Skill UI

This skill manages comprehensive software development life cycle operations—including issue triage, PR reviews, version releases, bug fixing, and marketing content generation. It operates using a parallel "agent army" of dynamic agents (10-20 per task), requiring the user to adopt an Engineering Manager (EM) mindset. Key principles involve rigorous citation checking (anti-hallucination) and applying multiple roles (Product Owner, Open Source Contributor, Architect) simultaneously to ensure robust, safe, and user-centric outcomes across all platforms.

This skill file acts as a Japanese translation placeholder for the 'product-capability' module. It indicates that the localized content is currently under development and not functional. Users should refer to the original English skill file for the core functionality and detailed implementation.

This tool analyzes specification files to extract all defined requirements and cross-references them with the current codebase implementation status. It systematically checks for unimplemented features, searches existing GitHub issues to prevent duplicates, and automatically generates detailed issues using a standardized feature request template. It is essential for robust product management, ensuring every defined requirement is tracked and scheduled for development.

A comprehensive workflow designed for Product Managers and technical leads to systematically identify, prioritize, and document missing features in a software project. It guides users through project understanding, gap analysis, applying a scoring matrix for prioritization, developing detailed Minimum Viable Product (MVP) specifications, and structuring the output as actionable development issues (e.g., GitHub Issues) with dependency mapping and workload optimization.

This generator provides a comprehensive, production-ready template for building a Model Context Protocol (MCP) server using Kotlin. It sets up the full Gradle structure, integrates essential dependencies (MCP SDK, Ktor, Coroutines), and includes robust placeholders for custom tools, error handling, and testing, accelerating the development of context-aware backend services.

A powerful toolset for automating core PostHog product analytics tasks via Rube MCP. It enables developers and analysts to capture user events, list and filter event data, manage feature flags (creation, retrieval, listing), and inspect user profiles. Ideal for integrating advanced product tracking and A/B testing workflows directly into development pipelines.

This tool generates a complete, production-ready project scaffold for a Model Context Protocol (MCP) server written in Ruby. It includes the necessary file structure, gem dependencies, Rake tasks, and boilerplate code for defining tools, prompts, and resources, accelerating backend development for AI applications.

The GitHub Copilot SDK empowers developers to embed advanced, agentic AI workflows directly into their proprietary applications. It provides a programmatic interface to Copilot's production-tested agent runtime, allowing you to define complex agent behaviors, manage tool invocation, process file edits, and implement real-time streaming responses. Ideal for building custom, highly intelligent tools and automating complex tasks across multiple languages (Python, TypeScript, Go, .NET).

This skill automatically generates an interactive SQL Notebook designed to validate changes in dbt models or snapshots. It processes changes identified from a GitHub PR or local repository, generating comparison queries that compare 'before' (production) and 'after' (development) data states. It is essential for embedding data observability and quality checks into CI/CD pipelines.

A comprehensive scaffold generator for building Model Context Protocol (MCP) servers using TypeScript and Node.js. This tool provides a complete, production-ready boilerplate, including proper project structure, dependency management (e.g., @modelcontextprotocol/sdk, zod), and implementation details for tools and resources. It supports both HTTP and stdio transport mechanisms, ensuring type safety, robust error handling, and best practices for modern backend development.

This guide outlines a systematic workflow for requesting code reviews, crucial for validating completed tasks, implementing major features, or preparing for merge. It emphasizes reviewing early and often, ensuring reviewers focus on the tangible work product rather than the development thought process. Learn how to use specific Git SHAs and structured templates to submit comprehensive feedback requests.

This guide provides a comprehensive, reproducible workflow for setting up an Algolia local development environment. It covers creating separate development indices, seeding mock data, and implementing client mocking for unit tests. This process ensures fast, safe iteration cycles, allowing developers to build and test search features without ever touching production data.