Skill UI

Rewrites overly formal or vague code review comments generated by AI or corporate tools. It cuts out filler phrases like "I noticed..." or "It might be worth considering...", transforming them into direct, actionable feedback. The output uses a clear format (Location: Severity: Observation. Fix.) that sounds like it came from a helpful, experienced teammate, significantly improving the quality and reception of PR feedback.

A constraint-based protocol designed to professionalize existing code by normalizing comments and performing safe, non-semantic cleanup. This skill converts informal, outdated, or missing documentation into clear, concise, and professional standards without altering the core logic or behavior. Ideal for preparing code for code review, open-sourcing, or formal documentation.

A comprehensive guide covering standards for building highly maintainable Flutter applications using Dart. It details best practices across architecture (MVVM, layered design), state management (Cubit/BLoC), folder structure, widget implementation, and testing strategies. Use this skill when writing new code, reviewing existing codebases, or planning complex features to ensure scalable and clean development.

A specialized presentation deck template adopting a cyberpunk, CRT terminal aesthetic. It is ideal for conducting deep technical reviews, such as benchmarking CLI agents, demonstrating code diffs, or presenting complex development tool analyses. Features a dark background, grid lines, command prompt indicators, and structured tagging for maximum professional impact.

Execute discrete, bounded coding, review, or verification tasks using the Codex CLI as an isolated subagent. This tool is ideal for tasks requiring explicit file ownership, such as isolated refactoring, feature implementation, or independent code verification, without relying on continuous conversational context. It runs in a safe sandbox environment.

This guide provides a comprehensive reference on Agent Skills, a standardized and powerful method for providing structured context, workflows, and tools to Large Language Models (LLMs). It details the anatomy of the SKILL.md file, the three-stage progressive disclosure loading process, and best practices for creating both capability (tool-based) and process (workflow-based) primitives. Use this resource when designing, reviewing, or debugging agent-driven applications to ensure reliable and context-efficient performance.

This tool reviews code changes (via diff) against accepted Architecture Decision Records (ADRs) to detect potential architectural drift, compliance violations, and adherence issues. It simulates a pre-merge check, ensuring that new code respects established architectural constraints and decisions made by the team. Ideal for CI/CD pipelines and mandatory code reviews.

This comprehensive guide outlines a rigorous, human-in-the-loop engineering workflow for leveraging advanced AI agents to complete complex implementation tasks. It mandates defining clear completion criteria, decomposing work into manageable units, and strategically routing models (Haiku, Sonnet, Opus) based on task complexity. The workflow emphasizes continuous evaluation, regression testing, security review of invariants and edge cases, and strict cost discipline to ensure robust and reliable software development using AI assistance.

Generates structured CodeTour `.tour` files in JSON format for detailed, step-by-step code walkthroughs. This tool is essential for onboarding new engineers, conducting architectural reviews, or documenting complex PR changes. It provides a guided narrative that directs users to specific files and line anchors, ensuring a structured and focused learning journey.

Code Health MCP provides structural maintainability feedback and design-level health scores, going beyond standard linting. It is crucial for AI-assisted coding workflows, allowing users to review code quality, verify score deltas after changes, and gate commits or pull requests to prevent codebase regression. Use it when refactoring, updating legacy modules, or ensuring the integrity of large diffs.

This comprehensive guide establishes foundational coding standards and best practices for enhancing code quality, readability, and maintainability across all projects. It covers naming conventions, immutability patterns, error handling, and core design principles like KISS, DRY, and YAGNI, applicable to TypeScript, JavaScript, and React development. Use it when starting a new module or performing code reviews.

A comprehensive guide derived from C++ Core Guidelines, enforcing modern, safe, and idiomatic practices for C++17, C++20, and C++23. It emphasizes crucial principles like RAII, type safety, immutability, and proper resource ownership using smart pointers. Use this when writing new features, reviewing existing code, or making architectural decisions to ensure code quality and consistency.