Team Brontosaurus: Wireflows + Sketchy Screens

Wireflows

Reasoning & Justification

Flow 1: Onboarding

This wireflow captures the full onboarding experience, which is the first interaction users have with AI Resource Goose after installing the extension. The flow begins with installation and user identification, followed by a deliberate selection step where users choose which AI tools they want the system to track, such as ChatGPT, Claude, Cursor, or browser-based copilots. This decision point is critical because it establishes agency from the start. Rather than automatically monitoring all activity, the product invites users to consciously define the scope of tracking, reinforcing that this is a self-regulation tool, not a surveillance system.

After selecting tools, users are prompted to grant the necessary system permissions. This checkpoint ensures transparency and builds trust by clearly communicating what the app needs access to and why. Once permissions are granted, users set a daily usage goal, either in hours or prompt count. Allowing both formats accommodates different behavioral patterns: some users think in time blocks, while others interact with AI in short, frequent bursts. The onboarding flow then includes an optional prompt-content tracking opt-in, further emphasizing user control. After confirmation, the Goose appears on the desktop, making the system feel present but lightweight. The onboarding sequence is intentionally short and reflective, setting the tone that this product supports intentional AI use rather than restricting it.

This design is grounded in our personas. For embedded AI operators who rely heavily on copilots throughout their workflow, tool selection and goal setting introduce awareness without imposing friction too early. For more cautious or reflective users, the opt-in controls and customization reinforce autonomy and reduce fear of over-monitoring. In both cases, onboarding frames the Goose as a companion for self-awareness rather than an authority enforcing limits.

Flow 2: Daily Usage Tracking

This wireflow represents the core daily interaction loop. The flow begins when a user opens an AI tool, triggering a session timer and initiating usage tracking in the background. As the user continues working, prompt counts increment automatically. After a defined threshold, such as five prompts, a reflection popup appears. This moment is intentional: it does not block usage but introduces a micro-interruption designed to shift the user from automatic iteration to conscious engagement.

From there, users select a reflection option before continuing. The resource meter updates visually, providing feedback on overall usage for the day. This visual system externalizes AI engagement into something emotionally legible, helping users see patterns rather than relying on vague intuition. The reflection step is subtle but important. It nudges users to consider whether they understood the output, whether they are iterating thoughtfully, and whether AI is augmenting or replacing their thinking.

This structure directly responds to insights from our personas. Embedded operators tend to default to AI at friction points and can quickly accumulate prompts without noticing. The reflection popup interrupts autopilot mode without halting progress. Meanwhile, more cautious builders often struggle with balancing learning and speed. For them, structured reflection validates intentional use and supports skill retention. In both cases, the daily tracking flow supports metacognition rather than restriction

Flow 3: Usage Limit Reached

This wireflow activates when a user approaches or exceeds their self-defined daily usage goal. If the user is nearing the threshold, the Goose changes state, for example turning yellow, signaling that they are approaching their limit. If the daily goal is exceeded, a soft-friction popup appears. Instead of locking the user out, the system presents two clear options: take a break or override the limit.

If the user chooses to take a break, the session ends. If they override the limit, the override is logged and the session continues. This logging mechanism introduces accountability without removing autonomy. The flow deliberately avoids hard restrictions, recognizing that AI is often embedded in real work contexts where blocking access would be impractical.

This decision is persona-driven. For embedded AI operators, a hard lock would likely result in immediate abandonment of the product. Soft friction maintains usability while introducing a moment of self-assessment. For reflective users, the break option provides a structured off-ramp that supports healthier habits without inducing guilt. The Goose functions less as a gatekeeper and more as a behavioral mirror, making invisible patterns visible and encouraging intentional engagement.