Install
openclaw skills install academic-translatorTranslate and analyze academic CS papers (EN↔ZH), summarize content, answer questions with paper text plus web research, and compare related works.
openclaw skills install academic-translatorTrigger: User provides a PDF file, arxiv link/ID, or asks to translate/analyze an academic paper.
Professional academic translation agent for CS papers. Translates papers (EN↔ZH), answers questions with combined paper content + web research.
From arxiv link/ID:
source /home/kjp/.openclaw/workspace/.venv/bin/activate
python {SKILL_DIR}/fetch_arxiv.py "<arxiv_url_or_id>" /tmp/arxiv_papers
Returns JSON with metadata (title, authors, abstract, pdf_path). Use this to:
From PDF file (uploaded or local path):
source /home/kjp/.openclaw/workspace/.venv/bin/activate
python {SKILL_DIR}/extract_pdf.py "<path_to_pdf>" [max_pages]
Returns JSON with page-by-page text. Omit max_pages for full extraction.
After extracting paper text, translate using these principles:
Translation Guidelines:
Translation Output Format:
# [翻译标题]
**原标题:** [Original Title]
**作者:** [Authors]
**来源:** [arxiv ID / conference / journal]
## 摘要
[Translated abstract]
## 1 引言
[Translated content...]
...
When the user asks questions about the paper:
web_search for supplementary context:
| Command | Action |
|---|---|
| "翻译这篇论文" / "translate this paper" | Full paper translation |
| "翻译摘要/引言/第N节" | Translate specific section |
| "总结这篇论文" / "summarize" | Generate structured summary |
| "解释 [concept]" | Explain a concept from the paper with web context |
| "[any question]" | Answer based on paper + web research |
| "对比 [paper A] 和 [paper B]" | Compare two papers |
When summarizing, use this structure:
## 📄 论文概览
**标题:** ...
**核心贡献:** 1-2 sentences
**关键词:** ...
## 🎯 主要贡献
1. ...
2. ...
## 🔬 方法概述
[Concise method description]
## 📊 实验结果
[Key results and comparisons]
## 💡 核心洞察
[Why this matters, limitations, future directions]
Store paper context in /tmp/academic_papers/ for multi-turn conversations:
/tmp/academic_papers/current_paper.json — metadata of current paper/tmp/academic_papers/current_text.txt — extracted full textThis allows follow-up questions without re-extracting.
web_search proactively for unfamiliar terms or cutting-edge topicssessions_spawn) for full paper translation to avoid blocking the main session