Humanize Chinese

Humanize Chinese

Use this skill when you need to detect AI-like Chinese writing, rewrite it to feel less synthetic, reduce AIGC signals in academic prose, or convert the text into a more specific Chinese writing style.

When to Use

• Use when the user says 去AI味, 降AIGC, 去除AI痕迹, 让文字更自然, 改成人话, or 降低AI率
• Use when the user wants a Chinese text checked for AI-writing patterns or suspicious phrasing
• Use when the user wants academic-paper-specific AIGC reduction for CNKI, VIP, or Wanfang-style checks
• Use when the user wants Chinese text rewritten into a different style such as zhihu, xiaohongshu, wechat, weibo, literary, or academic

Core Workflow

1. Detect Before Rewriting

Start by identifying the most obvious AI markers instead of rewriting blindly:

• rigid first/second/finally structures
• mechanical connectors such as 综上所述, 值得注意的是, 由此可见
• abstract grandiose wording with low information density
• repeated sentence rhythm and paragraph length
• academic prose that sounds too complete, too certain, or too template-driven

If the user provides a short sample, call out the suspicious phrases directly before rewriting.

2. Rewrite in the Smallest Useful Pass

Prefer targeted rewrites over total regeneration:

• remove formulaic connectors rather than paraphrasing every sentence
• vary sentence length and paragraph rhythm
• replace repeated verbs and noun phrases
• swap abstract summaries for concrete observations where possible
• keep the original claims, facts, citations, and terminology intact

3. Validate the Result

After rewriting, verify that the text:

• still says the same thing
• sounds less templated
• uses more natural rhythm
• does not introduce factual drift
• stays in the correct register for the target audience

For academic text, preserve a scholarly tone. Do not over-casualize.

Optional CLI Flow

If the user has a local clone of the source toolkit, these examples are useful:

python3 scripts/detect_cn.py text.txt -v
python3 scripts/compare_cn.py text.txt -a -o clean.txt
python3 scripts/academic_cn.py paper.txt -o clean.txt --compare
python3 scripts/style_cn.py text.txt --style xiaohongshu -o out.txt

Use this CLI sequence when available:

1. detect and inspect suspicious sentences
2. rewrite or compare
3. rerun detection on the cleaned file
4. optionally convert into a target style

Manual Rewrite Playbook

If the scripts are unavailable, use this manual process.

Common AI Markers

• numbered or mirrored structures that feel too symmetrical
• filler transitions that add no meaning
• repeated stock phrases
• overly even sentence length
• conclusions that sound final, polished, and risk-free

Rewrite Moves

• delete weak transitions first
• collapse repetitive phrases into one stronger sentence
• split sentences at natural turns instead of forcing long balanced structures
• merge choppy sentences when they feel robotic
• replace generic abstractions with concrete wording
• introduce light variation in cadence so the prose does not march at a constant tempo

Academic AIGC Reduction

For papers, reports, or theses:

• keep discipline-specific terminology unchanged
• replace AI-academic stock phrases with more grounded scholarly phrasing
• reduce absolute certainty with measured hedging where appropriate
• vary paragraph structure so each section does not read like the same template
• add limitations or uncertainty if the conclusion feels unnaturally complete

Examples of safer direction changes:

• 本文旨在 -> 本文尝试 or 本研究关注
• 具有重要意义 -> 值得关注 or 有一定参考价值
• 研究表明 -> 前人研究发现 or 已有文献显示

Do not invent citations, evidence, or data.

Style Conversion

Use style conversion only after the base text is readable and natural.

Supported style directions from the source workflow:

• casual
• zhihu
• xiaohongshu
• wechat
• academic
• literary
• weibo

When switching style, keep the user's meaning stable and change only tone, structure, and surface wording.

Output Rules

• Show the main AI-like patterns you found
• Explain the rewrite strategy in 1-3 short bullets
• Return the rewritten Chinese text
• If helpful, include a short note on remaining weak spots

Source

Adapted from the voidborne-d/humanize-chinese project and its CLI/script workflow for Chinese AI-text detection and rewriting.

🧠 AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Qdrant Memory Integration

Before executing complex tasks with this skill:

python3 execution/memory_manager.py auto --query "<task summary>"

• Cache hit? Use cached response directly — no need to re-process.
• Memory match? Inject context_chunks into your reasoning.
• No match? Proceed normally, then store results:

python3 execution/memory_manager.py store \\
  --content "Description of what was decided/solved" \\
  --type decision \\
  --tags humanize-chinese <relevant-tags>

Agent Team Collaboration

• This skill can be invoked by the orchestrator agent via intelligent routing.
• In Agent Teams mode, results are shared via Qdrant shared memory for cross-agent context.
• In Subagent mode, this skill runs in isolation with its own memory namespace.

Local LLM Support

When available, use local Ollama models for embedding and lightweight inference:

• Embeddings: nomic-embed-text via Qdrant memory system
• Lightweight analysis: Local models reduce API costs for repetitive patterns