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dsh-dashboard

Client Updated 2026.09.03

Run the following command in DeepSeek Harness:

dsh plugin install VinciBeans/dsh-dashboard

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

Run dsh plugin install VinciBeans/dsh-dashboard in your terminal to install; full source at https://github.com/VinciBeans/dsh-dashboard

About this plugin

When running multi-turn inference in DeepSeek Harness, token burn, latency swings, and tool-call patterns are buried in logs and hard to glance at from the conversation UI. DSH Dashboard adds a Statistics tab right beside Chat and Trajectory, surfacing all of that as a live panel without switching terminals or grepping output.

The panel covers four layers: a token layer with input including cache reads, output tokens, and cache-hit rate; a timing layer with LLM time, tool time, first-token latency, and decode rate; a tool-activity layer that breaks down per-tool calls, failures, and average duration rendered as proportional four-color segments; and an insights layer summarizing tool success rate, think-versus-tool time split, tokens per turn, and the input-to-output ratio. Every number updates live as the session progresses, and the locale follows the host automatically in both English and Chinese. All figures are derived client-side from the conversation snapshot tool-result nodes and the built-in sessionStats and tokenUsage projections, so there is no extra remote call.

If you are developing or running inference on the DeepSeek Harness v0.1.2 line and want an at-a-glance view of per-session resource cost and performance without leaving the chat window, this plugin fills the dashboard gap.

Use Cases

  • Inspect per-turn token cost and first-token latency while debugging multi-turn inference
  • Analyze tool-call success rate, failure count, and average duration distribution
  • Compare input-to-output token ratios and cache-hit rates when tuning prompts

Best For

  • Developers running daily inference on DeepSeek Harness v0.1.2
  • SREs who need real-time visibility into LLM resource consumption
  • Researchers optimizing prompt engineering and tool-call strategies