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 is a mandatory, one-time setup skill designed to interview users interactively to build a personalized knowledge base for advanced email triage. Using a structured, discipline-driven interview process, the skill collects data on email patterns, business context, and priorities, generating 7 critical structured files (e.g., email-taxonomy.md, rate-card.md). This knowledge base powers the companion 'inbox-triage' skill. Run this before initial use or whenever core business processes change significantly.

This skill transforms a course syllabus (PDF, DOCX, etc.) into a highly curated, supplementary academic reading list. It parses topics and learning outcomes, calibrates content difficulty based on the target audience (undergraduate, graduate, etc.), and searches academic databases (Consensus) for recent, peer-reviewed papers. The output is a professional .docx file containing summaries, links, and advanced discussion questions, making it invaluable for educators and students needing structured, up-to-date course resources.

A specialized skill for optimizing content to be cited as authoritative sources by major AI language models (ChatGPT, Perplexity, Claude, Gemini). Unlike traditional SEO, AEO focuses on E-E-A-T signals, factual density, and structured data to ensure LLM integration. Features include content auditing, E-E-A-T scoring, structural rewriting, and citation tracking.

A detailed technical workflow for inspecting complex 3D animation, rigging, and motion states within Blender. This skill moves beyond visual inspection by extracting structured data—such as bone transformations, world-space bounding boxes, ground contacts, and axis conventions—to accurately diagnose issues like foot sliding, rotation errors, or retargeting mismatches in professional animation pipelines.

Bilig WorkPaper provides a code-first runtime environment for complex spreadsheet business logic. It allows agents to interact with structured data using formulas and JSON workbooks via deterministic API calls (MCP/TypeScript). This is ideal for replacing unreliable UI automation in use cases like quote calculators, budget modeling, and data validation, ensuring calculation integrity through explicit read-back proof.

This skill generates comprehensive Entity Relationship Diagrams (ERDs) and database schemas as D2 source code from an existing Entity Framework Core codebase. It analyzes DbContexts, DbSet declarations, Fluent API mappings, and migrations to accurately visualize tables, columns, primary/foreign keys, relationships (including many-to-many joins), owned types, and constraints. The output is a structured .d2 file, suitable for rendering into SVG or PNG, helping developers document the persistence model.

This powerful router analyzes complex document types (scanned, medical, table-heavy, etc.) and determines the optimal conversion pipeline (tools, flags, cleanup) to extract data into various formats like Markdown, JSON, or DOCX. It goes beyond simple text extraction by prioritizing the preservation of document structure, relational data, and high fidelity, ensuring the generated output is accurate for downstream machine processing.

A comprehensive guide on embedding before-and-after screenshots directly into pull request descriptions. This skill helps reviewers visualize UI changes, layout adjustments, and data visualizations instantly, without needing to check out the branch. Includes best practices for image sizing, structured Markdown patterns, and specific API/upload guidance for Azure DevOps and GitHub.

This skill converts complex, narrative technical documents, research excerpts, or natural language problem descriptions into a highly structured Mathematical Problem Specification (MPS). It systematically extracts variables, constraints, objectives, and uncertainty parameters. By adhering to a strict Zero-Inference Protocol, it ensures all extracted elements are strictly grounded in the source text, making it invaluable for formalizing real-world optimization, data science, and engineering problems.

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 skill captures and persists critical user decisions, project constraints, and architectural choices across multiple sessions and agents. It saves this context into a structured memory database (`mdbase`), ensuring that future development steps or agents can retrieve the original intent without re-deriving it from scratch. Ideal for documenting project rules, tech stacks, and design rationale.