Skill UI

This skill guides researchers in selecting and refining topics for the Journal of Systems Engineering (Tianjin University). It helps define system boundaries, identify feedback loops, and ensure testable questions. It prevents vague topics by requiring specific system structures and verifiable outcomes. Suitable for academic research in systems engineering, operations research, and complex systems.

A skill for designing evidence and validation strategies for academic manuscripts submitted to the Journal of Systems Engineering. It covers standards for numerical experiments, simulations, real data, case studies, and empirical research, ensuring claims are supported by appropriate evidence rather than generic robustness checks.

This skill facilitates structured, bidirectional reading and knowledge synthesis for academic texts, theories, and articles. It forces users to first capture the source material's original intent, before critically mapping its relationships, conflicts, and absorptions against the CrossFrame framework. It ensures structured output with clear source boundaries, making it ideal for deep learning and scholarly work.

This advanced workflow is designed to generate comprehensive, fact-checked research reports on any complex topic. It systematically performs fan-out web searches across multiple defined angles, fetches multiple sources, and employs a rigorous adversarial verification system (3-vote) to validate claims. The final output is a highly cited, synthesized report, ideal for academic or critical business research.

A powerful tool for deterministic and reproducible retrieval of evidence from specialized academic and policy corpora. It supports searching arXiv (preprints), PubMed/PMC (biomedical literature), and US policy documents. Users can check corpus freshness, retrieve stable metadata, and fetch full-document Markdown for high-reliability research tasks.

This skill acts as a literature-research assistant, orchestrating a Python CLI to interface with APIs like Semantic Scholar and arXiv. It enables users to search for academic papers by topic, retrieve detailed metadata, perform impact analysis using citation tracking, download PDFs, and extract text content. It streamlines the entire academic research workflow from initial discovery to deep reading.

This skill generates comprehensive, academic-style survey papers as a single, self-contained HTML file. By reading an anchor resource and curating research data, it structures the content and utilizes advanced LLMs (like Kimi K2.6 via Fireworks) to generate all prose, figures, and bibliographies, suitable for rapid academic literature review.

Search Google, Bing, Google News and Google Scholar, and read public pages, via the Serply MCP server. Ideal for current facts, technical docs, news, academic research, and source verification with cited links.

Verify citations and references in documents against real sources using the Stipple API. Checks resolution, arithmetic, and unsupported claims. Use before publishing research reports, whitepapers, or academic writing.

A systematic workflow for conducting literature reviews across academic, biomedical, technical, and scientific topics. Includes search planning, source screening, synthesis, citation verification, and evidence logging.

A structured rubric-based skill for evaluating academic or scientific work, including research papers, proposals, literature reviews, and methodology sections. It assesses evidence quality, citation support, and provides actionable feedback for revisions.