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dsh-language-control

Workflow Updated 2026.08.28

Run the following command in DeepSeek Harness:

dsh plugin install wodongx123/dsh-language-control

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

Run dsh plugin install wodongx123/dsh-language-control in the DeepSeek Harness terminal, or visit https://github.com/wodongx123/dsh-language-control for the full source code; the plugin activates automatically after installation.

About this plugin

When an agent calls a tool that returns English documentation or code, its chain-of-thought quietly drifts into English—planning, reasoning, and even the final answer to the user all follow, silently breaking the reading experience of Chinese-speaking users. dsh-language-control is a lightweight plugin built to eliminate this language drift.

The approach is simple yet precise: before every reasoning step, the plugin injects a language constraint at the very top of the system prompt with the highest priority (order: -200), mandating that chain-of-thought reasoning, planning, tool-call deliberation, and internal thinking all stay in Chinese. Crucially, this is not a one-time injection at session start—the constraint is re-evaluated on every assemble() call, so even after a tool returns a wall of English text, the directive remains undiluted.

A visual settings panel lets you edit custom constraint text, toggle between custom and default modes, or restore defaults with one click. Zero dependencies, zero configuration, plug and play—ideal for anyone who wants their agent to think and respond in Chinese throughout the entire workflow.

Use Cases

  • Agent drifts into English after calling a tool that returns foreign content; forces Chinese reasoning
  • Multi-step planning workflow where every internal reasoning step must stay in Chinese
  • Team works primarily in Chinese and needs consistent language in both thinking and output

Best For

  • Chinese-first agent users and content creators
  • Developers building multi-step reasoning workflows
  • Team leads who need uniform language across agent output