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Humanizer Ultra Optimized

Content Creation Updated 2026.08.30

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Please follow https://skillhub.cn/install/skillhub.md and install @laoxi/humanizer-ultra.

About this skill

Problem

AI writing often sounds smooth but artificial: formulaic not A, but B structures, stacked adverbs, pseudo-precise numbers, forced parallels, and a teacherly tone can drain reader attention. This skill treats the issue as style, not grammar: it preserves facts, intent, and voice anchors, then rewrites only the delivery to make text concrete, natural, and readable.

How It Works

  • Scene framing: identify who is speaking, who it is for, and the human-sounding anchors in the source.
  • Fingerprint scan: check not A, but B, stacked intensifiers, weak metaphors, punctuation issues, and structural clichés.
  • Severity grading: classify light, medium, and heavy cases using banned-word density, parallel segments, and internal-state wording.
  • Three-pass edit: de-generalize, de-formalize, and re-inject naturalness, then apply L1-L4 quality checks.
    For hard paragraphs, it probes role, context, substantive information, domain vocabulary, and emotional specificity.

Boundaries

It optimizes only the same language and does not translate. It does not overwrite source files without confirmation. Data, names, dates, and stated facts are preserved; suspicious claims are flagged first. It avoids inventing sources and marks unstable sections with [needs review].

Use Cases

  • Before shipping a product note, rewrite an AI draft to remove clichés, forced parallels, and generic metaphors.
  • Review an English blog post for `not A, but B`, weak analogies, and pseudo-precise claims, then edit sentence by sentence.
  • Check a web-novel chapter for OOC dialogue tags and internal monologue, then externalize emotional descriptions.
  • Run an L1 banned-word and punctuation pass on a mixed-language document before publishing.

Best For

  • Content operations owners who need to turn AI drafts into publishable, human-sounding product copy.
  • Engineers writing English technical blogs who want to remove `not A, but B` and vague jargon while keeping facts.
  • Web-novel writers or editors checking dialogue tags, internal monologue, and OOC without changing plot or setup.
  • Bilingual content editors enforcing L1 banned-word, punctuation, and structural cliché checks before release.