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Found 51 Skills
Multi-AI Parallel Deep Research. Triggered when users need comprehensive research, in-depth study, multi-party comparison, or comprehensive analysis covering multiple dimensions and sources for a certain topic. Suitable for complex topics (technical selection, competitor analysis, industry trends, controversial topics, etc.), not suitable for simple fact queries. Conduct parallel research through multiple AI services, cross-validate, and output a comprehensive report with citations.
Use this skill any time the user wants in-depth research or comprehensive analysis on any topic. This includes: industry analysis, competitive landscape mapping, market sizing, trend analysis, technology reviews, investment research, sector overviews, due diligence, benchmark studies, patent landscape analysis, regulatory analysis, and academic surveys. Also trigger when: user says 帮我调研一下, 深度分析, 行业研究, 市场规模分析, 竞争格局, 技术趋势, 做个研究报告. If deep research or comprehensive analysis is needed, use this skill.
Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.
Write ML papers for NeurIPS/ICML/ICLR: design→submit.
Transform research questions into constructs, design, samples, measurement, analysis, falsification, and execution plans. Use when the user asks for "design research", "can this method answer the question?", "help me create a research proposal", or requests the rw-research-design workflow. Runs without a private local workspace or preset research-lab; use user-provided material and bundled public-source methods.
Optimize ToolUniverse skills for better report quality, evidence handling, and user experience. Apply patterns like tool verification, foundation data layers, disambiguation-first, evidence grading, quantified completeness, and report-only output. Use when reviewing skills, improving existing skills, or creating new ToolUniverse research skills.
Search and download academic papers from arXiv. Find papers by keywords, authors, or arXiv ID.
Run a literature review using paper search and primary-source synthesis. Use when the user asks for a lit review, paper survey, state of the art, or academic landscape summary on a research topic.
Optional AI SDLC research workflow. Use when an AI assistant needs to investigate a customer, market, domain, technology, regulation, competitor, operational question, or implementation uncertainty and produce a routed source inventory plus synthesized findings with confidence, limitations, open questions, and delivery trace targets. Supports `--quick-flow` for focused evidence and `--full-flow` for multi-source and source-diversity gates.
Iterative multi-round deep research with structured analysis frameworks. Use for: deep research on a topic, compare X vs Y, landscape analysis, evaluate options for a decision, deep dive into a technology, comprehensive research with cross-referencing. Triggers: deep research, compare, landscape, evaluate, deep dive, comprehensive research, which is better, should we use.
Survey State-of-the-Art literature on a research topic. Use when asked to find papers, survey a field, map the research landscape, identify gaps, or build a literature matrix. First step in any research workflow.
Conducts citation-backed research using Firecrawl MCP search, scrape, map, crawl, and agent tools with selectable quick, standard, deep, and ultradeep modes. Use for multi-source comparisons, technical evaluations, market research, and high-stakes decision support.