agent-browser
Browser automation CLI for AI agents. Use when needs to interact with websites, including navigating pages, filling forms,…
Best practices for AI-driven English-to-Chinese translation. This skill should be used when the user asks to "translate to Chinese", "update the Chinese translation", "improve Chinese translation", "fix translation quality", "review Chinese translation", or when translating any
$ npx -y skills add archibate/dotfiles-opencode --skill better-translate --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/better-translateContext preview
The summary Claude sees to decide when to auto-load this skill.
Best practices for AI-driven English-to-Chinese translation. This skill should be used when the user asks to "translate to Chinese", "update the Chinese translation", "improve Chinese translation", "fix translation quality", "review Chinese translation", or when translating any
name: better-translate description: > Best practices for AI-driven English-to-Chinese translation. This skill should be used when the user asks to "translate to Chinese", "update the Chinese translation", "improve Chinese translation", "fix translation quality", "review Chinese translation", or when translating any English text into Chinese. Also applies when polishing an existing Chinese translation of English content.
Produce natural, accurate Chinese translations that read as if written by a native speaker — free of 翻译腔 (translationese) and AI slop patterns.
AI models produce predictable defects in Chinese translation: English word order calqued into Chinese, filler phrases added for politeness, over-emphasis not present in the source, and tone shifts. This skill provides a structured iterative workflow to eliminate these issues through comprehension, translation, and repeated polish passes.
Read the full English source document before translating anything.
Identify ambiguous passages, domain-specific terminology, and phrases that could be interpreted multiple ways. Present ambiguities in a table:
| English Passage | Possible Meanings | Chosen Interpretation | |---|---|---| | ... | ... | ... |
For unresolved terminology: 1. Explore the project context (README, codebase, related docs) for usage conventions. 2. If ambiguity persists, stop and ask the user.
Do not proceed to translation until all ambiguities are resolved.
Translate the full document in one pass. During the initial draft:
Run a minimum of **3 rounds**. Each round performs three distinct checks in order. If any round produces changes, run one additional round after the minimum 3.
Compare translated text against the English source line by line. Fix:
Read the Chinese text without referring to the English source. Fix:
Detect and remove AI-specific defect patterns:
For a detailed catalog of these patterns with examples, consult **`references/translation-examples.md`**.
After iterative polish converges (a round produces zero changes):
1. Read the complete Chinese document from start to finish, as a Chinese reader, without referring to the English source. 2. Ask: does this read like a native Chinese speaker wrote it? Any AI slop indicators or 翻译腔 remaining? 3. If any issue is found, return to Phase 3 for another round. 4. Claim translation complete only when the full read-through raises no issues.
Do not rush this step.
| Phase | Action | Goal | |-------|--------|------| | 1. Comprehension | Read source, list ambiguities, resolve terminology | Full understanding | | 2. Initial Translation | Translate in one pass, preserve formatting | Complete draft | | 3. Iterative Polish | Accuracy → Fluency → AI Slop, minimum 3 rounds | High quality | | 4. Final Validation | Full read-through as Chinese reader | Confirm quality |
小彭老师现在改用claude code的开源替代opencode,满足了开源人士旺盛的定制需求x(其实opencode中也可以继续用claude的闭源模型,只是换成了开源的opencode客户端) 安装方法:SETUP.md
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