Skill UI

This comprehensive skill automates the entire process of generating novel, robust, and benchmark-grounded research ideas in robotics and embodied AI. It guides users from a broad initial concept to concrete, actionable project proposals by chaining rigorous literature surveys, novelty verification, and critical reviews. The workflow enforces robotics constraints, prioritizing simulation-first, falsifiable, and feasible real-world applications.

Transforms vague research directions into highly concrete, elegant, and frontier-aware methodological plans. This skill guides users through an iterative, multi-phase refinement process, ensuring the final output maintains a strong Problem Anchor, focuses on minimal adequate contributions, and is ready for top-tier academic publication.

This skill provides a multi-round, deep critical review of research proposals, papers, or experimental results by simulating the perspective of a senior ML reviewer (e.g., NeurIPS/ICML level). It guides the user through identifying logical gaps, missing experiments, and narrative weaknesses, ensuring high fidelity and iterative refinement of technical work.

A comprehensive tool and API wrapper for accessing, parsing, and analyzing research papers published on Hugging Face and arXiv. Users can input various URLs or IDs (e.g., arXiv ID) to retrieve structured metadata, full markdown content, and automatically link associated resources such as models, datasets, and application spaces. Ideal for researchers and developers needing structured access to cutting-edge AI knowledge.

Generates structured peer-review quality critiques of academic papers, covering summaries, methodology scrutiny, contribution evaluation, literature positioning, and recommendations across domains whenever a user requests reviews, analyses, or summaries of research publications.

This skill provides comprehensive interaction with the DeerFlow AI agent platform via its robust HTTP API. It is designed for managing complex, multi-step AI workflows, including deep academic research, code execution, web browsing, and file analysis. Users can initiate and continue conversation threads, manage agent memory, upload documents (PDF, etc.), and delegate complex research tasks to the autonomous agent system.

Open Notebook is a self-hosted, open-source alternative to major AI research tools. It allows users to ingest diverse content—including PDFs, videos, and web pages—and organize them into private notebooks. Key features include context-aware AI chat, automated summarization, multi-speaker podcast generation, and full programmatic access via a comprehensive REST API. By running on your own infrastructure, it ensures complete data privacy and supports 16+ AI providers.

This comprehensive skill guides users through the entire lifecycle of developing AI agent skills. From initial concept (Understand) to structured design, writing the core skill logic, rigorous testing, and final performance optimization. It ensures skills are robust, accurate, and highly triggerable across any domain, including engineering, content creation, and research.

This skill acts as a comprehensive hybrid router for any research request. It first deterministically classifies the user's intent, delegating the query to specialized modules like Pulse (trends/sentiment), Grants (NIH funding), or LitReview (academic papers). If no specialist matches, it falls back to a robust general search and synthesis workflow. The system ensures routing transparency, provides comprehensive reports, and includes full citations and an audit log.

This skill helps users find alternative, equivalent, or related tools for Japanese Natural Language Processing (NLP). By providing a specific GitHub repository URL, owner/repo name, or tool name (e.g., mecab, fugashi), the system mines a bundled dataset and performs advanced semantic scoring based on shared categories, labels, and tokens. It expands the search via web research to provide comprehensive alternatives.