Skill UI

This comprehensive guide provides expert knowledge on implementing structured data and schema markup using schema.org standards. Learn how to correctly use JSON-LD to help search engines understand your content, enabling rich results like star ratings, FAQs, and product details. It covers best practices, common schema types (Article, Product, FAQPage), and essential validation methods for maximizing search visibility.

This sophisticated orchestrator manages the entire academic research lifecycle, guiding users from initial research concepts through drafting, structured peer review, multiple revision stages, and final integrity checks. It coordinates specialized skills to ensure a reproducible and high-quality workflow, automating the complex transition from academic idea to publication-ready manuscript.

This skill generates robust JSON-LD structured data markup for various web components, including blogs, FAQs, tools, and SaaS pages. By implementing standard Schema.org types (WebSite, Article, FAQPage, etc.), you can significantly improve your SEO, boost click-through rates (CTR), and provide clear context to both search engines and AI systems. Includes implementation guidance for modern frameworks like Next.js.

This meta-skill orchestrates the entire academic writing workflow, from initial requirement extraction (topic, length, audience) to drafting, citation management, and final LaTeX/PDF compilation. It supports various modes, including compact skeletons for quick drafts and full manuscripts for publication-ready work. Ideal for users needing structured help with research papers or academic manuscripts.

This tool acts as a pre-drafting planning mechanism for academic or technical writing. It analyzes the user's request to extract and structure detailed preferences, such as target audience, citation style, depth, and desired emphasis. It determines the optimal generation mode (direct or preference-driven) before any writing or research takes place, ensuring all required criteria are met systematically.

Drafts a complete, publication-ready research paper section (e.g., Introduction, Method, Results) as a robust LaTeX fragment. This tool ensures the section adheres strictly to a predefined writing plan, maintaining narrative coherence, managing citation budgets, and slotting seamlessly into a larger academic manuscript.

This guide provides comprehensive instructions for searching Tabular Editor (TE) documentation and understanding complex configuration files. It details the usage of the `pbi-search` CLI tool, allowing users to query multiple sources (DAX, Power BI, SQLBI) simultaneously. It also covers the structure and best practices for model-specific files like `.tmuo` and application preferences, ensuring efficient data modeling and development.

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.

This skill guides the implementation of advanced SEO strategies tailored specifically for technical audiences and developers. It covers specialized techniques such as optimizing for error messages, researching high-intent technical long-tail keywords, creating structured how-to guides, and developing objective tool comparison content. Use this when targeting developers who search with precise technical intent, needing solutions that go beyond standard documentation.

This pattern is designed to solve context limitations in complex multi-agent workflows. Instead of relying on a single, massive context dump, it implements a structured, multi-cycle loop (Dispatch -> Evaluate -> Refine -> Loop). This process iteratively refines the search query by scoring content relevance and identifying missing context gaps, ensuring sub-agents gather the most targeted and highly relevant information needed for tasks like debugging or feature implementation.

This skill generates a structured, non-advisory worksheet for planning trade ideas, basket adjustments, or comparing prediction markets. It helps users methodically review venues, underliers, constraints, and manual execution steps without placing any trades or providing financial advice. Ideal for the research phase of quantitative finance workflows.

This skill provides a comprehensive, end-to-end workflow for addressing GitHub issues. It guides the user through crucial development stages: understanding the issue, researching prior art in the codebase, planning the fix, implementing changes in small commits, rigorous testing (including unit and regression tests), and finally, submitting a structured Pull Request (PR) using the GitHub CLI. Ideal for any developer maintaining codebases.