Skill UI

This skill is designed to comprehensively edit and improve existing articles. It first analyzes the content, dividing it into logical sections based on headings and considering the dependencies between information points. Subsequently, it rewrites each section to enhance clarity, coherence, and flow, ensuring the prose is tighter and more professional. Use this when you need to revise a draft, academic paper, or long-form content.

Generates high-quality, deterministic, and editable vector diagrams (SVG) for system architecture, workflows, and pipelines. By using a structured JSON specification (FigureSpec), it ensures that the same input always yields the identical output, making it ideal for reproducible technical documentation, academic papers, and formal system modeling where precise layout and connections are critical. It is optimized for non-data-plot, formal diagrams.

This skill guides the systematic process of conducting comprehensive academic literature reviews. It allows users to search multiple biomedical, scientific, and technical databases (e.g., PubMed, arXiv, Semantic Scholar) to synthesize current knowledge. The output generates professionally formatted reports (Markdown/PDF) with verified citations, and mandatorily integrates AI-generated scientific schematics to enhance visualization and depth, suitable for research papers, meta-analyses, and state-of-the-art reports.

This skill transforms an LLM into a disciplined wiki maintainer for Obsidian. It moves beyond simple RAG by incrementally ingesting sources (papers, notes, articles) to build a persistent, interlinked "Second Brain." The LLM manages cross-references, updates core concepts, and maintains an evolving knowledge graph, ensuring that knowledge compounds over time rather than being re-derived on every query. Ideal for deep research, personal knowledge management (PKM), and building comprehensive internal wikis.

This technique guides users through a rigorous two-phase research strategy. First, conduct a broad search to identify potential papers or authors. Crucially, the system instructs the user to use the extracted "author full name" as a highly specific anchor in subsequent queries. Combining the author's name with details like journal, institution, year, or biographical information ensures the most precise and reliable results, significantly narrowing down the search scope and achieving deep academic insights.

Performs rigorous, zero-context verification of all quantitative claims, comparisons, and scope statements reported in a research paper against the raw evidence files. It is designed to detect confirmation bias, such as rounding errors, data cherry-picking, or misreported deltas, ensuring strict fidelity between published claims and underlying raw results. Ideal for pre-submission quality control.

This advanced tool generates publication-ready academic figures and illustrations. It employs a sophisticated, multi-stage iterative workflow involving an AI Planner, Layout Optimizer, Style Verifier, and a dedicated native image generation bridge. The process includes strict visual review and self-correction loops (CVPR/NeurIPS standards) to ensure the highest level of academic polish.

This skill integrates ten major academic databases (including PubMed, arXiv, OpenAlex, and Semantic Scholar) via REST APIs. It enables comprehensive searches for scholarly articles, preprints, citations, and full-text access across various fields. Use this when searching for research papers on a topic, looking up specific DOIs/PMIDs, or needing global literature metadata.

This skill performs a structured Systematic Literature Review (SLR) by analyzing multiple academic papers on a given topic. It searches academic databases (like arXiv), extracts structured metadata (research questions, methodologies, key findings, limitations) from the entire set of papers, and synthesizes overarching themes and research trends. It is ideal for creating comprehensive literature surveys, annotated bibliographies, and cross-paper comparisons, outputting reports in professional citation formats like APA, IEEE, or BibTeX.

Provides a detailed, paragraph-level structural guide and page allocation blueprint specifically tailored for publishing top-tier systems papers (e.g., OSDI, SOSP, ASPLOS, NSDI). It includes mandatory templates for Abstract, Introduction (Problem-Gap-Insight), Design, and Evaluation, ensuring adherence to the highest academic standards.

This powerful compiler ingests heterogeneous research inputs, including PDF papers, code repositories, experiment logs, and raw notes. It transforms this diverse data into a comprehensive, structured Agent-Native Research Artifact (ARA). The system extracts core knowledge atoms, builds cognitive maps (claims, concepts, heuristics), and generates a verifiable, executable knowledge package for advanced AI consumption and research iteration.

Expert assistance for building, modifying, and debugging Minecraft server plugins for the Paper, Spigot, and Bukkit ecosystems. This includes implementing complex gameplay mechanics such as minigames, advanced combat systems, persistent player progression (perks, economy, quests), custom command handling, and robust state management. We handle configuration, listener registration, and adherence to best practices for Java-based server API usage.