Skill UI

This agent simulates the profound cognitive structure, aesthetic intuition, and strategic mindset of Steve Jobs. It is designed for users who need to approach product development, design, or business strategy with a visionary, minimalist, and highly curated perspective. Use it to strip away complexity, define core user experiences, and articulate powerful, simple narratives.

An expert guide for developing production-grade AI applications, chatbots, and generative UIs using the Vercel AI SDK, Next.js, and React. Covers core concepts including streaming text (`streamText`), managing chat state (`useChat`), implementing complex tool/function calling, and ensuring robust structured data generation (JSON schemas) across multiple LLM providers.

This powerful orchestrator coordinates the entire product development lifecycle by routing inquiries across 16 specialized sub-skills (e.g., RICE, OKRs, UX research, analytics). It acts as a central hub for both deterministic task routing and running continuous, machine-gated discovery loops. Use it when you need to systematically prioritize features, run A/B tests, or drive a goal from initial assumption to validated repository code.

A structured methodology for de-risking product ideas and validating market opportunities. This framework guides teams through defining measurable outcomes, building Opportunity Solution Trees (OST), mapping critical assumptions (Desirability, Viability, Feasibility), and planning rigorous experiments (prototyping, interviews, usability tests) before committing to full development resources. Ideal for discovery sprints and pre-build validation.

A comprehensive guide for building robust, production-grade desktop applications using Electron. This skill covers advanced topics including multi-process architecture, mandatory security hardening (contextIsolation, sandbox), secure IPC communication, and best practices for packaging, code signing, and implementing auto-update features.

Opinionated Docker development assistant that analyzes Dockerfiles, docker-compose setups, multi-stage builds, and container security to shrink images, speed builds, and enforce production-ready practices when users need optimized containers.

A comprehensive strategy for regression testing in AI-assisted development. It focuses on identifying systematic blind spots, such as schema inconsistencies between sandbox and production paths, and integrating automated bug checks into the development workflow. This guide teaches how to build robust, database-free API tests to ensure code quality when AI models contribute to the codebase.

Comprehensive Snowflake development assistant covering SQL best practices, dynamic tables, streams, tasks, Snowpipe, Cortex AI functions/agents, Snowpark Python, dbt integration, performance tuning, and security hardening for production data platforms.

A comprehensive multi-agent feedback loop inspired by Generative Adversarial Networks (GANs), designed to build complex, production-ready applications. It separates generation from evaluation, using a 'ruthlessly strict' evaluator (QA) to drive continuous iteration. This process ensures high design quality, robust functionality, and originality, overcoming the limitations of single-agent self-critique. Ideal for full-stack feature development and complex product specification implementation.

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 skill provides a systematic approach to debugging and error recovery when tests fail, builds break, or behavior deviates from expectations. It emphasizes stopping feature development to preserve evidence, diagnosing root causes through a triage checklist, and guarding against recurrence with tests. Ideal for developers facing runtime bugs, production incidents, or unexpected errors, ensuring efficient problem-solving without guessing.