- name
- semantic-paper-radar
- description
- Semantic literature discovery and synthesis across arXiv/OpenAlex/PubMed (and optional Google Scholar adapters). Use when users ask for domain must-read papers, research trend mapping, paper recommendations, reading lists, or academic lineage/context for a topic in natural science, AI, engineering, medicine, or interdisciplinary research.
Semantic Paper Radar
Build a domain reading list from natural-language intent, then output a concise research map.
Workflow
- Clarify query intent in one line:
- topic + scope + time window + priority (foundational / frontier / balanced).
- Run aggregated retrieval:
- General: python3 scripts/paper_radar.py search --query "<topic>" --max 40 --years 8 - Biomedical force-on: python3 scripts/paper_radar.py search --query "<topic>" --max 40 --years 8 --biomed
- Generate synthesis report:
- python3 scripts/paper_radar.py report --query "<topic>" --max 40 --years 8 --top 12 --mode balanced - Biomedical force-on: add --biomed - Export clickable HTML: add --export-html (optional --html-out <path>)
- Present results in Chinese unless user asked otherwise:
- 必读文献（分层） - 学术脉络（时间线） - 阅读顺序（先读3篇） - 可选下一步（细分子方向）
Output Rules
- Prefer OpenAlex entries with DOI/citation metadata for "经典" judgement.
- Keep arXiv entries for "最新前沿" and unreviewed but high-momentum work.
- If the query is biomedical/clinical, explicitly include a caution that arXiv papers may be preprint.
- If retrieval is sparse, broaden query with synonyms and rerun once.
Recommended Prompt Pattern
Use this framing when user asks for recommendations:
- "按
经典奠基(3-5)+方法跃迁(3-5)+近两年新进展(3-5)输出" - "每篇给：一句贡献、为什么必读、适合第几步读"
- "最后给该领域 3 条学术脉络主线"
Optional Scholar Integration
If the environment already has a Scholar-capable tool/skill (e.g., serper-scholar), call it after report and use it only for:
- citation cross-check
- venue/author authority补充
Do not block core workflow if Scholar integration is unavailable.