Skill UI

Expert skill for building Model Context Protocol (MCP) servers and tools from scratch. Covers the full development lifecycle—from defining tool schemas and resources to implementation, testing, and publishing to registries. Supports robust patterns using TypeScript and Python for integrating custom capabilities into AI agents.

This skill parses raw, verbose Simplicité log files from TXT format, filtering out noise and extracting only essential fields (e.g., timestamp, level, body). By converting logs into structured JSON, it significantly reduces the token size required for AI context optimization, making troubleshooting efficient and reliable.

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 skill guides the creation and maintenance of a `.geminiignore` file. By systematically filtering out machine-generated noise—such as build artifacts, dependency lock files, logs, and caches—it significantly optimizes the AI agent's context window. This process improves processing speed and, critically, reduces token consumption costs (FinOps).

This skill acts as a central orchestrator for all comprehensive Go development tasks. It analyzes the task context—whether designing an API, debugging a panic, or auditing security—and automatically loads a comprehensive set of required, specialized Go skills simultaneously. Designed for advanced AI coding agents, it ensures that complex tasks are never handled by a single, isolated skill, greatly improving coverage and accuracy.

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.

A comprehensive guide for implementing view transitions in React and Next.js. Learn how to animate UI states, shared element animations, and route changes using the native ViewTransition API. Covers best practices for maintaining spatial continuity and adapting to different navigation contexts.

This skill automates the debugging and fixing of failing Continuous Integration (CI) checks on GitHub PRs. It uses the GitHub CLI (gh) to locate failures, fetch detailed logs from GitHub Actions, summarize the failure context, and propose a structured fix plan. After user approval, it can implement the necessary code changes and verify the fix, streamlining the development cycle.

This advanced tool surfaces critical, often unrecorded, evidence of human-AI collaboration during development sessions. It analyzes git diffs and conversational context across multiple layers to classify the signal level (HIGH/MEDIUM/LOW), measure technical complexity, detect decision forks, and quantify the specific contribution of the AI model, providing structured data for post-session retrospectives and accountability.

This Main Agent acts as a central orchestrator for a specialized squad of AI agents, managing complex software development lifecycles. It guides projects through defined phases—from requirements gathering and planning to architecture, implementation, testing, and deployment. It maintains strict context discipline by summarizing agent outputs and referencing full artifacts, ensuring efficient, structured, and non-overwhelming communication to the user.

This skill guides the decomposition of monolithic applications into independent microservices using Domain-Driven Design (DDD). It defines bounded contexts, service boundaries, communication patterns (sync/async), data ownership, and provides a structured migration roadmap (e.g., Strangler Fig Pattern). Essential for scaling and modernizing large enterprise systems.