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dsh-tire-wear

Model Inference Updated 2026.08.20

Run the following command in DeepSeek Harness:

dsh plugin install uckkk/dsh-tire-wear

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

Run dsh plugin install uckkk/dsh-tire-wear in DeepSeek Harness to install this plugin, source: https://github.com/uckkk/dsh-tire-wear

About this plugin

Tire wear assessment has long relied on driver experience or rough visual inspection, lacking a consistent quantitative framework. dsh-tire-wear brings this process into DeepSeek's conversational reasoning pipeline: registered as a tool within the assistant, it accepts a set of wear-related parameters, applies built-in rules for precise calculation, and returns a clear evaluation conclusion along with actionable recommendations—giving every judgment a solid basis.

The plugin is implemented entirely in Node.js with no external network calls. All computation runs locally, so responses are fast and no additional API keys are required. This lightweight design means it slots easily into existing workflows without introducing extra deployment overhead.

If your team needs an embeddable tire-condition assessment capability for fleet management, after-sales support, or automated inspection scripts, dsh-tire-wear provides a ready-to-use reasoning tool that helps you deliver professional-grade judgments directly within a conversational context.

Use Cases

  • Quickly determine whether a tire needs replacement in fleet management conversations
  • Provide expert tire wear recommendations in customer support dialogues based on parameters
  • Embed quantitative tire-condition assessments into automated inspection workflows

Best For

  • Fleet or logistics managers who need a unified tire wear standard
  • Automotive after-sales and customer support teams
  • Developers building tire assessment into conversational systems