Humanize Chinese workflow skill. Use this skill when the user needs Detect and rewrite AI-like Chinese text with a practical workflow for scoring, humanization, academic AIGC reduction, and style conversion. Use when the user asks to \u53bbAI\u5473, \u964dAIGC, \u53bb\u9664AI\u75d5\u8ff9, \u8bba\u6587\u964d\u91cd, \u77e5\u7f51\u68c0\u6d4b, \u7ef4\u666e\u68c0\u6d4b, humanize chinese, detect AI text, or make Chinese text sound more natural and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
Humanize Chinese workflow skill. Use this skill when the user needs Detect and rewrite AI-like Chinese text with a practical workflow for scoring, humanization, academic AIGC reduction, and style conversion. Use when the user asks to \u53bbAI\u5473, \u964dAIGC, \u53bb\u9664AI\u75d5\u8ff9, \u8bba\u6587\u964d\u91cd, \u77e5\u7f51\u68c0\u6d4b, \u7ef4\u666e\u68c0\u6d4b, humanize chinese, detect AI text, or make Chinese text sound more natural and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
This public intake copy packages plugins/antigravity-awesome-skills/skills/humanize-chinese from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.
Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.
This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.
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.
Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Optional CLI Flow, Manual Rewrite Playbook, Academic AIGC Reduction, Style Conversion, Source, Limitations.
When to Use This Skill
Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.
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
Use when the request clearly matches the imported source intent: Detect and rewrite AI-like Chinese text with a practical workflow for scoring, humanization, academic AIGC reduction, and style conversion. Use when the user asks to 去AI味, 降AIGC, 去除AI痕迹, 论文降重, 知网检测, 维普检测, humanize....
Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
Operating Table
Situation
Start here
Why it matters
First-time use
metadata.json
Confirms repository, branch, commit, and imported path through the external_source block before touching the copied workflow
Provenance review
ORIGIN.md
Gives reviewers a plain-language audit trail for the imported source
Workflow execution
SKILL.md
Starts with the smallest copied file that materially changes execution
Supporting context
SKILL.md
Adds the next most relevant copied source file without loading the entire package
Handoff decision
## Related Skills
Helps the operator switch to a stronger native skill when the task drifts
Workflow
This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.
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
remove formulaic connectors rather than paraphrasing every sentence
vary sentence length and paragraph rhythm
Imported Workflow Notes
Imported: 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.
Imported: Optional CLI Flow
If the user has a local clone of the source toolkit, these examples are useful:
Use @humanize-chinese-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.
Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.
Example 2: Ask for a provenance-grounded review
Review @humanize-chinese-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.
Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.
Example 3: Narrow the copied support files before execution
Use @humanize-chinese-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.
Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.
Example 4: Build a reviewer packet
Review @humanize-chinese-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.
Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.
Best Practices
Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.
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
Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
Imported Operating Notes
Imported: 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
Troubleshooting
Problem: The operator skipped the imported context and answered too generically
Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills/skills/humanize-chinese, fails to mention provenance, or does not use any copied source files at all.
Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.
Problem: The imported workflow feels incomplete during review
Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task.
Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.
Problem: The task drifted into a different specialization
Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better.
Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.
Related Skills
@00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
@00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
Additional Resources
Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.
Resource family
What it gives the reviewer
Example path
references
copied reference notes, guides, or background material from upstream
references/n/a
examples
worked examples or reusable prompts copied from upstream
examples/n/a
scripts
upstream helper scripts that change execution or validation
scripts/n/a
agents
routing or delegation notes that are genuinely part of the imported package
agents/n/a
assets
supporting assets or schemas copied from the source package
assets/n/a
Imported Reference Notes
Imported: 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
Imported: 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.
Imported: 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.
Imported: Source
Adapted from the voidborne-d/humanize-chinese project and its CLI/script workflow for Chinese AI-text detection and rewriting.
Imported: Limitations
Use this skill only when the task clearly matches the scope described above.
Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
