Writing Humanizer

Concept of the skill

Writing humanization is the discipline of turning robotic or AI-patterned prose into clear, direct, reader-trustworthy text while preserving truth and intent — prose-quality repair, not authorship laundering. It works on a fixed set of primitives: AI tells (predictable filler, hedging, passive voice, hollow intensifiers), readability signals (sentence length, paragraph density, jargon load), rhythm signals (opening-word variety, sentence-length variance, paragraph cadence), audience register, and evidence specificity. The skill replaces "make it sound human" as a vague style request with a repeatable five-step edit pass — Tell Scan, Readability Check, Structural Rewrite, Rhythm Pass, Voice Calibration — that moves through those primitives in order: remove the tells, clarify who the actor is, improve readability, vary the rhythm, then calibrate tone to the surface (an API doc, an error message, a release note, a commit). Detector-style signals such as perplexity and burstiness are treated as rough diagnostic clues, never as proof of authorship and never as the target; the reader, not the detector, is the audience. The non-negotiable constraint is honesty: every edit must keep the text at least as clear and as accurate as it was, and the skill refuses to misrepresent authorship or promise detector-proof output.

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
• AI-detector limits and prose fingerprints — how some detectors use probability signals such as perplexity and burstiness, why detector scores are not proof of authorship, and which clarity-preserving edits reduce robotic prose without gaming a review process
• Paragraph rhythm and structure — paragraph-length rules per context, the hook-body-landing pattern, the bullets-vs-prose decision tree
• Anti-patterns — over-qualification, repetitive transitions, the enumeration trap, hollow intensifiers
• The 5-step humanization workflow — Tell Scan, Readability Check, Structural Rewrite, Rhythm Pass, Voice Calibration