📤 Toru – Export Manager

Toru - Export Manager

The Silent Finalization Agent

Toru works behind the scenes to wrap everything into production-ready packages. You don't hear from him directly - you just ship.

Core Responsibilities:

• File Organization: Clean, logical folder structures

• Repository Generation: Complete GitHub repos with proper documentation

• Asset Optimization: Compressed, web-ready files

• Quality Assurance: Final validation before export

Toru's Process:

1. Collection & Validation

   • Gathering outputs from all agents

   • Consistency checking across deliverables

   • Missing component identification

2. Structure & Organization

   • Folder hierarchy creation

   • File naming standardization

   • Cross-reference validation

3. Export Package Creation

   • Multiple format generation

   • Archive preparation

   • Repository initialization

Complete Export Package:

startup-package/
├── README.md
├── business/
│   ├── business-model-canvas.pdf
│   ├── market-analysis.md
│   ├── financial-projections.xlsx
│   └── pitch-deck.pdf
├── design/
│   ├── brand-identity/
│   │   ├── logo-files/
│   │   ├── color-palette.pdf
│   │   └── typography-guide.pdf
│   ├── ui-mockups/
│   │   ├── mobile/
│   │   ├── desktop/
│   │   └── design-system.pdf
│   └── assets/
│       ├── icons/
│       └── illustrations/
├── technical/
│   ├── architecture.md
│   ├── api-specification.yaml
│   ├── database-schema.sql
│   └── deployment-guide.md
├── repository/
│   ├── frontend/
│   ├── backend/
│   ├── documentation/
│   └── deployment/
└── exports/
    ├── presentation.pdf
    ├── one-pager.pdf
    └── executive-summary.md

Repository Features:

• Complete README with setup instructions

• CI/CD pipeline configuration

• Environment variable templates

• Docker containerization

• Deployment scripts for major platforms

Toru's Standards:

• Every file must have a clear purpose

• All documentation must be actionable

• Repository must be deployment-ready

• No placeholder content anywhere

---

Agent Workflow Coordination

Execution Flow

Input: Startup Idea
    ↓
[Clara] → Validation & Reality Check
    ↓
[Vince] → Name Generation & Brand Strategy  
    ↓
[Mira] → Business Model & Revenue Design
    ↓
[Dex] → Product Definition & Feature Scope
    ↓
[Lana] → Interface Design & User Experience
    ↓
[Toru] → Package Creation & Export
    ↓
Output: Complete Startup Package

Context Flow Example

Initial Input: "AI-powered customer service tool"

Clara's Memory:
- Market analysis: Saturated space, 200+ competitors
- Validation status: CONDITIONAL - needs differentiation
- Recommended focus: SMB market, specific industry vertical

Vince's Memory:
- Name concepts: ServiceBot, HelpFlow, SupportAI
- Selected: "Resonance" - implies harmony between human/AI
- Domain secured: resonance.ai
- Brand direction: Professional but approachable

Mira's Memory (inherits Clara + Vince):
- Target market: SMB professional services
- Revenue model: Freemium SaaS + usage tiers
- Brand name: Resonance
- Unit economics: $49/month starting point

Dex's Memory (inherits all previous):
- Product name: Resonance
- Target: SMB professional services
- Business model: Freemium SaaS
- Core feature: AI-human handoff optimization
- MVP scope: 6 core features, 12-week timeline

Quality Gates

Each agent validates inputs and outputs according to their specialized criteria:

Clara's Gates:

• Market size validation

• Competitive differentiation check

• Technical feasibility assessment

• Business model viability

Vince's Gates:

• Name availability verification

• Brand coherence validation

• Market resonance testing

• Trademark conflict checking

Mira's Gates:

• Revenue model sustainability

• Unit economics validation

• Scaling mechanism verification

• Risk assessment completion

Dex's Gates:

• Feature necessity validation

• Technical scope feasibility

• Timeline reality checking

• Success metric definition

Lana's Gates:

• User experience flow validation

• Accessibility compliance checking

• Mobile usability verification

• Design system consistency

Toru's Gates:

• File completeness validation

• Cross-reference consistency

• Quality assurance checking

• Deployment readiness verification

---

Best Practices for Agent Interaction

Maximizing Agent Effectiveness

Input Optimization:

• Provide complete context to each agent

• Be specific about constraints and requirements

• Accept agent feedback and recommendations

• Use revision capabilities for refinement

Agent-Specific Tips:

Clara Interactions:

• Be honest about market knowledge and experience

• Provide complete problem context

• Accept hard feedback on idea viability

• Use competitive analysis insights

Vince Interactions:

• Describe target audience communication style

• Share naming preferences and constraints

• Consider emotional tone and brand personality

• Test names with real users

Mira Interactions:

• Be realistic about customer acquisition capabilities

• Share capital and resource constraints

• Focus on single revenue stream initially

• Validate unit economics assumptions

Dex Interactions:

• Trust feature elimination recommendations

• Focus on core user workflow

• Accept MVP scope limitations

• Plan for iterative development

Lana Interactions:

• Describe user technical comfort level

• Share accessibility requirements

• Consider primary usage contexts

• Review user flow logic over aesthetics

Toru Interactions:

• Choose export formats matching team capabilities

• Ensure repository deployment preferences

• Review documentation completeness

• Plan development team handoff

Common Success Patterns

Ideas That Work Best:

• Clear problem statements with identifiable pain points

• Specific target markets (not "everyone")

• Technically achievable solutions

• Business models aligned with user behavior

• Founder domain expertise or market access

Typical Agent Refinements:

• Clara narrows target markets for focus

• Vince provides multiple name options for selection

• Mira recommends single revenue stream starts

• Dex cuts feature scope by 60-70%

• Lana prioritizes mobile-first design

• Toru ensures deployment readiness

The agent ecosystem works best when users embrace the collaborative process and trust each agent's specialized expertise while providing clear context and constraints.