joining a project, maintaining an older codebase, or reviewing AI-generated changes, SlopCop gives you a structured read on maintainability risk.<br>Later in this post, we will get into how the findings work, how the recommendations are produced, and what shows up in the case file. The short version is that a good scan should leave you with concrete moves: split this module, delete this unused layer, tighten this test, reuse this helper, stop swallowing this error, add this lint rule, or write this down in AGENTS.md so the next agent does not repeat the same mistake.<br>You can treat it as a wall of shame, a scorecard, or a serious report. The useful part is that it points you toward the changes most likely to make the codebase easier to live with.<br>The Most Wanted Wall<br>The Most Wanted wall is a public slop scoreboard where you can "show off" your slop score:<br>Public scans can be pinned as case files, ranked by score, and opened as dossiers. Each entry shows the repo, verdict, summary, score, and supporting report.<br>Yes, it's primarily in fun. Who doesn't love a leaderboard?<br>How we built it<br>Linters or static analysis can give false positives or negatives, but they are deterministic and backed by some well-defined policy that matches code evidence. An LLM is intelligent, can present findings in a persuasive paragraph claiming one thing or another, but whether that is true or not is difficult to say without evidence.<br>SlopCop uses both: static analysis finds leads, agents verify the surrounding code, and the final report gives recommendations a developer or another agent can actually use.<br>We tuned SlopCop by running it against real repositories, looking at where it failed, and tightening the prompts, routing, and scoring until the results were harder to fool. We benchmarked different step limits, model setups, and report shapes, then checked whether the output actually held up when humans reviewed the evidence.<br>Don't we all love a good dashboard?There’s a slightly meta part to this where in some cases the job starts to look like a reverse Turing test: can we tell whether this mess came from a person, a model, or both? We audited the sniff test the same way we audited the rest of the system; if a report sounded confident but wasn’t precise, or if the scoring rewarded the wrong thing, we changed the rubric. The report needs to be useful, and it needs to acknowledge where the evidence is weak.<br>Each finding is accompanied by a concrete metric, and examples in files linked back to the repository.If the next step is going back to an LLM, the prompt should already know which files matter, which pattern was suspicious, and what kind of fix would help. Acknowledging this led to the creation of the “Copy Recommendation as Prompt” feature. Sometimes that means code changes. Sometimes it means a lint rule, a CI check, or an AGENTS.md entry that tells future agents which utilities and conventions to use.<br>Based on the concrete findings, the agent will give a recommendation which can be copied to your clipboard as a prompt.Why Specialists?<br>We found that a single reviewing agent, even with a deep bag of tools, tends to smooth every issue into the same advice. SlopCop splits the work across specialists because different kinds of slop need different instincts.<br>For large repositories, this split also helps us to scale. A single "do everything" agent can be slow, lead to "out of context" errors, and even cause hallucinations. Breaking up work is a way to parallelise bite-sized investigations into multiple lanes. Some of the agents SlopCop uses include:<br>The Cognitive Complexity agent looks for code that is hard to hold in your head: nested branches and tangled control flow that make change risky.<br>The Size and Sprawl agent looks for long methods, god objects, oversized modules, and...