Skill UI

Logic Lens is an advanced AI skill that performs deep, formal logic-driven code review. Unlike standard linters, it analyzes code for complex issues like race conditions, security vulnerabilities (SQL injection, XSS), type safety flaws, and algorithmic errors across 9 critical risk categories. It is ideal for pre-merge PR reviews, auditing sensitive code paths, and refactoring complex business logic.

This skill generates a complete, standalone, and highly structured pricing page in standalone HTML. It automatically respects the active design system (colors, typography, components) and supports defining 2-4 plan tiers, including feature cards, comprehensive comparison tables, and an FAQ section. Ideal for product marketing and sales-focused websites.

Generates a complete, single-page SaaS landing page structure. This tool produces a fully contained HTML file, integrating required sections such as Hero, Features, Social Proof, Pricing, and Call-to-Action (CTA). It strictly adheres to an existing design system for colors, typography, and layout, making it ideal for product launches and marketing campaigns.

Squirrel is an advanced, full-cycle AI coding skill designed to guide software development from concept to production. It automatically detects the current project state (greenfield, in-progress, or mature) and adapts an 8-phase engineering pipeline. This comprehensive workflow covers planning, building, rigorous testing, bug hunting, code polishing, documentation, and deployment readiness, ensuring high-quality, maintainable results.

Cavecrew provides a set of specialized subagents (investigator, builder, reviewer) designed for efficient code development delegation. Unlike vanilla agents, Cavecrew compresses output results, drastically reducing the token usage injected back into the main context. This allows users to maintain context over much longer, complex coding sessions, effectively preventing context exhaustion during large-scale refactoring, debugging, or multi-step feature development.

A robust toolkit for managing dependencies in Go. It uses code generation to resolve the entire dependency graph at compile time, eliminating the need for runtime containers or reflection. This ensures that all missing dependencies are caught during the build step (`wire ./...`), leading to highly robust, predictable, and type-safe applications. Ideal for building large-scale, enterprise Go microservices.

This skill guides the development of robust, Unix-like Command Line Interfaces (CLIs) in Go using the industry-standard `cobra` library. It covers the entire lifecycle of a CLI application, including command tree setup, managing persistent vs. local flags, advanced positional argument validation, and utilizing the full execution hook chain (RunE). Ideal for engineers adopting or maintaining sophisticated Go CLI projects.

A powerful utility that converts Markdown (`.md`) files into richly formatted Word (`.docx`) documents. Built using pure JavaScript, it requires no external dependencies like Pandoc or LibreOffice. Features include automatic generation of title pages (from YAML front-matter), dynamic table of contents, and accurate embedding of PNG images, making it ideal for technical documentation and reports.

This skill analyzes the comprehensive commit history between two branches to generate a concise, objective, and highly structured Pull Request (PR) description. It automatically groups changes by domain, summarizing the purpose, technical notes, and user impact, ensuring consistency and minimizing manual documentation effort.

This skill assesses the AI readiness of a codebase by running a comprehensive audit against multiple technical and governance pillars. It generates a self-contained, detailed HTML dashboard that provides an overall maturity score, identifies strengths, and, most importantly, delivers a prioritized remediation plan (Fix First / Fix Next). Ideal for auditing repositories before major AI feature development.

This skill generates comprehensive, tailored instruction files for AI coding assistants like GitHub Copilot and Claude. It supports multiple strategies (flat or nested) and is specifically designed for managing complex monorepos by creating area-scoped instructions using `applyTo` globs. Use it to standardize AI tooling usage across large codebases.

A deep-dive session designed to stress-test a technical plan against an existing domain model. This process forces the user to resolve ambiguities, sharpen terminology, and validate decisions by cross-referencing code and existing documentation (CONTEXT.md, ADRs). It ensures architectural rigor and completeness before implementation.