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dsh-subagent-model-picker

Model Inference Updated 2026.09.08

Run the following command in DeepSeek Harness:

dsh plugin install chr003/dsh-subagent-model-picker

Paste the following prompt into your AI chat to install this plugin:

Run dsh plugin install chr003/dsh-subagent-model-picker in DeepSeek Harness to install this plugin; the source code is available at https://github.com/chr003/dsh-subagent-model-picker.

About this plugin

In DSH, the main conversation and its subagents share a single model selection, so there is no built-in way to route subtasks to a lighter model for summarisation or a higher-reasoning model for planning without changing what the parent is running. dsh-subagent-model-picker adds a dedicated Sub: picker beside the normal model selector, letting you assign a specific provider, model, and reasoning effort to newly created subagents while the main conversation keeps its own model. Settings are stored per conversation with Inherited as the default; an explicit choice overrides the inherited policy and is saved immediately. Existing subagents retain the model they were captured with across continuation and restart, child views are read-only, and save or validation failures surface an error instead of silently switching a managed child.

This is useful when your workflow benefits from heterogeneous model selection — for example, running a strong reasoning model on the parent for orchestration while delegating retrieval, formatting, or draft generation to a faster or specialised model on child agents. The plugin controls standard in-process DSH subagents only; it does not extend to external tools such as Codex or Claude CLI, and cross-model vision support has not yet been independently qualified.

Released under the MIT licence, the project is community-maintained on a best-effort basis. It has been tested on Linux with Node 22/24 and DSH 0.1.2-rc.1, passing all 61 automated tests including a real archive roundtrip. Feedback and contributions are welcome.

Screenshots

Use Cases

  • Parent conversation runs a strong reasoning model for orchestration while children reuse a lighter model for summarisation or formatting
  • Set Inherited or explicit per-conversation policies so descendants follow the owning root policy automatically
  • Captured subagents keep their bound model and effort across continuation and restart without drifting to defaults

Best For

  • Users running multi-agent DSH workflows who want task-specific model assignment
  • Developers orchestrating heterogeneous models to balance quality against token cost
  • Power users who need fine-grained control over subagent behaviour without side effects on the parent conversation