Imagine describing the software you want to build, getting a structured project plan, and putting the tasks into progress. AI starts working through them in order. Each task receives the results of the previous one. When execution finishes, a GitHub pull request is waiting for your review.
Vibe Coding Plan - Smart AI Model Routing for Developers | Save 60% on AI Costs
Generate execution plans that recommend the right AI model per task. Use expensive models only when it matters. Save 60% on AI costs.
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While that happens, you can work on another priority.
That is the experience I’m building with Vibe Coding Plan: automated planning and execution connected in one workflow, with shared context and smarter control over AI costs.
Anyone who has built a project with AI knows how much coordination can sit between coding requests. You decide what to build next, prepare the prompt, choose a model, explain the project, inspect the result, and carry the relevant information into the next task.
Multiply that by the number of tasks in a project, and the coordination becomes a job of its own. Here is how the features work together to take more of that work off your hands.
Everything starts with the project idea. You describe what you want to build, and automated scoping turns that description into a structured plan with tasks and generated prompts. The purpose is to establish the work ahead before execution begins, so individual coding tasks have a clear place in the project.
Consider a client portal with authentication, project management, and a dashboard. Those features depend on one another. The dashboard needs an API to call, and that API needs to understand which user can access which project. A useful plan gives those dependencies an order.
The tasks and their prompts live on a Kanban board. You can see the work in one place, inspect the instructions for each task, and decide what should move into progress. The plan stays connected to the work being executed.
This is where the central feature comes in: moving tasks into progress starts execution automatically. The tasks run one by one. You do not need to return after every completed task to manually start the next one and explain what just happened.
In the client portal example, authentication runs first. Once it finishes, its results become available to the next task. The projects API can work from those results, and the dashboard task can then use the API work that came before it.
That sequence matters. A later task should build on the decisions and output already produced. Passing those results forward gives the next execution a useful starting point and reduces the handoff work you would otherwise handle yourself.
Shared memory supports the same goal across both tasks and models. Your project’s context should remain available when one model finishes its part and another takes over. Architecture decisions, conventions, and earlier results need to travel with the project.
This also makes using multiple models more practical. Each task can be routed according to what it needs, while retaining the context required to contribute to the same codebase.
A difficult architecture problem and a straightforward CRUD task have different demands. Smart model routing helps match those demands to an appropriate model, including a cheaper option where the task allows it. You can reserve more expensive execution for work that needs it.
I’m interested in that distinction because AI spending is affected by the decisions made across an entire project. Repeatedly choosing an expensive model for routine work adds up. Routing brings that decision into the task workflow instead of leaving you to make it manually every time.
Token-saving skills address another part of the cost. These provide reusable, focused instructions for development tasks. Combined with the relevant project context, they help keep execution directed at the work that needs to be done.
The goal is to reduce unnecessary instructions and context while preserving what the model needs to complete the task. Model routing and optimized skills work together: one addresses which model executes the work, and the other helps make its instructions more efficient.
Once execution finishes, the workflow creates a GitHub pull request automatically. The changes arrive in a form you can inspect, discuss, and merge. You review the code and decide when it is ready.
That is an important part of the experience. Automation should get the work to a reviewable result. The pull request gives you a concrete place to assess what was built and make the final decision.
There is another addition coming soon: a free open-source model for easy tasks in your project plan.
Routine work could then use that free option, while more demanding tasks continue to use an appropriate model. For developers watching their AI spending, that would provide another way to keep costs focused on the harder parts of a project.
Taken together, these features give you a connected path from the initial idea to a pull request: scope the project, organize the tasks and prompts, choose suitable models, preserve context, and execute the work in order.
The benefit I care about most is being able to put a planned sequence of work into progress and spend my attention elsewhere while it runs.
Vibe Coding Plan - Smart AI Model Routing for Developers | Save 60% on AI Costs
Generate execution plans that recommend the right AI model per task. Use expensive models only when it matters. Save 60% on AI costs.
vibecoderplanner.com
If you could describe your next project, queue the tasks, and come back to a GitHub PR ready to review, what would you build first?
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