Score Chinese long-form articles on seven dimensions and remove AI-generated patterns.
npx skills add https://github.com/neo-unicorn/neo-skills --skill neo-wright-review
Please help me install this Agent Skill. Skill: neo-wright-review Source: https://github.com/neo-unicorn/neo-skills/tree/main/neo-wright-review Command: npx skills add https://github.com/neo-unicorn/neo-skills --skill neo-wright-review
It evaluates Chinese long‑form articles, scores them on seven dimensions, removes AI‑generated patterns, and iteratively revises until each dimension reaches a high standard, helping creators produce authentic, high‑impact content.
Ask the skill to review an article by saying: “请用 Neo‑Wright‑Review 评审以下内容并去除 AI 痕迹”，然后粘贴文章内容。
High-intent search queries and answers about installing and using this skill.
AI-detox for Chinese articles detects and rewrites template sentences, transition words, preachy tone, and dad-tone to remove AI-generated patterns. It automatically cleans your content and iteratively revises up to three cycles until quality scores reach nine or higher.
Scoring Chinese long-form content involves evaluating seven dimensions: attractiveness, resonance, information density, storytelling, CTA strength, AI-detox, and funnel fit. You provide the article text and receive a detailed score report identifying strengths and weaknesses across all dimensions.
Funnel suitability checking validates TOFU, MOFU, and BOFU alignment by scoring how well your Chinese article fits each stage of the content funnel. It assesses funnel fit as one of seven quality dimensions and revises content to ensure proper alignment for viral impact.
The best way to automatically revise Chinese drafts is through iterative rewriting cycles that score content on seven dimensions and auto-revise up to three times. You paste the article text, receive a detailed score report, and get a polished version meeting nine-plus points on every dimension.
No special tools or setup are required for seven-dimension scoring of Chinese articles. The review process requires only the article text as input and returns a detailed score report plus revised content without any dependencies or external components.
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