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Found 126 Skills
Conduct comprehensive literature reviews using multi-perspective dialogue simulation. Generate diverse expert personas, conduct grounded Q&A conversations, and synthesize findings into structured knowledge. Use when starting a new research project or writing a survey section.
Simulate target-conference reviewers for an ML/AI paper before submission. Use this skill whenever the user wants a reviewer-style critique, predicted scores, likely reject reasons, rebuttal risks, area-chair style meta-review, adversarial Reviewer 2 feedback, or venue-specific pre-review for conferences such as NeurIPS, ICML, ICLR, CVPR, ACL, EMNLP, or similar venues. This skill should dynamically inspect reviewer guidelines, example reviews, accepted papers, and project evidence when available.
Design hypothesis-driven ML/AI experiments before running them. Use this skill whenever the user wants to plan experiments, ablations, baselines, metrics, controls, seeds, logging, stop conditions, reviewer-proof evidence, or an experiment matrix for a paper claim before using run-experiment or writing results.
Create and operate durable, source-backed Researcher runs. Use when a user wants cited research, a live watch URL, reusable source records, YouTube/video transcript extraction, website/domain extraction, run continuation, forked report versions, or run-scoped Q&A over a completed Researcher run.
Autonomous NeMo-RL research agent workflow for directed hypothesis testing and open-ended discovery. Guides agents through the full experiment lifecycle: understanding recipes and environments, wiring RL or NeMo-gym runs, launching reproducible baselines and iterations, analyzing results, preserving human oversight, and using git plus TSV logs as the research ledger. Do NOT use for: bug fixes, code review, documentation, refactoring, dependency updates, or single-file changes.
Overview of Tavus, the AI research lab pioneering human computing. Use when you need context about what Tavus is, their mission, core concepts like CVI and Human Computing, the model stack (Phoenix, Raven, Sparrow), or links to docs/platform/resources.
Guide a focused CS or AI literature review sprint that turns a topic, idea, claim, or project direction into a ranked paper map, closest-work risk assessment, method taxonomy, novelty implications, baseline implications, and next actions. Use this skill whenever the user needs to survey a topic, check novelty, map related work, prepare a project, find canonical or recent papers, decide read/skim/ignore priority, or turn papers into a research direction.
AI autonomous research agent for LLM training optimization using opencode as the agent. The agent autonomously modifies train.py, runs experiments, evaluates val_bpb, and iterates to find the best model. Use when: "run autoresearch", "start experiment", "train model", "autonomous research", "optimize LLM training".
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.
Comprehensive research grounded in web data with explicit citations. Use when you need multi-source synthesis—comparisons, current events, market analysis, detailed reports.
Conduct deep research on any topic — get comprehensive reports with citations, key findings, and actionable insights in minutes. Use when user wants to "deep research", "research this", "investigate", "analysis report", "深度研究", "调研", "リサーチ", "심층 연구".
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.