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Found 58 Skills
Conduct comprehensive, multi-round research that produces rich visual reports. Use when asked for "deep research", "comprehensive analysis", "compare frameworks", "evaluate options", "research the state of X", or any task requiring investigation across 10+ sources. NOT for quick lookups — this is a 5-15 minute deep dive that produces a briefing-quality artifact with screenshots, diagrams, tables, and cited findings.
Search research papers via Gemini for broad literature discovery. Use when user says "gemini search", "gemini papers", "search with gemini", or wants AI-powered literature discovery beyond arXiv/Semantic Scholar indexes.
Use when asked to run deep research or AI-Q research through a reachable NVIDIA AI-Q Blueprint backend.
12 research methodology skills. Trigger: study design, methodology selection, scientific reasoning, mentoring. Design: rigorous methods frameworks covering qualitative, quantitative, and mixed approaches.
Trigger native web search. Use when you need quick internet research with concise summaries and full source URLs.
Guide a CS or AI PhD student through a focused literature review sprint that produces a ranked paper map, notes, gaps, and next actions. Use this skill whenever the user needs to survey a topic, prepare related work, check whether an idea is novel, catch up on a field, read papers before a meeting, or turn a pile of papers into an organized research direction.
Audit a CS or AI research project for reproducibility across environment, data, code, configuration, logging, and documentation. Use this skill whenever the user wants to make experiments reproducible, prepare code for collaborators, debug environment drift, write a README, package a project for paper release, or ensure they can rerun results months later.
Diagnose surprising, negative, unstable, or ambiguous ML/AI experiment results and decide whether to debug implementation, rerun experiments, change metrics or baselines, revise the algorithm, narrow the paper claim, park, or kill a direction. Use this skill whenever results do not match expectations, a method fails, metrics conflict, seeds vary, baselines beat the method, plots look suspicious, or the user asks what to do next after experimental results.
Help a CS or AI PhD student turn a rough research idea into a validated next-step decision using the handbook's FIVE+C framework. Use this skill whenever the user says they have a research idea, wants to know whether an idea is worth pursuing, needs help choosing between project directions, is preparing to pitch an idea to an advisor or senior student, or feels unsure whether a project is too incremental, too ambitious, already solved, hard to evaluate, or missing resources.
Own experiment evidence semantics: decide datasets, baselines, metrics, ablations, robustness tests, chart evidence, and exactly what rows or columns a result table should contain. Use for experiment design, benchmark planning, supplied-result evidence structure, result-table schema, chart-spec semantics, 设计实验, 对比实验, 消融, 结果表证据结构. Do not search literature as the main deliverable, visually beautify or render an already specified table/figure, improve layout/colors/readability, or invent results.