project-designer

name	project-designer
description	name: project-designer Use when this capability is needed.

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name: project-designer description: Comprehensive project design and ideation skill for AI/ML and web/mobile applications. Use when users present initial project ideas that need enhancement, architectural guidance, technology stack recommendations, feature expansion, or implementation strategy. Triggers include requests like "help me design a [project type]", "I want to build [concept] but need to flesh out the idea", "what features should I add to [project]", "recommend a tech stack for [idea]", or "enhance my project concept". Provides structured design thinking, architectural patterns, feature frameworks, and technology recommendations through progressive disclosure of specialized reference materials.

Project Designer

Overview

Transform initial project concepts into comprehensive, well-architected designs through systematic analysis, feature enhancement, and technology recommendations. This skill guides you through structured ideation, architectural decision-making, and strategic planning for AI/ML agents, web applications, and mobile projects.

Workflow

Phase 1: Concept Understanding

Objective: Thoroughly understand the user's core idea, goals, and constraints.

Actions:

1. Extract Core Information:

   • Project type (AI/ML agent, web app, mobile app)
   • Primary problem being solved
   • Target users or personas
   • Key constraints (budget, timeline, team size, technical expertise)

2. Identify Gaps:

   • Missing requirements or specifications
   • Unclear objectives or success criteria
   • Undefined user needs

3. Ask Clarifying Questions: Use the AskUserQuestion tool when:

   • Project scope is ambiguous
   • Multiple architectural approaches are viable
   • Technical requirements are unclear
   • User needs are not fully specified

Phase 2: Architecture and Technology Guidance

Objective: Recommend appropriate architectural patterns and technology stacks based on project requirements.

Reference Materials: Load based on project type and needs:

• references/ai-ml-patterns.md: For AI/ML agents, RAG systems, conversational AI, ML pipelines
• references/web-app-patterns.md: For web/mobile applications, API design, full-stack systems
• references/tech-stack-matrix.md: For technology selection across all project types

Actions:

1. Identify project type and match to established architectural patterns
2. Recommend technology stack based on team expertise, budget, scalability needs
3. Explain architectural decisions with clear rationale and trade-offs

Phase 3: Feature Discovery and Enhancement

Objective: Expand the initial concept with comprehensive, prioritized features.

Reference Material: references/feature-frameworks.md

Actions:

1. Apply feature discovery methods (Jobs-to-be-Done, User Story Mapping, Feature Analogies)
2. Categorize features: Must-Have (MVP), Should-Have (Post-MVP), Could-Have (Future)
3. Apply prioritization framework (RICE, ICE, Kano model)
4. Consider enhancement dimensions: capability, UX, integration, intelligence

Phase 4: Structured Documentation

Objective: Produce comprehensive project documentation using standardized templates.

Asset Templates: Use appropriate templates based on user needs:

• assets/templates/project-brief.md: Complete project specification
• assets/templates/architecture-blueprint.md: Detailed technical architecture
• assets/templates/feature-roadmap.md: Prioritized feature timeline

Phase 5: Iterative Refinement

Objective: Refine the design based on user feedback and emerging requirements.

Actions:

1. Present recommendations clearly with critical decisions highlighted
2. Respond to user feedback and adjust recommendations
3. Provide detailed explanations of recommendations
4. Start high-level, drill down into specifics as needed

Usage Patterns

Pattern 1: New Project Ideation

User presents initial idea → Understand requirements → Recommend architecture/stack → Enhance features → Generate project-brief.md

Pattern 2: Existing Project Enhancement

User has working system → Understand current state → Apply feature frameworks → Prioritize enhancements → Generate feature-roadmap.md

Pattern 3: Technology Stack Consultation

User needs tech guidance → Clarify requirements → Consult tech-stack-matrix.md → Recommend stack with rationale

Pattern 4: Architecture Design

User needs technical design → Gather requirements → Recommend pattern → Define components → Generate architecture-blueprint.md

Best Practices

Communication:

• Follow workflow phases sequentially
• Use progressive disclosure (high-level first, details when needed)
• Provide concrete, implementable recommendations

Technical Recommendations:

• Balance pragmatism with best practices
• Consider team capabilities and constraints
• Provide primary recommendation + alternatives with trade-offs

Feature Design:

• Ground features in user problems and needs
• Define clear MVP scope
• Use quantitative prioritization frameworks

Reference Material Usage

Load references/ai-ml-patterns.md when: AI agents, RAG systems, ML pipelines, NLP/CV applications

Load references/web-app-patterns.md when: Web/mobile apps, API design, authentication, database patterns

Load references/tech-stack-matrix.md when: Technology selection, team/budget considerations, scalability needs

Load references/feature-frameworks.md when: Feature ideation, prioritization, user-centric design, roadmap planning

Anti-Patterns to Avoid

Don't:

• Suggest over-engineered solutions for simple problems
• Ignore constraints (budget, timeline, expertise)
• Skip Phase 1 understanding to rush to recommendations
• Load all references unnecessarily
• Make assumptions without clarifying

Do:

• Ask clarifying questions when unclear
• Tailor recommendations to specific context
• Provide rationale for recommendations
• Use reference materials to support recommendations
• Balance comprehensiveness with actionability

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Source: abdullahmalik17/voice-assistant17 — distributed by TomeVault.