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 introducing new ones. ## 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: Applies a triage method that fixes each finding by asserting what the sentence actually means rather than paraphrasing the pattern, with a verify loop that re-scans every rewrite until clean. - Genre register profiles: Adjusts the output for academic articles, tweets, reddit posts, LinkedIn, email, blogs, marketing copy, and technical docs using per-genre fatal-tell lists. - Use Case: You drafted a LinkedIn post with an AI assistant and it reads like every other AI post. Run the de-slop pass to strip the stacked one-line paragraphs and "not X but Y" framing, then verify it against the LinkedIn register profile before publishing. ## Quick Start De-slop this draft and rewrite it as a reddit comment, then show me the findings table and change log.
