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dsh-train-refund

Web Tools Updated 2026.08.20

Run the following command in DeepSeek Harness:

dsh plugin install uckkk/dsh-train-refund

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

Run dsh plugin install uckkk/dsh-train-refund in DeepSeek Harness to install this plugin; the source repository is https://github.com/uckkk/dsh-train-refund .

About this plugin

After buying a train ticket, the hardest part of a refund or reschedule is rarely the decision itself but figuring out exactly how much you will be charged. China rail refund fees are tiered by how far in advance you act, with extra conditions for already-rescheduled tickets and holiday periods, making mental math error-prone. dsh-train-refund turns this into a precise parameter call: supply the train and time window, and the tool returns a line-item breakdown of every fee component before you commit.

The plugin is a pure Node.js library with zero network calls. All calculation logic runs locally, so there is no timeout risk and no dependency on external APIs that may change. When you invoke the train_refund tool, the result comes back as structured fields, making it easy to compare total refund costs across candidate trains in a single conversation or to drop the numbers straight into an email or note.

If you travel on business frequently and need to weigh refund costs across several options, or simply want to confirm a fee before purchasing, adding this plugin to your DeepSeek Harness session removes one guess and adds one certainty.

Use Cases

  • Confirm the exact refund fee before submitting a cancellation request
  • Compare reschedule costs across different trains and time windows before committing
  • Verify refund and change costs for expense reports and business travel reconciliation

Best For

  • Frequent business travelers who need to weigh refund costs across multiple train options
  • Casual passengers who want a precise fee number before purchase or cancellation
  • Developers building travel-assistant or expense-workflow experiences on DeepSeek Harness