dsh-subagent-model-picker
Run the following command in DeepSeek Harness:
dsh plugin install ringoage/dsh-subagent-model-picker
Paste the following prompt into your AI chat to install this plugin:
Install the dsh-subagent-model-picker plugin from https://github.com/ringoage/dsh-subagent-model-picker to add a manual subagent model and reasoning-effort picker beside the main model seat in DeepSeek Harness.
About this plugin
Routing subagents to a cheaper model has been solved many ways in recent days, yet every existing approach shares one trait: it decides for you. Tool-based routing lets the model pick, auto-routing leans on policy classification, and command-style input offers no visible state at all. dsh-subagent-model-picker takes a different path. It places a dropdown right beside the main model seat so you choose visually, and every selection applies deterministically to all subagent paths in that session—no guessing involved.
Under the hood it covers subagent, subagent_fork, and workflow fan-out children at any depth. Models that expose reasoning-effort levels get a matching Thinking effort menu alongside the model picker. The choice is per-session state that survives a restart, and the default behavior is simply to inherit the main model, with no hidden routing. It composes cleanly with auto-routing plugins: keep a policy router as the default, then override a specific session manually—or do the reverse.
This is for developers who want precise control over subagent inference cost without delegating the routing decision to a model or a policy rule. If you want to know, at a glance, exactly which model every subagent is running on before you hit send, and keep that state visible after the fact, this is the tool you have been looking for.
Use Cases
- Switch subagents to a lower-cost model and tweak reasoning effort mid-session
- Override a specific session's subagent routing on top of an auto-router plugin
- Pin a uniform model across deep workflow fan-out children instead of guessing per level
Best For
- DSH users who want full control over subagent cost without delegating routing to a model or policy
- Developers who need a visible, at-a-glance confirmation of which model subagents are actually running on
- Teams using subagent_fork or workflow fan-out that track token spend closely
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