Loading...
Loading...
Found 126 Skills
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.
Use when asked to run deep research or AI-Q research through a reachable NVIDIA AI-Q Blueprint backend.