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 comprehensive toolkit provides 23 production-ready, specialized engineering skills covering the entire software development lifecycle (SDLC). Expertise areas include full-stack development, system architecture, DevOps, cybersecurity, AI/ML, and data engineering. It is designed to guide advanced AI agents (like Claude or Codex) through complex, multi-disciplinary development tasks, ensuring high code quality and robust system design.

A comprehensive toolkit providing 10 production-ready agent skills covering the entire product lifecycle. Features include PM frameworks (RICE, OKR), agile planning, UX research, UI design system generation, competitive teardowns, and SaaS scaffolding, helping teams streamline development from concept to launch.

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.

Guides building secure production-grade Electron desktop apps with isolated renderer processes, hardened IPC via preload bridges, packaging through electron-builder, code signing, and full auto-update workflows.

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.

This skill guides the implementation of automated security scanning for Infrastructure as Code (IaC) templates using industry-leading tools like Checkov and tfsec. It focuses on integrating security checks into CI/CD pipelines to detect misconfigurations (e.g., public buckets, open ports) in Terraform, CloudFormation, and Kubernetes manifests before they reach production, thereby enforcing policy-based governance and securing the development lifecycle.

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.