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Humanizer (Chinese)

by op7418 · op7418/Humanizer-zh

中文文本去 AI 味的金标准 — 大量真实语料调过的规则集，专治公众号/小红书/blog 的机翻腔。

Humanizer-zh is op7418's Chinese-localized fork of the original Humanizer skill, specifically tuned for the patterns that make Claude-generated 中文 sound robotic: redundant connectives, formulaic openings, repetitive sentence rhythm, overly formal register. It rewrites in place, preserving meaning while shifting register toward blog / social-platform conventions.

▶ Watch demo 📦 Install 💡 Use Cases

Why use it

Key features

Live Demo

What it looks like in practice

ready

Install

Author repository · Claude Skills documentation

Use Cases

Real-world ways to use Humanizer (Chinese)

Rewrite an AI-generated blog post in 公众号 voice

👤 中文 content creators ⏱ ~25 min beginner

When to use: Claude wrote a tech post and it reads like a translated whitepaper.

Flow
  1. Identify AI tells
    Use humanizer-zh. Pass 1: scan /drafts/post.md. List the top 10 sentences that scream AI — quote them.✓ Copied
    → Concrete sentence-level diagnoses, not generic feedback
  2. Rewrite
    Now rewrite the whole post in 公众号 voice. Keep technical accuracy; change rhythm and register.✓ Copied
    → Output reads like a person wrote it; no '众所周知' / '总而言之' patterns
  3. Diff review
    Show the diff section by section. I'll flag any over-translation.✓ Copied
    → Clear before/after for each paragraph

Outcome: Post reads natural; passes a reader's BS detector.

Pitfalls
  • Skill softens technical claims (e.g. 'is' → 'might be') — Add to the prompt: 不要软化技术结论, only change register
Combine with: filesystem

Convert a long article into 小红书 笔记 format

👤 小红书 creators ⏱ ~10 min beginner

When to use: You have a 1500-word article and need a 小红书 post that follows the platform's conventions.

Flow
  1. Apply 小红书 profile
    Humanizer-zh: 小红书 mode. Reduce to 600 字, add emoji where natural, break into short paragraphs, add a hooky 标题.✓ Copied
    → Output matches platform tone; no obvious AI emoji-stuffing

Outcome: Ready-to-post 小红书 笔记.

Pitfalls
  • Emoji over-stuffing — Cap at 1 emoji per ~80 字

Humanize a tutorial without breaking code

👤 Tech writers ⏱ ~15 min intermediate

When to use: Tutorials are sensitive — you want voice change but not corrupted code blocks.

Flow
  1. Mark code blocks
    Humanizer-zh: rewrite /tutorials/python.md. Treat all ``` blocks as untouchable. Only rewrite prose.✓ Copied
    → Code untouched; prose rewritten

Outcome: Tutorial reads natural while code stays correct.

Pitfalls
  • Inline code formatted with … also gets rewritten — Explicit prompt: protect inline code too

Combinations

Pair with other MCPs for X10 leverage

humanizer-zh-skill + filesystem

Batch process all drafts in a folder

Humanizer-zh on every .md under /drafts/. Save as /drafts-final/.✓ Copied
humanizer-zh-skill + x-article-publisher-skill

Humanize then publish

Humanize the post, then publish to X via x-article-publisher.✓ Copied

Tools

What this MCP exposes

ToolInputsWhen to callCost
diagnose text Pass 1 — understand what to fix 0
rewrite text, voice_profile? Pass 2 — apply the change 0
diff_review before, after Pass 3 — verify nothing was over-changed 0

Cost & Limits

What this costs to run

API quota
N/A
Tokens per call
Roughly 2x source length (read + rewrite)
Monetary
Free (the skill itself); your model tokens apply
Tip
Diagnose first on the whole doc, but rewrite section-by-section to keep context tight

Security

Permissions, secrets, blast radius

Minimum scopes: filesystem-read filesystem-write
Credential storage: None
Data egress: None (skill is local)

Troubleshooting

Common errors and fixes

Output sounds equally robotic

Specify voice profile; default mode is conservative

Code blocks corrupted

Add explicit 'protect ``` blocks' to prompt

Length drift (output much shorter)

Specify target length explicitly

Alternatives

Humanizer (Chinese) vs others

AlternativeWhen to use it insteadTradeoff
Original Humanizer (English)Source is EnglishGeneric; less tuned for Chinese AI tells
Manual editingStakes are very high (e.g. published whitepaper)Slow; doesn't scale

More

Resources

📖 Read the official README on GitHub

🐙 Browse open issues

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