在保留含义、归属和作者身份的前提下,改善中文文本的节奏、具体性和清晰度。
Chinese Content Humanizer When to use Edit Chinese text for natural rhythm, specificity, and clarity while preserving meaning, attribution, and authorship integrity. Workflow 1. Confirm the user owns the input or has permission to process it. Collect the source material, audience, output format, and acceptance criteria. 2. Separate observed facts and user-provided constraints from inference. Ask for missing information instead of inventing it. 3.
# Chinese Content Humanizer ## When to use Edit Chinese text for natural rhythm, specificity, and clarity while preserving meaning, attribution, and authorship integrity. ## Workflow 1. Confirm the user owns the input or has permission to process it. Collect the source material, audience, output format, and acceptance criteria. 2. Separate observed facts and user-provided constraints from inference. Ask for missing information instead of inventing it. 3. Agree on genre or purpose, audience, voice, length, structure, continuity, and claims that must be preserved. 4. Use <skill-tag>skill:0198d3d1-d2fc-5fbe-bff7-5b6a983e9cce</skill-tag> for the connected writing and revision workflow. Show material changes and keep factual claims attributable. ## Output contract Return the input assumptions, the approved plan, the generated or analyzed result, uncertainty and limitations, and a concise verification checklist. Preserve source links and provider-returned identifiers when available. Stop if the required connected capability is unavailable; do not substitute local scripts, package installation, or embedded credentials. ## Safety and review Do not upload private or sensitive media without explicit authorization. Do not claim certainty beyond the evidence. Require review before externally visible, paid, destructive, or account-changing actions. ## Source and license This Aident-native re-authoring is based on [the pinned upstream source](https://github.com/op7418/Humanizer-zh/tree/91f3d394db8419c20d67ebe22a96cf8fee0a404b), discovered through [ModelScope](https://modelscope.cn/skills/iSolver/Humanizer-zh), and used under MIT. It preserves the upstream workflow goal while removing local executable, credential, and package assumptions.