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css-slop-detector is a small command-line tool that scores selected CSS and HTML patterns commonly associated with generic AI-style web design. It reports a score from 0 to 100 and names the patterns it finds. That makes it a heuristic design-review aid—not a detector of AI authorship, and not an objective grade of whether a website is good.
What css-slop-detector checks
In a DEV Community article published October 1, 2026, author hao li describes the project this way: “So I built css-slop-detector: it scores your CSS/HTML from 0 to 100 for AI design cliches, with every finding named.” Its seven named rules are:
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big-radiusglasspurple-gradientglow-shadowpure-blackhero-centerno-light-mode
The example report flags cues such as a 32 px card radius, backdrop blur, a purple or violet gradient, a large centered hero headline, and a dark-only design. These are the project’s chosen signals; they are not a complete inventory of design patterns or a proven recipe for identifying machine-generated work.
According to hao li’s DEV Community announcement, the scanner reads .css and .html files, including CSS in style blocks and inline style attributes. It can produce JSON and can be used as a CI gate with a configured failure threshold. The article describes it as a single-file, standard-library-only project for Python 3.9 or later.
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How to interpret its score
The author’s categories are 0–20, “clean”; 21–50, “mild slop”; 51–80, “slop”; and 81–100, “peak slop.” They are project-defined bands, not an industry standard, and the available material does not show that they have been calibrated against an independent measure of design quality or AI authorship.
A high score means the rules found more of the patterns they target. It does not establish that AI made the site, which AI tool was used, whether a person reviewed the result, or whether the design works for its audience. A purple gradient, rounded cards, dark colors, or a centered headline can all be deliberate choices.
What the project’s evidence does—and doesn’t—show
The DEV announcement reports that all 8 of 8 tests passed and says the project is MIT licensed. The test count is the author’s report about the project’s tests; by itself, it does not demonstrate detection accuracy, broad browser coverage, or independent validation. The available sources provide no published precision, recall, error rate, or correlation with AI authorship.
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Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →That distinction matters if you want to use the score to make decisions. Treat a finding as a prompt to inspect a specific styling choice, not as proof against a designer or a reason to reject a site. If you need evidence of authorship, this tool’s stated capabilities do not provide it.
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How to review a site beyond the score
A more useful design review asks whether the interface fits the actual product and its users, rather than whether it avoids a particular visual style. InterfaceKit’s guide, “What makes a website look AI-generated?”, offers practical questions to consider:
- Product specificity: Does the page reflect what this product actually does, or could its copy and layout describe almost any product?
- Consistency across screens: Do design decisions hold together across routes and tasks, rather than appearing polished on one landing page and improvised elsewhere?
- Incomplete and difficult states: Does the experience account for empty results, errors, loading, or other less-than-ideal situations?
- Concrete language: Does the copy identify a real user, task, or outcome, instead of relying on vague claims?
These checks help assess whether a design feels purposeful and usable. They are review prompts, not proof that AI was involved.
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When a rule-based score is useful
A named finding can make a quick review more actionable than a bare label: a designer can inspect the flagged radius or gradient and decide whether it suits the product. JSON output or a CI threshold may also help teams surface these chosen patterns during development. But an automated gate only enforces the rules and threshold a team configures. It cannot decide whether a particular visual choice is justified by the product or audience.
If you evaluate any site-scoring tool, consider what it reads—CSS, HTML, a rendered page, or source code—which signals it names, whether rules and thresholds can be configured, how its findings are delivered, and whether its claims have independent validation. The sources here describe css-slop-detector alone, so they do not support a ranking against other tools.
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