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dsh-effort-router

Model Inference Updated 2026.09.11

Run the following command in DeepSeek Harness:

dsh plugin install wanghaixu-hai/dsh-effort-router

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

Run dsh plugin install wanghaixu-hai/dsh-effort-router in DeepSeek Harness to install; source code is at https://github.com/wanghaixu-hai/dsh-effort-router . Restart the host process after installation to activate.

About this plugin

In daily DeepSeek Harness use, the most annoying cost problem is rarely that the model is too weak. It is that every single turn burns the most expensive brain on the lightest question, or that a hard problem is shoved into a cheap model and the quality visibly drops. dsh-effort-router automates the decision of how much reasoning a turn actually needs: it classifies each request, overrides the provider, model, and reasoning effort for that one call only, then hands everything back. No session-level or global settings are ever touched.

At its core is a zero-token rule classifier that sorts input into four tiers: simple, standard, hard, and vision. Only the ambiguous gray zone triggers a single 8-token call to a cheap model for arbitration, so there is no keyword whitelist to maintain. The vision tier is independent, with reasoning effort scaled from low to max by difficulty, so a holiday photo and a stack-trace screenshot no longer share the same level. Before every send, the client syncs the turn plan onto the composer seat, so the model box always reflects what this turn will really invoke. A seat you picked by hand is never overridden; the plugin logs skip:manual and steps aside.

Ideally suited for DeepSeek Harness users who bounce between models, want to save tokens without sacrificing quality on hard problems, and dislike plugins that silently rewrite global settings. One toggle right in the composer takes effect on the very next turn, or switches off just as easily. Zero overhead.

Use Cases

  • Mixing chit-chat and architecture questions in one session without manually swapping models every turn
  • Pasting a stack-trace screenshot and wanting the vision model with difficulty-scaled effort automatically
  • Saving tokens per turn without leaving any residue in session or global settings

Best For

  • DeepSeek Harness power users juggling multiple models and watching token spend
  • Developers who need full strength on hard problems but refuse to burn budget on trivia
  • Workflow-oriented users who prefer per-request overrides over any global-state mutation