Skill UI

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 orchestrator dynamically manages and loads multiple specialized Go skills based on the current task context. Whether you are designing an API, debugging a panic, auditing security, or optimizing performance, it automatically combines the most relevant skills (e.g., gRPC loads testing, error handling, and core Go skills). It acts as a comprehensive guide to Go best practices and modern development patterns.

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 powerful tool for designing and writing deterministic, multi-agent workflow scripts in JavaScript. It allows users to orchestrate complex, repeatable, multi-step tasks using advanced patterns like fan-out, pipelines, loops, and judge-panels. Ideal for automating sophisticated business processes that require multiple specialized sub-agents and robust context management.

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 meta-skill automates the process of handling a GitHub issue by fetching context, delegating the fix to a sub-agent, summarizing the resolution, and finally opening a pull request (PR). It is best suited for small, well-scoped issues with clear acceptance criteria. This pipeline simulates a complete development workflow.

A comprehensive skill for generating new design screens from text prompts or images, and iteratively editing existing designs. It incorporates advanced prompt enhancement, design tokens, and professional UI/UX terminology to ensure professional, structured, and context-aware design output for web and mobile platforms.

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 guide serves as a comprehensive reference for writing idiomatic, efficient, and modern Python code. It covers best practices for comprehensions, context managers, error handling (EAFP), data class usage, type hinting, and avoiding common anti-patterns, helping developers write cleaner, more robust, and truly 'Pythonic' code.