anti-slop

Find and repair substance defects in AI-assisted prose, code, documentation, and agent output.

It reports defects. It never reports authorship.

Why this is not another AI detector or humanizer

Most tools in this space do one of two things, and both are broken.

Detectors guess who wrote something. They are unreliable and their failures land on identifiable people. Sixteen detection models disproportionately flagged English-language-learner essays, and non-White ELL students more than White ELL peers, while human annotators on the same essays showed no significant demographic bias (Stowe et al., ACL 2026). OpenAI withdrew its own classifier at 26 percent true positive and 9 percent false positive.

Humanizers strip the surface tells. The Wikipedia guide that most of this field derives from warns against exactly that, in bold:

The patterns listed here are also only potential signs of a problem, not the problem itself. Please do not merely treat these signs as the problems to be fixed; that could just make detection harder.

The measurement agrees. "All humanizers tend to degrade the quality of the original text": best-tier tools win a fluency comparison against the original only 26.0 percent of the time (DAMAGE, COLING 2025).

And you cannot just ask a model. Agreement between LLM judges and human slop labels is kappa 0.01 for GPT-5, minus 0.01 for DeepSeek-V3, and 0.03 for o3-mini, which is chance (Shaib et al., arXiv 2509.19163). Worse, judges are biased toward slop features: GPT-4 preferred model-written pitches 89 percent of the time against human raters at 36 percent (PNAS 122(31)).

So this project never asks whether text "looks like" slop. Every check is a mechanical procedure that emits an artifact you can inspect.

How it works

Three layers, and only the bottom two may fail a build.

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flowchart LR
    IN["Draft, diff, docs,agent output"]
    L0["Layer 0deterministic scanners"]
    L2["Laye