writing-humanizerlisted

# Writing Humanizer ## Coverage The full pipeline for transforming AI-generated or robotic text into clear, human-sounding prose: - **AI-tell detection and removal** — Tier 1 zero-tolerance word list (`delve`, `testament`, `crucial`, `vital`, `paramount`, `furthermore`, `seamless`, `robust`, `comprehensive`, `cutting-edge`, `foster`, `empower`, `leverage`, `harness`, etc.); Tier 2 conditional list (`utilize`, `facilitate`, `streamline`, `implement`, `optimize`) - **Active-voice conversion** — passive-to-active decision tree based on actor knownness; hedging-pattern removal table - **Readability scoring as diagnosis** — Flesch-Kincaid Grade (target 8–10 for general docs), Gunning Fog Index (10–12 for technical docs), Flesch Reading Ease (60–70 for UI copy); the readability diagnostic tree for sentence length, word complexity, paragraph density, and nested clauses - **Sentence variety and rhythm** — the 3-beat short-long-medium pattern; sentence-structure variety checklist (declarative, compound, conditional, question+answer, fragment); opening-word rotation rule - **Vocabulary diversity** — repeat technical terms exactly, rotate generic verbs, avoid elegant variation; the abstract-vs-concrete table; the jargon decision tree - **Tone mapping** — the formal-to-casual spectrum (1–5) and a context-tone table covering API documentation, error messages, UI tooltips, commit messages, PR descriptions, release notes, issue bodies, onboarding copy, empty states, and marketing copy -