What problem does it solve? AI-generated and AI-assisted text carries recognizable statistical fingerprints — negative parallelism ("not X but Y"), em-dash overuse, rule-of-three lists, puffery vocabulary, and uniform sentence cadence — that make prose read as generic and machine-written. This Skill mechanically detects those tells and rewrites the text into its target genre without reintroducing the same patterns. ## Core Features & Use Cases - Mechanical tell detection: Runs a catalog of grep-ready regex patterns against the text to find negative parallelism, inflated vocabulary, hedging, false ranges, and formatting tells, plus structural checks for cadence and skeleton structure. - Meaning-based rewriting with verify loop: Rewrites each finding by asserting the underlying claim rather than paraphrasing the pattern, then re-scans the rewrite until zero pattern hits remain (capped at 4 passes). - Genre register profiles: Adjusts output for academic articles, tweets, reddit posts, LinkedIn, email, blogs, marketing copy, and technical docs using per-genre fatal-tell lists. - Use Case: Before publishing an agent-drafted blog post, run the scan to surface 12 findings (3 negative parallelisms, 5 puffery words, 4 cadence issues), receive a diagnosis table, then get a rewritten draft that passed two verification loops with a change log. ## Quick Start De-slop this draft and rewrite it as a reddit post, showing me the diagnosis table before and after the rewrite.