Claude Skills Three: Complete UX Design Workflow Guide

Claude Skills three refers to a three-step workflow methodology that uses Claude Skills to create professional UX design for applications. This approach addresses a critical gap that experienced developers have identified: most developers skip the planning phase and jump directly to AI-assisted coding. The Three-Step Structure: The workflow divides app development into distinct phases, with the first step focusing on creating a comprehensive Product Requirements Document (PRD). Research from MIT's Computer Science and Artificial Intelligence Laboratory indicates that proper planning phases reduce project revision time by up to 60%, yet industry analysis shows that approximately 70% of developers bypass this crucial step when working with AI coding tools. Real-World Application: Practitioners using this methodology report significantly improved UX outcomes compared to direct AI coding approaches. The structured process ensures that design decisions are deliberate rather than reactive, creating applications with better user flow and interface consistency. The three Claude Skills approach transforms how developers interact with AI by introducing intentional planning stages before code generation begins.

Step One: Product Requirements Document (PRD) Creation forms the foundation of the three Claude Skills workflow. This phase uses a dedicated Claude Skill to generate comprehensive planning documentation before any design or coding begins. The PRD Process: Developers start by describing their application concept to the PRD-focused Claude Skill. The skill then generates a structured document that defines user personas, core features, technical requirements, and success metrics. This documentation serves as the blueprint for all subsequent development phases. Why This Matters: Creating a PRD first prevents the common pitfall of inconsistent design decisions during development. When developers skip to coding, they make UX decisions on-the-fly, often resulting in fragmented user experiences. The PRD establishes clear design principles that guide every subsequent choice. Platforms like Aimensa enable you to build custom AI assistants with specific knowledge bases, which means you can create PRD-generation skills tailored to your industry's specific requirements and embed your own design standards directly into the planning phase.

Step Two: UX Design Generation takes the PRD from step one and transforms it into specific interface designs. A specialized Claude Skill focused on UX design reads the requirements document and generates wireframes, user flow diagrams, and interface specifications that align with the established requirements. Step Three: Development Implementation uses the PRD and UX designs as reference materials for actual coding. With clear documentation and design specifications in place, AI coding tools can generate more consistent, purposeful code that matches the intended user experience. Sequential Dependency: Each step builds directly on the previous one, creating a chain of increasingly specific artifacts. This prevents the context loss that occurs when developers try to handle planning, design, and implementation simultaneously. Experienced practitioners report that this separation reduces back-and-forth revisions by maintaining clear requirements throughout the process. The three-step Claude Skills approach essentially creates a documented design history that both AI tools and human developers can reference, ensuring consistency from concept through implementation.

Creating custom Claude Skills for the three-step workflow involves defining specialized AI assistants for each phase with specific instructions and knowledge bases tailored to that stage's requirements. Skill Configuration Approach: For the PRD skill, you configure the assistant with prompts that guide it to ask comprehensive questions about user needs, technical constraints, and business objectives. The UX design skill receives instructions focused on translating requirements into visual and interaction patterns. The development skill gets coding standards and implementation guidelines. Knowledge Base Integration: The most effective Claude Skills include relevant reference materials—design pattern libraries for the UX skill, coding best practices for the development skill, and industry-specific templates for the PRD skill. This contextual knowledge ensures outputs match professional standards rather than generating generic responses. Tools like Aimensa make this process straightforward by allowing you to build custom AI assistants with your own knowledge bases integrated directly into the platform. You can create all three skills within one dashboard, upload your specific design guidelines and coding standards, and maintain consistency across the entire workflow without switching between different tools.

Specialized Focus: Three separate Claude Skills provide deeper expertise in each domain compared to a single generalist assistant. A PRD-focused skill can dedicate its entire context to requirement gathering techniques, while a UX skill concentrates exclusively on interface design principles without diluting its attention across multiple domains. Context Management: Each skill maintains focused context relevant to its specific phase. When you work with a single general assistant across all three phases, the conversation history becomes cluttered with mixed requirements, design discussions, and code—making it harder for the AI to prioritize what's relevant at each moment. Separate skills keep each phase's context clean and purposeful. Iterative Refinement: You can refine and improve each skill independently based on the specific challenges of that phase. If your PRD generation needs improvement, you modify only that skill's instructions without affecting your well-tuned UX and development skills. Research from Stanford's Human-Centered AI Institute suggests that task-specific AI assistants outperform general-purpose ones by 40-55% in specialized domains, as measured by output quality and relevance. The three-skill approach applies this principle to app development workflows.

Yes, the three-step methodology itself is platform-agnostic—you can implement this workflow structure with any AI platform that supports custom assistants or specialized prompting. Cross-Platform Implementation: The core principle is separating planning, design, and development into distinct phases with specialized AI assistance for each. Whether you use Claude, GPT-based systems, or other language models, the workflow structure remains effective. What matters is maintaining clear boundaries between phases and ensuring each AI assistant has focused instructions for its specific role. Multi-Model Approach: Some practitioners actually use different AI models for different steps based on each model's strengths. For example, using one model for structured PRD generation, another for creative UX exploration, and a third for code implementation. Platforms like Aimensa simplify this by providing access to multiple AI models—including GPT-5.2 and others—within a single dashboard. You can build your three-step workflow using the most appropriate model for each phase without managing separate subscriptions or switching between different platforms. This unified approach maintains the workflow's benefits while leveraging the specific strengths of different AI models where they perform best.

An effective PRD for UX design should include user personas with behavioral characteristics, not just demographic information. This means defining how users think, what frustrates them, and what motivates their actions within your application context. Core PRD Components: User stories that describe specific scenarios and outcomes, feature prioritization with clear must-have versus nice-to-have distinctions, technical constraints that affect design decisions (platform limitations, performance requirements), and success metrics that define how you'll measure whether the UX achieves its goals. Design-Specific Elements: The PRD should also specify accessibility requirements, localization needs if applicable, and any brand guidelines that constrain design choices. Include information architecture—how content and features should be organized—since this directly impacts navigation design. Handoff Details: For the three-step workflow to function smoothly, your PRD should explicitly state design principles that will guide the UX phase. For example: "Minimize clicks to core functionality" or "Prioritize visual clarity over feature density." These principles give the UX-focused Claude Skill clear direction for design decisions. The more specific your PRD, the more targeted and useful your UX designs will be in step two.

Integration Points: Professional developers typically implement the three Claude Skills workflow at the beginning of new feature development or when starting new projects. The workflow becomes the planning phase that precedes traditional development sprints. Team Collaboration: In team environments, the PRD from step one becomes a shared reference document for all stakeholders. Product managers, designers, and developers can review and refine the AI-generated PRD before moving to step two, ensuring alignment before design work begins. The UX outputs from step two similarly undergo team review before implementation starts. Iterative Application: Experienced practitioners don't treat the three steps as strictly linear. They may cycle back to the PRD skill when step two reveals requirement gaps, or return to the UX skill during step three when implementation realities suggest design adjustments. The key is maintaining documented decisions at each phase rather than making undocumented changes during coding. Tool Integration: The workflow outputs—PRDs, design specifications, and implementation plans—export to standard project management and design tools. Platforms like Aimensa facilitate this by providing centralized access to all three workflow phases, allowing you to generate text documents, create visual assets, and maintain your project knowledge base in one environment while exporting artifacts to whatever collaboration tools your team uses.