Score any prose 0-100 on how human it reads (100 = reads human) by counting six families of style tells: LLM word/phrase density, em-dash density, formulaic structures, sentence-length uniformity, lack of specifics, and over-perfect bullet lists. Returns the score, a verdict, per-dimension sub-scores with the raw counts that produced them, and up to 6 concrete fixes. Fully deterministic and local: no LLM call, no network, same input always gives the same number. This measures STYLE, not authorship — it is a tells counter, not an AI detector.

Score a landing-page hero (headline + sub-headline + call-to-action button) 0-100 across five weighted dimensions: Anti-hype (25), Specificity (25), Clarity (25), Headline shape (13), CTA (12). Returns the score, a verdict, per-dimension sub-scores with raw counts, short flags, and up to 6 targeted rewrites. Deterministic, no LLM, no network. This is the same engine used to score the public 239-page dataset exposed by get_slop_stats, so scores are directly comparable to real landing pages.

Return the reference distribution for grade_landing_copy: 239 real landing pages (min 41, median 79, mean 80.1, 19 perfect scores), how often each tell fires across them, the 10 lowest-scoring domains, and the score distribution. Use it to tell a user whether their score is actually good. Static local data, no network.