Model-aware task delegation for subagents

Right now, when I create implementation plans, I often annotate each task with the recommended model and reasoning level, for example:

- [ ] T005 Add the SwiftUI app entry point and SwiftData model container wiring in DriftApp/App/DriftApp.swift
  [model: gpt-5.4-mini | reasoning: medium]

This works well for planning, but when the orchestrator spawns subagents, they currently inherit the orchestrator’s own model instead of using the task-level configuration.

That creates friction because I have to manually switch models for nearly every task.

It would be extremely useful if the orchestrator could:

• Parse task metadata automatically

• Spawn subagents with the requested model

• Apply the specified reasoning level (low, medium, high, etc.)

• Optionally fall back to the orchestrator defaults if no task-level config is provided

Example:

- [ ] T001 Create the SwiftUI iOS app project
  [model: gpt-5.4-mini | reasoning: medium]

- [ ] T020 Design subscription state machine
  [model: gpt-5.5 | reasoning: high]

This would enable much better cost/performance optimization:

• cheap models for scaffolding and boilerplate

• stronger models for architecture and critical logic

• more predictable execution across large implementation plans

For agentic workflows and spec-driven development, this would be a huge productivity improvement.