Skill UI

This tool systematically generates, validates, and ranks concrete, publishable research ideas based on a broad research direction provided by the user. It guides the user through the entire idea discovery pipeline, from initial landscape survey (Phase 1) to validation (novelty checks, experiments) and review, ensuring the generated ideas are novel and relevant for academic publication. It manages complex workflow orchestration and utilizes multiple external tools.

This advanced skill guides users from a broad robotic concept (e.g., 'humanoid grasping') to a highly defined, research-ready hypothesis. It automates a robust four-stage pipeline: literature survey, idea creation, novelty verification, and critical review. The process rigorously enforces academic constraints, prioritizing simulation-first approaches, grounding ideas in established benchmarks (like ManiSkill or Habitat), and ensuring feasibility for real-world robotics development.

This advanced skill systematically verifies the novelty of a proposed research method or technical idea against the global body of academic literature. It guides the user to extract core technical claims, performs multi-source searches across major academic databases (e.g., arXiv, Google Scholar, top conferences), and utilizes high-level LLM reasoning to generate a detailed, structured novelty assessment report. Ideal for researchers seeking to validate the unique contribution of their work.

Automates the generation of high-quality, publication-ready figures and comparison tables from raw experimental data (JSON, CSV). This skill is essential for academic writing workflows, handling various plot types including line graphs, bar charts, scatter plots, and heatmaps, ensuring consistency with professional style guides like LaTeX and specific DPI requirements.

This skill generates a structured, section-by-section outline for academic papers. By synthesizing diverse inputs—including research narratives, auto-review conclusions, and experiment results—it guides researchers in structuring their findings into a cohesive draft plan. It also supports optional style references to align the outline's section structure with specific target venues or published works.

Automatically generates structured academic papers in LaTeX format. By integrating a detailed paper plan, the research narrative, and generated figures, this tool structures the content section by section. It supports major conference and journal templates (e.g., ICLR, NeurIPS, IEEE), manages complex citation fetching via DBLP/CrossRef, and allows structural guidance using reference papers. Ideal for drafting the complete manuscript.

This skill automates the end-to-end academic paper writing process. It transforms raw narrative reports, research ideas, or existing plans into polished, submission-ready LaTeX source and PDF. The comprehensive pipeline includes planning, figure generation, structured writing, compilation, and an advanced improvement loop, allowing users to achieve publication-quality results. Users can optionally guide the structural style using a reference paper.

A powerful tool for conducting comprehensive academic research and literature reviews. It searches, analyzes, and synthesizes key ideas from multiple sources, including local PDF libraries (Zotero, Obsidian), the general web, and specialized academic databases (Semantic Scholar, DeepXiv). Use this when you need to map the historical context, identify related works, or understand complex concepts across multiple studies on a given topic.

This skill automates the entire academic research lifecycle. It orchestrates stages from broad idea discovery and literature review to experimental implementation, rigorous cross-model review, and finally, generating a polished research paper PDF. Ideal for researchers needing an end-to-end, autonomous workflow for academic publication preparation.

This skill transforms vague or fuzzy research directions into highly concrete, elegant, and academically rigorous proposals. It operates through multi-phase, iterative refinement, anchoring the process to the core problem while ensuring the proposed method is minimal, cutting-edge, and ready for publication or implementation.

This comprehensive pipeline guides users from a vague research concept to a fully structured, testable academic proposal. It first refines the core methodology (research-refine) to establish a stable thesis, and then generates a detailed, claim-driven experiment roadmap (experiment-plan). This end-to-end workflow ensures that the final academic output is both elegant and empirically validated.

Get a deep, multi-round, and adversarial critical review of your research work, ideas, or drafts. This skill simulates the perspective of a senior ML reviewer (e.g., NeurIPS/ICML level) using dedicated external backends (Codex or Manual). It is designed to find logical gaps, missing experiments, and narrative weaknesses, providing brutal honesty to elevate your academic contribution.