Skill UI

A comprehensive guide covering patterns and architectures for automating code execution using LLMs. It spans simple sequential pipelines (e.g., daily dev scripts) to advanced, RFC-driven multi-agent Directed Acyclic Graph (DAG) orchestration. Ideal for building CI/CD style continuous development pipelines, managing complex state, and automating quality gates without human intervention.

This skill is intended to analyze and improve the quality of codebases, ensuring adherence to best practices and coding standards. Note: This specific file is a translation placeholder, and the core functionality description should refer to the original skill file for details.

Explains when to call the code-reviewer subagent, how to collect SHAs, fill the template, and act on its feedback so every task, feature, or merge meets quality expectations.

Guides execution of implementation plans by dispatching isolated subagents per task, enforcing a spec-first then quality review loop, and preserving overall coordination context for faster, high-quality iterations.

This model provides an engineering operational framework for teams that leverage AI agents to generate large volumes of code. It shifts the focus from simple syntax to process quality, robust architecture design, rigorous testing, and systematic risk control, ensuring the development of highly stable and reliable systems in AI-assisted environments.

This skill enforces a rigorous, opinionated baseline for UI development, ensuring consistency across spacing, hierarchy, typography, and layout. It helps maintain high code quality by fixing structural and stylistic inconsistencies, particularly useful for polishing AI-generated or quickly developed interfaces to meet professional standards and accessibility requirements.

This skill simulates a senior engineer's final, deep audit of an entire codebase before committing or pushing to a remote repository. It systematically checks for junk files, dead code, security vulnerabilities (secrets, injection flaws), and performance bottlenecks (N+1 queries). Use this pre-flight check before any major release or public deployment to ensure maximum code quality and production readiness.

This skill provides deep technical expertise regarding the DWARF debugging format (v3-v5) and the DWARF standard. It assists in analyzing, parsing, verifying, and generating debug information from compiled binaries. Use cases include answering questions about the DWARF specification, writing code that interacts with DWARF data, and utilizing tools like `dwarfdump` or `readelf` for structural validation and quality metric extraction.

This guide outlines a systematic process for transforming any recurring technical pattern, complex debugging solution, or workflow into a standalone, portable skill. It details the multi-step extraction workflow, including pattern identification, structured documentation generation (SKILL.md), and quality control checks to ensure the skill can be reused across different projects.

This assistant streamlines the workflow for managing GitHub issues using the GitHub CLI. It guides users through structured processes to create, list, and view issues. When creating, it prompts for detailed information (including issue type, reproduction steps, and expected vs. actual behavior for bugs), ensuring high-quality issue reports. It also allows filtering and listing of existing issues.

A comprehensive skill for advanced text and typography handling within the Makepad framework. It provides robust mechanisms for font configuration, text layout management (Layouter), and high-quality GPU-based text rendering using Signed Distance Fields (SDF). Use this skill when needing to implement rich text displays, manage complex font styles, or ensure crisp, scalable typography across different resolutions.

This comprehensive reference guide for Makepad provides essential materials for developers. It covers common errors, debugging tips, API documentation, and advanced techniques for adaptive and high-quality layouts. Use this resource when troubleshooting, looking up specific API details, or ensuring code robustness across different platforms.