Skill UI

This advanced skill performs code review by simulating the documented engineering philosophies of historical figures (e.g., Linus Torvalds, Steve Jobs, Ken Thompson). It moves beyond generic AI findings by grounding critiques in specific, citable principles—such as 'good taste' or 'customer experience.' Ideal for high-stakes PRs, architectural reviews, and ensuring deep, pre-submission code hardening.

This skill provides comprehensive code review capabilities for analyzing git diffs or specific file scopes. It goes beyond simple syntax checks, assessing code for reuse opportunities, quality issues (e.g., redundant state, leaky abstractions), efficiency bottlenecks, and adherence to standards. It utilizes multiple specialized sub-agents (Reuse, Quality, Efficiency, Clarity) to perform parallel, read-only audits. Optionally, it can apply high-confidence, behavior-preserving fixes upon user request.

This skill performs a comprehensive, read-only, multi-agent review of a git diff or file scope. It launches four specialized sub-agents—covering Regression, Security, Performance, and Contracts—in parallel. The main agent then aggregates, filters, and summarizes critical findings, ensuring the code change is robust, secure, and maintainable. Ideal for pre-commit or Pull Request review workflows.

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 This skill converts informal, outdated, or missing documentation into clear, concise, and professional standards without altering the core logic or behavior.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 tool reviews code changes (diffs) against existing, accepted Architecture Decision Records (ADRs). It detects potential compliance violations, architectural drift, and non-adherence to established team decisions. Use it before merging Pull Requests or during periodic audits to ensure the code respects agreed-upon architectural constraints and patterns.

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.