new-product

name	new-product
description	Deep research and architecture design for new product development. Use when Codex should run the converted new-product workflow. Inputs: input.

New Product

Converted Claude skill workflow for Codex/OpenAI use.

Source

Converted from skills/new-product/SKILL.md.

Bundled Resources

Supporting files copied from the Claude source:

• references/README.md

Converted Instructions

The content below was adapted from the Claude source. Rewrite tool and runtime assumptions as needed when they refer to Claude-only features.

New Product - Deep Research & Architecture Design

Given documentation or a product description, perform deep research and cross-reference technologies to determine the optimal architecture for building a new application.

Your Task

You are orchestrating a deep research workflow to design product architecture. Follow these stages in order, using parallel agents for deep research, and present a final comprehensive review at the end.

---

Stage 0: Initialize Product Workspace

Parse input argument:

• If the input is a file path (contains / or ends in .md, .txt, etc.), read the documentation
• If the input is plain text, treat it as a product description

Extract product name:

• From documentation: look for project title, product name
• From description: derive a short kebab-case name (e.g., "real-time chat app" → "realtime-chat")

Create folder structure:

mkdir -p /job-queue/product-{name}/research-notes

Output:

• Display: "🚀 Stage 0/6: Workspace created at /job-queue/product-{name}/"
• Store the product name for use in later stages

---

Stage 1: Big Idea & High-Level Research

Goal: Generate a comprehensive high-level understanding of the product and initial technology landscape.

Step 1.1: Analyze Input

If documentation provided:

• Read all provided files
• Extract key concepts: features, user flows, requirements
• Identify technical hints or constraints mentioned

If product description provided:

• Parse the description for:
  • Core functionality
  • Target users
  • Key features
  • Scale expectations

Step 1.2: Ask Initial Clarifying Questions

Use ask the user to gather essential context:

Questions to ask:

1. Product Vision

   • Header: "Vision"
   • Question: "What is the primary goal of this product?"
   • Options:
     • "Solve a specific problem for users" - "Focus on targeted pain points"
     • "Innovate in an existing space" - "Bring new approach to established market"
     • "Integrate multiple services" - "Connect disparate systems"
     • "Explore new technology" - "Research/experimental project"

2. Scale & Audience

   • Header: "Scale"
   • Question: "What scale are you targeting?"
   • Options:
     • "Prototype/MVP (100s of users)" - "Proof of concept"
     • "Small-Medium (1,000s of users)" - "Early stage product"
     • "Medium-Large (100,000s of users)" - "Established product"
     • "Enterprise scale (1M+ users)" - "High-scale production"

3. Deployment Context

   • Header: "Deployment"
   • Question: "Where will this product run?"
   • Options:
     • "Cloud-hosted (AWS/GCP/Azure)" - "Managed infrastructure"
     • "Self-hosted/On-prem" - "Customer infrastructure"
     • "Edge/Distributed" - "CDN, edge compute"
     • "Hybrid" - "Mix of cloud and local"

Step 1.3: High-Level Technology Research

Use WebSearch to research broad technology landscape:

Search queries:

• "[product type] architecture patterns 2026"
• "[product type] technology stack comparison 2026"
• "[product type] best practices 2026"
• "[scale level] architecture considerations 2026"

For each search result:

• Use WebFetch to read top 2-3 articles
• Extract: common patterns, recommended technologies, pitfalls to avoid

Save research notes:

Write to: /job-queue/product-{name}/research-notes/stage1-research.md

Step 1.4: Generate big-idea.md

Content structure:

# Big Idea: [Product Name]

## Product Overview
[2-3 paragraphs describing what the product does, who it's for, and why it matters]

## Core Value Proposition
[What makes this product unique or valuable]

## High-Level Architecture Approach
[Based on research, what architectural style makes sense?]
- Monolithic vs Microservices
- Serverless vs Server-based
- Event-driven vs Request-response
- [Any other key architectural decisions]

## Technology Landscape
[Summary of technology options researched]

### Frontend Options
- Option 1: [technology] - [pros/cons]
- Option 2: [technology] - [pros/cons]
- Option 3: [technology] - [pros/cons]

### Backend Options
- Option 1: [technology] - [pros/cons]
- Option 2: [technology] - [pros/cons]
- Option 3: [technology] - [pros/cons]

### Database Options
- Option 1: [technology] - [pros/cons]
- Option 2: [technology] - [pros/cons]

### Deployment Options
- Option 1: [platform] - [pros/cons]
- Option 2: [platform] - [pros/cons]

## Key Challenges Identified
1. [Challenge 1 and mitigation approach]
2. [Challenge 2 and mitigation approach]
3. [Challenge 3 and mitigation approach]

## Next Steps
- Deep dive into runtime execution patterns
- Research abstraction layer approaches
- Evaluate integration strategies
- Design output rendering pipeline

## Research Sources
[List of URLs and documentation consulted]

Save to:

Write to: /job-queue/product-{name}/big-idea.md

Output:

• Display: "✅ Stage 1/6: Big idea generated at product-{name}/big-idea.md"

---

Stage 2-5: Parallel Deep Research for Architectural Documents

Launch 4 parallel research agents using the Task tool with skill="general-purpose".

Each agent is assigned one architectural document and operates independently.

Stage 2: Runtime Execution Research (Agent 1)

Agent task:

You are researching runtime execution architecture for: [Product Name]

CONTEXT:
[Include big-idea.md content]

YOUR GOAL:
Create a comprehensive `runtime-execution.md` document that explains how the system executes work.

RESEARCH APPROACH:
1. Based on the technology options in big-idea.md, research each option's runtime model
2. Use WebSearch to find:
   - "[technology] runtime architecture 2026"
   - "[technology] execution model 2026"
   - "[technology] process lifecycle 2026"
   - "[technology] concurrency model 2026"

3. Use WebFetch to read official documentation for top candidates

4. Ask user clarifying questions about runtime preferences using ask the user

QUESTIONS TO ASK USER:

1. **Execution Model**
   - Header: "Execution"
   - Question: "How should the system execute work?"
   - Options:
     - "Request-response" - "Traditional HTTP request/response"
     - "Event-driven" - "Async processing with queues"
     - "Stream processing" - "Continuous data flow"
     - "Batch processing" - "Scheduled periodic jobs"

2. **Concurrency Needs**
   - Header: "Concurrency"
   - Question: "What concurrency model do you need?"
   - Options:
     - "Single-threaded async" - "Node.js style, high I/O"
     - "Multi-threaded" - "Python/Java threads, CPU work"
     - "Actor model" - "Erlang/Akka style isolation"
     - "Function-based" - "Serverless auto-scaling"

3. **State Management**
   - Header: "State"
   - Question: "How should runtime state be managed?"
   - Options:
     - "Stateless" - "No server-side state, scale easily"
     - "In-memory cache" - "Redis/Memcached for sessions"
     - "Persistent state" - "Database-backed state"
     - "Distributed state" - "Multi-node coordination"

DOCUMENT STRUCTURE:

Create: /job-queue/product-{name}/runtime-execution.md

# Runtime Execution Architecture

## Executive Summary
[2-3 paragraphs: What runtime model was chosen and why]

## Core Execution Engine

### Selected Technology
- **Primary Runtime**: [chosen technology]
- **Rationale**: [why this choice]
- **Trade-offs**: [what we're giving up]

### Execution Paradigm
[Describe: request-response, event-driven, streaming, batch]

## Process Lifecycle

### Initialization
[How the system starts up, bootstraps, loads config]

### Request Handling
[How incoming work is received and routed]

### Execution Flow
[Step-by-step: what happens when work arrives]

### Teardown/Cleanup
[How resources are released, graceful shutdown]

## State Management

### State Architecture
[Where state lives: memory, cache, database]

### State Lifecycle
[How state is created, updated, invalidated]

### State Consistency
[How consistency is maintained across requests]

## Concurrency Model

### Concurrency Approach
[Threads, async, actors, serverless]

### Parallelism Strategy
[How work is distributed across cores/nodes]

### Synchronization
[Locks, queues, coordination mechanisms]

## Hot-Reload & Dynamic Updates

### Development Experience
[Hot reload during dev? How does it work?]

### Production Updates
[Zero-downtime deploys? Blue-green? Rolling?]

## Resource Constraints & Optimization

### Memory Management
[How memory is allocated, GC considerations]

### CPU Utilization
[How CPU is used, optimization strategies]

### I/O Patterns
[Network, disk, database access patterns]

### Scaling Strategy
[Vertical vs horizontal, auto-scaling triggers]

## Technology Comparison

### Evaluated Options
| Technology | Pros | Cons | Fit Score |
|------------|------|------|-----------|
| [Tech 1]   | ...  | ...  | 8/10      |
| [Tech 2]   | ...  | ...  | 6/10      |
| [Tech 3]   | ...  | ...  | 4/10      |

### Decision Rationale
[Why we chose what we chose]

## Implementation Considerations

### Development Setup
[How to run locally]

### Testing Strategy
[How to test runtime behavior]

### Monitoring Needs
[What metrics to track]

## Research Sources
[URLs consulted during research]

---

After completing research and asking user questions, write this document to:
/job-queue/product-{name}/runtime-execution.md

Also save your raw research notes to:
/job-queue/product-{name}/research-notes/runtime-research.md

Stage 3: Abstraction Layer Research (Agent 2)

Agent task:

You are researching abstraction layer architecture for: [Product Name]

CONTEXT:
[Include big-idea.md content]

YOUR GOAL:
Create a comprehensive `abstraction-layer.md` document that explains how user intent translates to executable logic.

RESEARCH APPROACH:
1. Research how modern frameworks handle abstraction
2. Use WebSearch for:
   - "[technology] abstraction patterns 2026"
   - "[technology] DSL design 2026"
   - "no-code/low-code architecture 2026" (if applicable)
   - "[technology] configuration vs code 2026"

3. Use WebFetch to read architectural docs

4. Ask user clarifying questions

QUESTIONS TO ASK USER:

1. **Input Format**
   - Header: "Input"
   - Question: "How will users define what they want the system to do?"
   - Options:
     - "Code (APIs, SDKs)" - "Developer-focused, programmatic"
     - "Configuration (YAML, JSON)" - "Declarative configs"
     - "Visual interface (drag-drop)" - "No-code UI builder"
     - "Natural language" - "AI-powered interpretation"

2. **Translation Approach**
   - Header: "Translation"
   - Question: "How should user intent become executable code?"
   - Options:
     - "Direct interpretation" - "Runtime interpretation, flexible"
     - "Compilation to native" - "Compiled ahead-of-time, fast"
     - "IR/AST transformation" - "Intermediate representation"
     - "Template-based generation" - "Code generation from templates"

3. **Extensibility**
   - Header: "Extensibility"
   - Question: "How should users extend default behavior?"
   - Options:
     - "Plugin system" - "Loadable extensions"
     - "Hooks/callbacks" - "Event-based extension points"
     - "Subclassing/inheritance" - "OOP-style extension"
     - "Middleware/interceptors" - "Pipeline-based modification"

DOCUMENT STRUCTURE:

Create: /job-queue/product-{name}/abstraction-layer.md

# Abstraction Layer Architecture

## Executive Summary
[How user intent becomes executable logic]

## Input Formats

### Primary Input Method
[Code, config, visual, NLP]

### Input Schema
[Structure of input: JSON schema, API surface, grammar]

### Validation & Parsing
[How inputs are validated and parsed]

## Intermediate Representation

### IR Design
[AST, JSON schema, custom IR - what does it look like?]

### Why This IR
[Rationale for chosen representation]

### Example Transformation
[Show: input → IR → output]

## Translation Mechanisms

### Chosen Approach
[Interpretation vs compilation vs generation]

### Translation Pipeline
[Step-by-step: input → IR → executable]

### Optimization Passes
[Any optimization during translation?]

## Mapping Tables

### UI/Config → System Primitives
[How high-level concepts map to low-level operations]

### Example Mappings
| User Concept | System Primitive | Implementation |
|--------------|------------------|----------------|
| [Concept 1]  | [Primitive 1]    | [How it works] |

## Extension Points

### Extension Architecture
[Plugin system, hooks, inheritance]

### Extension Registration
[How extensions are discovered and loaded]

### Extension API
[What APIs are exposed to extensions]

### Example Extension
[Code sample showing how to extend]

## Trade-offs Analysis

### Flexibility vs Performance
[What we optimized for]

### Simplicity vs Power
[Where we drew the line]

### Developer Experience
[How easy is it to use?]

## Technology Comparison

### Evaluated Approaches
| Approach       | Flexibility | Performance | DX  | Chosen |
|----------------|-------------|-------------|-----|--------|
| [Approach 1]   | High        | Medium      | Low | ❌     |
| [Approach 2]   | Medium      | High        | High| ✅     |

## Implementation Considerations

### Schema Evolution
[How to version the abstraction layer]

### Backward Compatibility
[How to handle breaking changes]

### Documentation Strategy
[How users learn the abstraction]

## Research Sources
[URLs consulted]

---

Write to: /job-queue/product-{name}/abstraction-layer.md
Research notes: /job-queue/product-{name}/research-notes/abstraction-research.md

Stage 4: Integration Layer Research (Agent 3)

Agent task:

You are researching integration layer architecture for: [Product Name]

CONTEXT:
[Include big-idea.md content]

YOUR GOAL:
Create a comprehensive `integration-layer.md` document that explains how the system connects to external resources.

RESEARCH APPROACH:
1. Research connector patterns and integration architectures
2. Use WebSearch for:
   - "[technology] connector architecture 2026"
   - "API integration best practices 2026"
   - "[technology] authentication patterns 2026"
   - "service mesh architecture 2026" (if microservices)

3. Use WebFetch for vendor-specific docs (if integrating with known services)

4. Ask user clarifying questions

QUESTIONS TO ASK USER:

1. **Integration Scope**
   - Header: "Integrations"
   - Question: "What external systems will this product integrate with?"
   - MultiSelect: true
   - Options:
     - "Databases" - "PostgreSQL, MySQL, MongoDB, etc."
     - "APIs (REST/GraphQL)" - "External HTTP APIs"
     - "Message queues" - "RabbitMQ, Kafka, SQS, etc."
     - "Storage services" - "S3, GCS, Azure Storage"

2. **Authentication Strategy**
   - Header: "Auth"
   - Question: "How should the system authenticate to external services?"
   - Options:
     - "API keys" - "Simple token-based auth"
     - "OAuth 2.0" - "Delegated authorization"
     - "Mutual TLS" - "Certificate-based auth"
     - "Service accounts" - "Cloud provider IAM"

3. **Discovery Mechanism**
   - Header: "Discovery"
   - Question: "How should the system discover external services?"
   - Options:
     - "Static configuration" - "Hardcoded endpoints"
     - "Environment variables" - "Deploy-time config"
     - "Service discovery" - "Consul, Eureka, k8s DNS"
     - "Dynamic registry" - "Runtime service registration"

DOCUMENT STRUCTURE:

Create: /job-queue/product-{name}/integration-layer.md

# Integration Layer Architecture

## Executive Summary
[How the system connects to external resources]

## Connector Architecture

### Connector Pattern
[Plugin-based, driver-based, adapter pattern]

### Supported Protocols
- REST API
- GraphQL
- gRPC
- Message queues
- Database protocols
- Custom protocols

### Connector Lifecycle
[How connectors are initialized, used, closed]

## Authentication & Credential Management

### Authentication Methods
[API keys, OAuth, mTLS, service accounts]

### Credential Storage
[Where credentials live: env vars, secrets manager, vault]

### Rotation Strategy
[How credentials are rotated without downtime]

### Security Boundaries
[How credentials are isolated per service]

## Service Discovery

### Discovery Mechanism
[Static, env-based, service discovery, dynamic]

### Configuration Format
```json
{
  "services": {
    "database": {
      "type": "postgresql",
      "discovery": "static",
      "endpoint": "..."
    }
  }
}

Health Checking

[How to detect unhealthy services]

Data Flow Patterns

Pull Pattern

[Request-response, polling]

Push Pattern

[Webhooks, callbacks]

Streaming Pattern

[WebSockets, SSE, gRPC streams]

Chosen Approach per Integration

Integration	Pattern	Rationale
[Service 1]	Pull	[Why]
[Service 2]	Stream	[Why]

Error Handling & Retry Logic

Error Classification

• Transient errors (retry)
• Permanent errors (fail fast)
• Rate limit errors (backoff)

Retry Strategy

• Exponential backoff
• Circuit breaker pattern
• Fallback mechanisms

Example: Retry Configuration

{
  "retry": {
    "maxAttempts": 3,
    "backoff": "exponential",
    "initialDelay": "100ms"
  }
}

Security & Isolation

Network Segmentation

[How integrations are network-isolated]

Data Encryption

[TLS for transit, encryption at rest]

Least Privilege

[IAM policies, scoped credentials]

Technology Comparison

Evaluated Integration Patterns

Pattern	Complexity	Reliability	Chosen
Direct API calls	Low	Medium	✅
Message queue	Medium	High	❌
Service mesh	High	High	❌

Implementation Considerations

Local Development

[How to run external dependencies locally]

Testing Strategy

[Mocking, test containers, integration tests]

Monitoring & Observability

[Metrics, logs, traces for integrations]

Research Sources

[URLs consulted]

---

Write to: /job-queue/product-{name}/integration-layer.md Research notes: /job-queue/product-{name}/research-notes/integration-research.md

### Stage 5: Output Rendering Research (Agent 4)

**Agent task:**

You are researching output rendering architecture for: [Product Name]

CONTEXT: [Include big-idea.md content]

YOUR GOAL: Create a comprehensive output-rendering.md document that explains how results are delivered to consumers.

RESEARCH APPROACH:

1. Research rendering strategies for the chosen tech stack

2. Use WebSearch for:

   • "[technology] rendering strategies 2026"
   • "SSR vs CSR vs SSG comparison 2026"
   • "[technology] streaming rendering 2026"
   • "[technology] caching strategies 2026"

3. Use WebFetch for framework-specific rendering docs

4. Ask user clarifying questions

QUESTIONS TO ASK USER:

1. Rendering Strategy

   • Header: "Rendering"
   • Question: "How should content be rendered?"
   • Options:
     • "Server-side (SSR)" - "Render on server, send HTML"
     • "Client-side (CSR)" - "Render in browser with JS"
     • "Static generation (SSG)" - "Pre-render at build time"
     • "Hybrid" - "Mix of SSR/CSR/SSG per route"

2. Real-time Requirements

   • Header: "Real-time"
   • Question: "Do outputs need real-time updates?"
   • Options:
     • "No real-time needed" - "Traditional request-response"
     • "Polling" - "Client polls for updates"
     • "WebSockets" - "Bidirectional real-time"
     • "Server-sent events (SSE)" - "Unidirectional streaming"

3. Output Formats

   • Header: "Formats"
   • Question: "What output formats do you need?"
   • MultiSelect: true
   • Options:
     • "HTML" - "Web pages"
     • "JSON/XML" - "API responses"
     • "Binary (images, PDFs)" - "Generated files"
     • "Streams" - "Continuous data streams"

DOCUMENT STRUCTURE:

Create: /job-queue/product-{name}/output-rendering.md

Output Rendering Architecture

Executive Summary

[How results are delivered to consumers]

Output Formats

Supported Formats

• HTML (web UI)
• JSON (API responses)
• Binary (files, images)
• Streams (continuous data)

Serialization Strategy

[How data is serialized for each format]

Content Negotiation

[How clients request specific formats]

Rendering Pipeline

Chosen Rendering Strategy

[SSR, CSR, SSG, hybrid]

Rendering Flow

[Step-by-step: data → rendered output]

Server-Side Rendering (if applicable)

• Template engine: [e.g., React Server Components, Jinja2]
• Hydration strategy
• Performance characteristics

Client-Side Rendering (if applicable)

• JavaScript framework: [React, Vue, Svelte]
• Bundle strategy
• Initial load performance

Static Generation (if applicable)

• Build-time generation
• Incremental static regeneration (ISR)
• When to use vs SSR

Streaming & Real-Time Updates

Real-Time Strategy

[None, polling, WebSockets, SSE]

Streaming Architecture

[How data streams to client]

Example: Real-Time Flow

1. Client subscribes to updates
2. Server pushes incremental changes
3. Client updates UI reactively

Template/Component System

Component Architecture

[How UI is composed: components, templates, views]

Component Library

[Design system, reusable components]

Composition Patterns

[How components are nested and composed]

Example Component Structure

<Layout>
  <Header />
  <Main>
    <DataGrid data={...} />
  </Main>
  <Footer />
</Layout>

Caching & Persistence

Caching Layers

1. CDN caching: [CloudFlare, CloudFront]
2. Server caching: [Redis, in-memory]
3. Client caching: [Service worker, browser cache]

Cache Invalidation

[How caches are invalidated when data changes]

Persistence Strategy

[How outputs are stored: database, file system, object storage]

Performance Optimization

Bundle Optimization

• Code splitting
• Tree shaking
• Lazy loading

Image Optimization

• Responsive images
• Format selection (WebP, AVIF)
• CDN delivery

Data Fetching

• Parallel fetching
• Request deduplication
• Prefetching strategies

Technology Comparison

Evaluated Rendering Approaches

Approach	SEO	Performance	Complexity	Chosen
SSR	✅	Medium	Medium	✅
CSR	❌	Slow	Low	❌
SSG	✅	Fast	Low	✅

Implementation Considerations

Development Experience

[Hot reload, dev server, debugging]

Testing Strategy

[Component tests, visual regression, E2E]

Monitoring

[Performance metrics, Core Web Vitals]

Research Sources

[URLs consulted]

---

Write to: /job-queue/product-{name}/output-rendering.md Research notes: /job-queue/product-{name}/research-notes/output-research.md

### Parallel Execution

**Launch all 4 agents at once:**

```bash
# Agent 1: Runtime Execution
delegation workflow:
  subagent_type: "general-purpose"
  description: "Runtime execution research"
  prompt: [Stage 2 agent task]

# Agent 2: Abstraction Layer
delegation workflow:
  subagent_type: "general-purpose"
  description: "Abstraction layer research"
  prompt: [Stage 3 agent task]

# Agent 3: Integration Layer
delegation workflow:
  subagent_type: "general-purpose"
  description: "Integration layer research"
  prompt: [Stage 4 agent task]

# Agent 4: Output Rendering
delegation workflow:
  subagent_type: "general-purpose"
  description: "Output rendering research"
  prompt: [Stage 5 agent task]

Wait for all agents to complete.

Output:

• Display: "✅ Stage 2/6: Runtime execution research complete"
• Display: "✅ Stage 3/6: Abstraction layer research complete"
• Display: "✅ Stage 4/6: Integration layer research complete"
• Display: "✅ Stage 5/6: Output rendering research complete"

---

Stage 6: Final Review & Presentation

Goal: Present all generated documents to the user for review and approval.

Step 6.1: Read All Generated Documents

Read:
- /job-queue/product-{name}/big-idea.md
- /job-queue/product-{name}/runtime-execution.md
- /job-queue/product-{name}/abstraction-layer.md
- /job-queue/product-{name}/integration-layer.md
- /job-queue/product-{name}/output-rendering.md

Step 6.2: Generate Executive Summary

Create a final summary document:

File: /job-queue/product-{name}/ARCHITECTURE-SUMMARY.md

# Architecture Summary: [Product Name]

## Research Completion

**Date:** [timestamp]
**Input:** [original input]
**Product Name:** [derived name]

## Recommended Architecture

### Technology Stack
- **Frontend**: [chosen technology]
- **Backend**: [chosen technology]
- **Database**: [chosen technology]
- **Deployment**: [chosen platform]

### Architectural Style
- [Monolithic/Microservices/Serverless]
- [Event-driven/Request-response]
- [SSR/CSR/SSG]

### Key Design Decisions

#### Runtime Execution
[1-2 sentence summary from runtime-execution.md]

#### Abstraction Layer
[1-2 sentence summary from abstraction-layer.md]

#### Integration Layer
[1-2 sentence summary from integration-layer.md]

#### Output Rendering
[1-2 sentence summary from output-rendering.md]

## Trade-offs & Risks

### Major Trade-offs
1. [Trade-off 1]: Chose X over Y because [reason]
2. [Trade-off 2]: Chose X over Y because [reason]

### Identified Risks
1. [Risk 1]: [Mitigation strategy]
2. [Risk 2]: [Mitigation strategy]

## Next Steps

To proceed with implementation:

1. Review detailed architecture documents:
   - `runtime-execution.md`
   - `abstraction-layer.md`
   - `integration-layer.md`
   - `output-rendering.md`

2. Set up development environment

3. Create initial project structure

4. Begin implementation with chosen tech stack

## Research Artifacts

All research is saved in:
- `/job-queue/product-{name}/`
- `/job-queue/product-{name}/research-notes/`

Total research sources consulted: [count]

Step 6.3: Present to User

Display:

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
🎉 PRODUCT RESEARCH COMPLETE
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Product: [Product Name]
Location: /job-queue/product-{name}/

Generated Documents:
  ✅ big-idea.md              - High-level vision and approach
  ✅ runtime-execution.md     - How the system executes work
  ✅ abstraction-layer.md     - How user intent becomes logic
  ✅ integration-layer.md     - How system connects externally
  ✅ output-rendering.md      - How results are delivered
  ✅ ARCHITECTURE-SUMMARY.md  - Executive summary

Recommended Stack:
  Frontend:   [technology]
  Backend:    [technology]
  Database:   [technology]
  Deployment: [platform]

Research Sources: [count] URLs consulted

Next Steps:
  1. Review detailed architecture docs in /job-queue/product-{name}/
  2. Approve or request revisions
  3. Begin implementation

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Ask for feedback:

Use ask the user:

• Question: "Would you like to revise any architectural decisions?"
• Options:
  • "Approve - looks good" - "Proceed with this architecture"
  • "Revise runtime execution" - "Re-research execution model"
  • "Revise abstraction layer" - "Re-research abstraction approach"
  • "Revise integration layer" - "Re-research integrations"
  • "Revise output rendering" - "Re-research rendering strategy"

If user approves:

• Display: "✅ Architecture approved! Ready for implementation."

If user requests revision:

• Re-run the specific stage's research agent with feedback
• Regenerate that document
• Re-present for approval

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Error Handling

At any stage failure:

1. Display which stage failed
2. Show what was completed
3. Provide recovery instructions

Example:

❌ Research Failed at Stage 3/6: Abstraction Layer

Completed:
  ✓ [Stage 0] Workspace created
  ✓ [Stage 1] Big idea generated
  ✓ [Stage 2] Runtime execution researched
  ✗ [Stage 3] Abstraction layer FAILED

Error: [error message]

Partial work saved at: /job-queue/product-{name}/

Recovery:
  - Fix the issue and resume with: $new-product [input]
  - Or manually continue research

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Tools Used

• WebSearch - Technology research and comparisons
• WebFetch - Reading official documentation
• Task Tool - Parallel research agents
• ask the user - Iterative clarification
• Read/Write - Document generation

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Expected Outcomes

After completion:

1. ✅ Comprehensive architecture research
2. ✅ 5 detailed architecture documents
3. ✅ Technology stack recommendations
4. ✅ Trade-offs and risks identified
5. ✅ User approval obtained
6. ✅ Ready for implementation

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Notes

• Deep research: Use WebSearch + WebFetch extensively
• Parallel execution: Launch all 4 document agents at once
• Iterative questions: Ask questions during each stage, not all upfront
• Cross-referencing: Each agent reads big-idea.md for context
• Documentation quality: Comprehensive, well-structured, actionable

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Total Expected Duration:

• Stage 0: ~10 seconds (folder creation)
• Stage 1: ~5-10 minutes (high-level research + big-idea.md)
• Stages 2-5: ~15-25 minutes (parallel deep research for 4 docs)
• Stage 6: ~2-3 minutes (summary generation + review)

Total: ~20-40 minutes (depending on research depth and user interaction speed)