Loading...
Loading...
Found 17 Skills
Comprehensive scientific literature search across PubMed, arXiv, bioRxiv, medRxiv. Natural language queries powered by Valyu semantic search.
Search arXiv physics, math, and computer science preprints using natural language queries. Powered by Valyu semantic search.
Search academic papers across arXiv, PubMed, Semantic Scholar, bioRxiv, medRxiv, Google Scholar, and more. Get BibTeX citations, download PDFs, analyze citation networks. Use for literature reviews, finding papers, and academic research.
DEFAULT for all research and web queries. Use for any lookup, research, investigation, or question needing current info. Fast and cost-effective. Only use parallel-deep-research if user explicitly requests 'deep' or 'exhaustive' research.
This skill should be used when user asks to "search for papers", "find research papers", "search arXiv", "search PubMed", "find academic papers", "search IEEE", "search Scopus", or "look up scientific literature".
Web search, content extraction, crawling, and research capabilities using Tavily API
Use Google Scholar API for academic search to find papers, research reports, academic literature, and obtain detailed information such as citation data, authors, publication journals, etc.
Search and progressively read open-access academic papers through DeepXiv. Use when the user wants layered paper access, section-level reading, trending papers, or DeepXiv-backed literature retrieval.
ONLY use when user explicitly says 'deep research', 'exhaustive', 'comprehensive report', or 'thorough investigation'. Slower and more expensive than parallel-web-search. For normal research/lookup requests, use parallel-web-search instead.
General-purpose deep research with multi-source synthesis and confidence-scored findings. Auto-classifies complexity from quick lookup to exhaustive investigation. Cross-validates across independent sources with anti-hallucination verification, contradiction detection, and bias auditing. Produces synthesis products with evidence chains and provenance. Resumable journal sessions. Use when investigating technical topics, academic questions, market analysis, competitive intelligence, architecture decisions, technology evaluation, fact-checking, literature review, or trend analysis. NOT for code review (use honest-review), strategic decisions (use wargame), multi-perspective debate (use host-panel), or simple factual Q&A answerable in one search.
General-purpose web search using DuckDuckGo and AI-synthesized search engines. Use this skill for web searches, current information, fact-checking, news, and research on any topic where live internet data is needed. Supports all languages. Three modes: fast web results, AI-synthesized answers (IAsk.ai, great for deep questions and academic research), and Monica AI synthesis. Trigger on: "search for", "look up", "find information about", "what is the latest", "search the web", "find out about", "what happened with", "current status of", "recent news", "is X still true", "查一下", "搜索", "查资料", "上网查", "検索して", "調べて", any question requiring real-time or post-training web data. Do NOT trigger for: code exploration, local file analysis, codebase-internal questions, or well-established facts fully covered by training knowledge. Note: if the `agent-reach` skill is also available, prefer `ddg-search` for pure web search tasks; prefer `agent-reach` when the task involves social platforms (Twitter, Reddit, YouTube, WeChat, Bilibili, etc.) or platform-specific APIs.
A specialized skill for generating high-quality illustrations for academic papers, supporting two output formats: (1) LaTeX/TikZ code: Suitable for structured diagrams such as system architecture diagrams, data flow diagrams, and geometric schematic diagrams, which can be directly embedded into papers; (2) draw.io XML: Suitable for highly decorative diagrams such as technical roadmaps, scientific research display diagrams, and academic presentation illustrations, supporting gradient colors, shadows, and free layout, which can be opened and edited at app.diagrams.net. Supports the above two output formats with a unified workflow: Analyze input (copy/image/paper) → Drawing instructions → Code generation → Compilation verification → Full-score delivery. It automatically identifies the field of the paper and designs illustrations as an expert in that field. Use when the user asks to: 画论文图、画架构图、画流程图、画示意图、 LaTeX画图、TikZ画图、论文配图、生成画图指令、复刻图片、 画图代码、学术论文图、画系统架构、画协议流程、论文插图、tikz diagram、 latex figure、根据论文画图、画个图、帮我画图、生成tikz、论文tikz、 根据文案画图、照着图片画、复刻这张图、技术路线图、科研架构图、 学术汇报图、drawio、draw.io、路线图、研究框架图、技术方案图。