Skill UI

A comprehensive tool for searching, retrieving, and analyzing academic papers from the arXiv repository. Users can search by topic or specific ID to get metadata, download associated PDF files, and receive a structured summary of the paper's key contributions, making it essential for academic research and literature review.

This skill automates the multi-round research review process. It iteratively reviews provided work using advanced AI models (Codex MCP), identifying critical weaknesses, implementing fixes, and re-reviewing until the content achieves a positive assessment or the maximum round limit is reached. Ideal for refining complex academic papers, technical designs, or large codebases that require rigorous, iterative quality assurance.

This skill executes an autonomous, iterative research review loop, simulating a rigorous peer-review process. It repeatedly uses an external OpenAI-compatible LLM via API calls to score, critique, and identify weaknesses in a given research topic or document. The process continues to refine the content by implementing suggested fixes until the LLM provides a positive assessment or the maximum round limit is reached. Ideal for academic writing, technical documentation, and refining complex proposals.

This skill automates the iterative process of academic paper refinement, simulating the rigorous peer-review cycle of top-tier conferences (e.g., NeurIPS, ICML). It connects with an external LLM (MiniMax) to receive multi-round, critical feedback, tracks state persistence, and guides the user through necessary revisions until the research is deemed submission-ready. Ideal for maximizing the quality of technical writing.

Use Elasticsearch-powered UEBA to model baselines from authentication, file, and network logs, score deviations, and flag insider threat indicators for SOC investigation workflows.

This skill transforms a raw research proposal or method idea into a rigorous, paper-ready experimental roadmap. It guides the user through defining core claims, identifying necessary ablations (e.g., novelty, simplicity, frontier necessity), and structuring experiments into actionable, phased stages (Sanity, Baseline, Main, Decision). Ideal for advanced academic research in AI/LLM/VLM fields that requires strong validation and systematic reporting.

This skill provides a comprehensive, multi-stage pipeline for transforming a broad research topic into concrete, publishable hypotheses. It systematically conducts literature landscape surveys, identifies knowledge gaps, leverages advanced LLMs for divergent brainstorming, and applies multi-criteria filtering to ensure feasibility and novelty. Ideal for academic researchers, PhD students, or R&D teams starting a new project.

This advanced skill orchestrates a systematic, multi-stage workflow for generating novel and feasible research ideas in robotics and embodied AI. It moves users from a broad concept (e.g., manipulation, locomotion) to specific, benchmark-grounded, simulation-first projects. The pipeline surveys literature, checks novelty against major conferences (CoRL, ICRA, RSS), and reviews technical constraints to ensure ideas are falsifiable and practically achievable with current infrastructure.

Generates comprehensive, professionally formatted academic papers in LaTeX format. This tool guides users through drafting a paper section by section using an existing outline, research narrative, and figures. It supports major ML/AI conference venues (ICLR, NeurIPS, ICML, etc.), manages complex citation fetching (DBLP/CrossRef), and adheres to specific formatting rules, ensuring the final output is ready for submission.

This autonomous skill orchestrates the entire scientific research lifecycle, guiding users from a vague research direction to a polished, review-ready document. The pipeline systematically executes Idea Discovery (Workflow 1), full-scale Implementation, Experiment Deployment (Workflow 2), automated Review Loops, and optional Paper Writing (Workflow 3). It handles everything from preliminary literature review and novelty checks to generating high-quality, reproducible code and final academic drafts.

This skill transforms vague research ideas or fuzzy technical approaches into structured, elegant, and highly concrete method plans, suitable for academic papers or complex projects. It operates through an iterative, multi-phase review cycle, anchoring the original problem while forcing the development of the minimal, most impactful contribution. Ideal for researchers who need to refine a breakthrough idea into an implementation-ready proposal, simulating the rigorous process of top-tier academic review.

This end-to-end workflow automates the transition from a vague research direction to a structured, actionable proposal. It first refines the core method thesis and dominant contribution (using research-refine), and then automatically generates a detailed, claim-driven experiment roadmap and comprehensive pipeline summary. Ideal for researchers needing to transform initial concepts into a complete, executable paper proposal and experimental plan.