Discovery Workflow

Goal: Idea maturation, not speed.

Proceed in phases. Even if the user provides details, complete each phase through conversation. Do not infer or assume -- discuss and validate with user. Proceed one phase at a time.

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Phase 1: Value Proposition

1. Problem + User: What problem does this solve? For whom?
2. Pain: How is it solved today? What's painful about that?
3. Core actions: 1-3 focused actions (not a full app port)

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Phase 2: Why LLM?

1. Conversational win: Where does "just say it" beat clicking through menus?
2. LLM adds: What does the LLM contribute? (intent parsing, generation, reasoning, summarization)
3. What LLM lacks: Your data? Your APIs? Ability to take real actions?

Fail patterns (stop if any match):

• Long-form or static content better suited for a website
• Complex multi-step workflows that exceed widget display modes
• Dashboards (use tables, lists, or short summaries instead)
• Full app ports instead of focused atomic actions
• No clear answer to "why inside an AI assistant vs standalone?"

If a fail pattern matches: explain the gap, suggest a different interface or narrower scope.

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Phase 3: UI Overview

Describe the user journey through core actions:

1. First view: What does the user see when the widget loads?
2. Key interactions: What happens at each core action?
3. End state: How does the experience conclude?

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Phase 4: Product Context

Gather:

• Existing products and APIs
• Authentication method (OAuth, API key, none)
• Rate limits and constraints
• Data sources

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Phase 5: Spec Summary

After phases 1-4 are discussed, assemble a brief spec:

# App Name

## Value Proposition

[Problem, user, pain point, core actions]

## Why LLM?

[Conversational win, what LLM adds, what it lacks]

## UI Overview

[First view, key interactions, end state]

## Product Context

[APIs, auth, constraints]

## Tools and Widgets

[List of tools and widgets with input/output shapes]

After the spec is ready, proceed to architecture.md for API design, then setup.md for project scaffolding.