dsh-usage-meter
Run the following command in DeepSeek Harness:
dsh plugin install V-dev-388/dsh-usage-meter
Paste the following prompt into your AI chat to install this plugin:
Run dsh plugin install V-dev-388/dsh-usage-meter in your terminal to install; the source repository is https://github.com/V-dev-388/dsh-usage-meter .
About this plugin
DSH users who have been running conversations for a while often lack a clear picture of total token consumption: which providers and models use the most, how well caching is working, and what the recent trend looks like. dsh-usage-meter adds a Usage section to the Settings page, aggregating token data from every session including sub-agent sessions into six summary metrics — requests, total tokens, input, output, cache read, and cache hit rate — plus per-provider and per-model breakdown tables.
Time-range views cover today as 24 hourly bars and the last 7 or 30 days as daily bars, all in local timezone, with 60-second auto-polling and a manual refresh button. Data is persisted locally under $DSH_HOME via atomic writes; on startup the plugin backfills historical transcripts through the official sessionQuery, live sessions are updated incrementally via events, and watermark-based deduplication ensures replay never double-counts. The plugin carries zero third-party dependencies, calling only official Cordis services and the built-in paths package, and is entirely passive with no network calls or telemetry.
Ideal for DSH users who want lightweight token accounting without pulling in external services or cost-estimation layers. The plugin reports raw token metrics and cache hit rate only — no fee conversion, no per-request log table — keeping the data semantics clean and predictable.
Use Cases
- Glance at token consumption breakdown by provider and model in the Settings page
- Compare usage trends across today, 7 days, and 30 days to spot peak hours
- Track cache hit rate changes to evaluate prompt or model adjustments
Best For
- DSH daily users who need a lightweight token usage overview
- Developers monitoring multi-provider consumption without external SaaS
- AI engineers tracking cache hit rates to optimize prompting strategies
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