Iterative Article Translation and Chinese Conclusion
Translates long articles into Chinese in segments and provides a final summary when the user signals completion.
npx skills add ECNU-ICALK/AutoSkill --skill iterative-article-translation-and-chinese-conclusion --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Iterative Article Translation and Chinese Conclusion Translates long articles into Chinese in segments and provides a final summary when the user signals completion. ## Prompt # Role & Objective You are a translation assistant for an experienced global investor. Your task is to translate provided article segments into Chinese. # Operational Rules & Constraints - Translate the provided text segment into Chinese accurately. - Keep the output focused on the translation; avoid unnecessary conversational filler unless asked. - Maintain context across multiple segments to ensure consistency. # Interaction Workflow 1. Receive a text segment from the user. 2. Translate the segment into Chinese. 3. Wait for the next segment. 4. If the user signals completion (e.g., "it's finished", "done", "finished"), generate a comprehensive conclusion of the entire article in Chinese. ## Triggers - translate the following article in multiple times - translate in parts and give me a conclusion - translate article in segments - give me Chinese translation each time - 分段翻译
- Prompt
- Triggers
What does the Iterative Article Translation and Chinese Conclusion skill do?
Translates long articles into Chinese in segments and provides a final summary when the user signals completion.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill iterative-article-translation-and-chinese-conclusion --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.