dsh-peak-badge
Run the following command in DeepSeek Harness:
dsh plugin install mj5809/dsh-peak-badge
Paste the following prompt into your AI chat to install this plugin:
Run dsh plugin install mj5809/dsh-peak-badge in your terminal; the source is available at https://github.com/mj5809/dsh-peak-badge . After installation, restart dsh web and hard-refresh the browser.
About this plugin
Since August 2026, DeepSeek has applied peak/off-peak pricing: token rates double during the 09:00-12:00 and 14:00-18:00 Beijing-time windows and are halved outside them. For developers who drive heavy prompt volume through the DSH web GUI every day, knowing which window you are in at a glance directly affects per-conversation cost.
dsh-peak-badge drops a draggable floating pill into the DSH browser. It refreshes every 30 seconds via the Asia/Shanghai timezone, showing a green label for off-peak (half price) or an orange one for peak (2x price). Press and drag to reposition; the location persists in localStorage across page reloads. A single click opens a detail card with the current Beijing time, the two peak windows, and reference input/output prices for DeepSeek V4-Flash and V4-Pro. The plugin is purely client-side with a no-op host half, so it introduces no host-level risk and requires zero configuration.
It is built for developers and power users who chat with DeepSeek through the DSH browser and want to confirm the current rate at a glance before committing to a long generation task, choosing instead to defer expensive work to the cheaper off-peak hours.
Use Cases
- Check peak vs off-peak status at a glance before starting a long conversation in the DSH browser
- Schedule expensive generation tasks to the cheaper off-peak window to reduce token spend
- Complement the AGENTS.md text-rule reminder in basecamp-style repositories
Best For
- Developers who drive heavy DeepSeek prompt volume through the DSH web GUI daily
- Individual users who want to time expensive generation tasks to the off-peak window
- Team engineers managing multi-model workloads in basecamp-style repositories
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