DeepSeek Harness plugin

dsh-task-estimator

DSH plugin: session-header task estimator — estimated remaining time, token usage and per-model cost (≈CNY) with an optional LLM-powered estimate. / DSH 插件:会话标题栏任务估算窗口——预计剩余时间、token 用量与按模型花费估算(≈CNY)

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Source facts

Repository
0x250-t/dsh-task-estimator
Latest update
Aug 16, 2026
Category
Memory
GitHub stars
1
Format
plugin
Catalog evidence
Upstream dsh.bundle evidence
Evidence path
package.json#dsh.bundle
Checked against
0.1.0-rc.8
Upstream check date
2026-08-20

This evidence comes from the upstream catalog. This site has not installed, run, or security-reviewed the plugin.

Install

Start with a prompt that asks an agent to review the GitHub repository and source. Switch to the command if you want to install it yourself.

Copy this prompt into DSH, Codex, or another agent and ask it to review the GitHub repository and source first.

Do not install or run any commands yet. Read this plugin's GitHub repository, README, and relevant source code. Then answer the questions below clearly and directly so I can decide whether it fits my needs:

1. What is this plugin, and what problem does it solve?
2. Who is it for, and what are its typical use cases?
3. How is it used after installation? Include one minimal example.
4. What known limitations or privacy, security, compatibility, or maintenance risks does it have?
5. Give a clear recommendation: recommend, conditionally recommend, or do not recommend, with reasons.

Distinguish statements documented by the repository, inferences from source code, and unknowns. If evidence is insufficient, say so explicitly. Do not guess or simply repeat the README.

GitHub: https://github.com/0x250-t/dsh-task-estimator
Plugin: dsh-task-estimator
Author: 0x250-t

Check the source files

Read the README and other files from this plugin directory before installing.

File explorer3 files
README.mdSource · read only

dsh-task-estimator

> v1.1.1 — 修复 client 加载:factory 内定义 module 全局(浏览器 ESM 运行时无 module,否则 client 半区启动报错)

<p align="center"> <sub>🤖 由 <a href="https://github.com/deepseek-ai/DeepSeek-Harness">DeepSeek Harness</a> 辅助开发 · Built with <a href="https://github.com/deepseek-ai/DeepSeek-Harness">DeepSeek Harness</a></sub> </p>

DSH 插件:会话标题栏任务估算窗口——预计剩余时间、token 用量与按模型花费估算(≈CNY),支持 LLM 智能估算。

Session-header task estimator for the DeepSeek Harness web GUI: estimated remaining time, token usage and per-model cost (≈CNY), with an optional LLM-powered estimate.

功能 Features

  • 启发式估算 Heuristic estimate:基于会话投影(sessionStats/tokenUsage/goal)实时计算——已用轮次/token/耗时、平均每轮、预计剩余(近 5 轮趋势外推 + 置信区间)、预计完成时刻
  • 花费估算 Cost estimate:按会话日志重放 token 用量,按「模型 × 时段」(legacy/peak/offPeak,北京时间)增量计价,返回汇总 + 按模型明细(模型 / token / 花费),价格随生效日期自动切换
  • AI 估算 LLM estimate:用会话当前模型做轻量补全,输出 PERT 三点估算(乐观/最可能/悲观剩余分钟 + 期望值)、剩余轮数/token、任务复杂度分档(XS~XL)、METR 可靠性警告(估算超过模型 p80 阈值时建议拆分任务)
  • 本轮预计 Per-round estimate:agent 思考中(「Deep diving...」状态行)时,在该行右侧注入「本轮预计 ≈ X」——以近 5 轮 LLM 耗时趋势(趋势不可用时取全程平均)预估本轮对话所需时长,随每轮节奏变化自动刷新
  • 交互 Interaction:悬停显示、移开 0.3s 后 0.5s 线性淡出、点击固定(再次点击任意位置关闭)、Alt+E 切换、触屏自动降级、面板内点击不退出、内容超高可滚动
  • 设置 Settings:价格表可编辑(legacy 档,峰谷倍率自动派生)、刷新间隔自定义(分钟,支持小数)、恢复默认、固定状态持久化(localStorage)、价格生效日程(本地/北京日期、当前价格时段、新价格生效日)

安装 Install

bundle 插件,安装后重启 web

dsh plugin --profile web add github:0x250-t/dsh-task-estimator#v1.1.0

或本地目录(先 npm run build):

cd dsh-task-estimator
dsh plugin --profile web add .

使用 Usage

1. 悬停会话标题栏右侧的估算按钮(⏱ 剩余 X · Y tok · ≈¥Z)查看明细 2. 点击按钮固定面板;再次点击任意位置关闭 3. AI 估算:展开「AI 估算」区块 → 点击「开始估算」(也可设置自动刷新间隔) 4. 设置:展开「设置」区块 → 编辑价格/间隔 → 保存;「恢复默认」一键还原 5. agent 思考中(「Deep diving...」状态行):右侧会显示「本轮预计 ≈ X」,即对当前这轮对话耗时的实时预估

算法说明 Algorithm

  • PERTE = (O + 4M + P) / 6,区间 [O, P],顺序异常时排序降级
  • METR:估算期望超过模型 p80 可靠性阈值(flash 约 60 分钟 / pro 约 90 分钟)时提示拆分任务
  • 任务分档:按轮次 / 工具调用数 / 用户消息量归为 XS~XL
  • 参考:agent-estimate(github.com/kiloloop/agent-estimate)、codex-eta(pypi.org/project/codex-eta)

隐私 Privacy

  • 全部状态为进程内存,无任何持久化
  • 无 API key / 密钥 / 密码硬编码;无网络外发(除 AI 估算时对已配置模型的一次调用)
  • 价格表为内置参考值,可在设置中修改

构建 Build

npm install          # 安装 esbuild(devDependency)
npm run build        # 构建 lib/client.js + 语法检查

License

MIT