dsh-whale-meter
Usage tiers and shareable stats cards: 🐟→🐳 ranks by monthly token burn with a locally-computed estimated percentile, precise pricing for 46 models across 6 vendors (including size-tiered Chinese pricing), backfill of pre-install sessions, and old-vs-new comparison across the 2026-08-17 rate change. Local-only, no telemetry.
用量段位与战绩卡片：按本月 token 消耗评 🐟→🐳 五档段位，分位本地估算（零上报）；6 家厂商 46 个模型精准计价，支持国内厂商按输入长度分档；自动回填安装前的历史会话；8·17 调价前后同一份用量的新旧价对比。数据全在本机，无遥测。
How to install
dsh plugin add dsh-whale-meter About
🐳 鲸鱼电表 whale-meter **给你的鲸鱼装个电表 —— DeepSeek Harness 用量与费用面板** 一行安装 · 零配置 · 消耗量段位 · 人民币账单 · 涨价前后对比 · 可晒的战绩卡片 English · 安装 · 配置 · FAQ --- **你这个月在 DeepSeek Harness 上烧了多少 token？到什么段位了？** > 鲸鱼电表把它算清楚：本月消耗量、🐟→🐳 五档段位、估算超过多少用户，外加一句有画面感的换算——「≈ 592 遍《三体》全集」。一键生成战绩卡片，直接发出去。 > 顺带也把钱算明白：**8-17 DeepSeek 涨价了**（v4-flash 未命中输入 ¥1 → ¥3，输出 ¥2 → ¥9），同一份用量按新旧两套价目分别算给你看，缓存帮你挡下多少、哪个模型在烧钱，一目了然。 30 秒装上 bash dsh plugin --profile <你的profile> add dsh-whale-meter 重启 dsh web，会话页顶部出现 🐳 花费徽章，点开就是完整账单。 从源码装：dsh plugin --profile <你的profile> add github:Shiye-10Pages/dsh-whale-meter（首次需按提示在 profile 的 pnpm-workspace.yaml 里授权构建…
Recommendation signals
Meta
- License
- MIT
- Language
- TypeScript
- GitHub stars
- 11
- mo. downloads
- 609
- Last push
- 2026-08-16
- Created
- 2026-08-16
Basic safety check
- Findings
- None
- Sources
- curated:awesome-dsh-plugin.com, curated:awesome-dsh-plugin/awesome-dsh-plugin, curated:0xsline/awesome-deepseek-harness
- Topics
- agent, ai-cost, billing, cordis, cost-tracking, deepseek, deepseek-api, deepseek-harness, dsh, dsh-plugin, llm-cost, observability, token-counter, token-usage, typescript, usage-dashboard
Related plugins
TokenLedger
zh667/TokenLedger
Sidebar usage panel that attributes tokens to the relay site that served each request, read from your existing provider config: today/month/all-time totals, per-site and per-model breakdowns, a year activity heatmap, and New API / Sub2API / DeepSeek balances.
dsh-context
bowenliang123/dsh-context
DSH context insight panel: Context dashboard + /context command + Context browser — one-stop context lifecycle management with categorized composition, content details, evolution trends, compaction/injection events, and stats.
dsh-usage-statistics-panel
HaoyueQin/dsh-usage-statistics-panel
Usage statistics settings panel: 26-week activity heatmap, daily token trend with a cache hit-rate curve, per-model donut and breakdown, time-range filters, summary cards, one-time historical backfill and lossless rebuild.
dsh-all-usage
ParticleLight/dsh-all-usage
Analyze usage across models, providers, workspaces, and time ranges — including custom start/end dates over the full retained history — with live cache-hit visibility and separate model and provider breakdowns. Includes hourly single-day trends, daily cross-day trends, a 53-week GitHub-style heatmap, usage streaks, model and workspace Token-share donut charts with Token, cost, and percentage details, independent combinable filters, cache-efficiency trends, DeepSeek account balance, workspace aliases, CSV export, and a durable ledger that preserves successfully flushed usage after session deletion — and after a workspace is deleted, where its recorded usage is summed into one Deleted row — and rebuilds incrementally — on restart, unchanged sessions are applied from the ledger without re-reading their logs (a per-session log revision is the change signal), and only changed/new events are folded. The workspace registry is kept fresh automatically through the DSH domain/changed probe: only added/removed workspaces are reprocessed incrementally, unchanged workspaces reuse their computed aggregates and ledger with zero rescan, and usage is strictly limited to DSH-registered workspaces (unregistered cwds are ignored). After a scan, lightweight revision status fetches full history only when the Host/data state changes and exposes non-sensitive sync health (revision reuse, rereads, ledger recovery, and failures). Token accounting is explicit: input is fresh (cache read/write and reasoning are separate buckets) and zero-usage replays never overwrite recorded usage. Chinese uses local time, English uses UTC; API access is loopback-only.