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dsh-test-workbench

Verified · install-tested on dsh dmsobtl

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2026-08-15Last push
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What it does

Test workbench Profile based on DeepSeek Harness — out-of-the-box QA Agent.

✅
Our take
Works — verified, early-stage project

Test workbench Profile based on DeepSeek Harness — out-of-the-box QA Agent. It installs cleanly and boots without issues in our testing. It's early-stage but functional.

“Verified” means our automated CI actually ran dsh plugin add in a clean profile and it booted — nothing more. Feature descriptions and version compatibility are the author’s claims. This is not a security audit and not an endorsement of third-party code.

README

dsh-test-workbench

基于 DeepSeek Harness 的测试工作台 Profile —— 开箱即用的 QA Agent。

组装了三个插件(UI 验证、会话分析、视觉路由)+ 预置 Skill + QA 测试人格。

快速开始

前提:三个业务插件 dsh-tool-app-verify / dsh-session-analyst / dsh-llm-vision-router
需要先发布到 npm(npm publish),否则下面的 dsh plugin add 找不到它们。

# 1. 安装 dsh
npm install -g @deepseek-ai/dsh

# 2. 把本仓库克隆成名为 workbench 的 profile(profile 必须放在 ~/.dsh/profiles/<name>/ 下)
mkdir -p ~/.dsh/profiles
git clone https://github.com/dmsobtl/dsh-test-workbench.git ~/.dsh/profiles/workbench
cd ~/.dsh/profiles/workbench

# 3. 安装插件依赖(把三个插件拉进该 profile 的 node_modules)
pnpm install
# 或逐个:dsh plugin add dsh-tool-app-verify dsh-session-analyst dsh-llm-vision-router

# 4. 配置 API Key
export DEEPSEEK_API_KEY=your-deepseek-key
export OPENAI_API_KEY=your-openai-key   # 可选,用于有截图时切视觉模型

# 5. 启动(注意:profile 是名字,不是路径)
dsh --profile workbench "打开 https://example.com 并验证页面标题"

包含什么

插件

插件 功能
dsh-tool-app-verify 浏览器/Electron 操作 + UI 断言 + 视觉回归
dsh-session-analyst 会话质量分析 + 回归检测
dsh-llm-vision-router 有图片时自动切多模态模型

预置 Skill(位于 .agents/skills/,DSH 自动发现)

Skill 用途
test-login-flow 登录功能完整测试流程
visual-regression 视觉回归测试标准流程
session-diagnosis 测试会话效率诊断

QA 测试人格

Agent 默认以 QA 专家身份工作(见 cordis.patch.yml 中的 system-prompt persona)。

目录结构

dsh-test-workbench/
├── package.json          # dsh.profile.bundles: dsh-base + dsh-headless(一次性任务模式)
├── cordis.patch.yml      # 插入业务插件 + 覆盖 system-prompt 人格
├── pnpm-workspace.yaml   # 供 pnpm 管理 out-of-tree 插件
├── .agents/skills/       # 项目级 skill(DSH 自动加载)
│   ├── test-login-flow.md
│   ├── visual-regression.md
│   └── session-diagnosis.md
├── baselines/            # 截图基线存储(gitignore)
└── README.md

配置定制

切换视觉模型

编辑 cordis.patch.yml 中 vision-router 的配置:

- insert:
    - id: vision-router
      name: dsh-llm-vision-router
      config:
        visionProvider: qwen        # 换成通义千问
        visionModel: qwen-vl-plus

同时把 llm-pi-ai 的 providers 改成对应 provider(或另配 adapter)。

切换 Electron 模式

- insert:
    - id: app-verify
      name: dsh-tool-app-verify
      config:
        mode: electron

添加新 Skill

在 .agents/skills/ 目录下添加 markdown 文件即可,DSH 会自动加载。

使用示例

# 快速验证一个页面
dsh --profile workbench "打开 http://localhost:3000,检查首页所有链接是否可点击"

# 登录流程测试
dsh --profile workbench "用 skill test-login-flow 测试 http://localhost:3000/login,账号 [email protected] / password123"

# 视觉回归(先建基线,再对比)
dsh --profile workbench "打开 http://localhost:3000,对首页、登录页分别截图保存基线"
dsh --profile workbench "对比首页、登录页的当前截图与基线,输出回归报告"

# 分析上次测试
dsh --profile workbench "分析最近一次测试会话的效率"

成本估算

场景 DeepSeek 消耗 Vision 消耗
5 页面功能验证(无截图) ~50k tokens 0
3 页面视觉回归 ~30k tokens ~3 次调用
完整登录流程测试 ~80k tokens ~2 次调用

Roadmap

  • dsh-tool-test-runner:结构化测试执行(解析 vitest/jest/pytest)
  • dsh-tool-env-check:环境就绪度检查
  • dsh-tool-api-test:HTTP 接口测试
  • dsh-hook-test-report:turn 结束自动生成报告
  • CI 集成模板(GitHub Actions)
  • 定时巡检 + 钉钉推送

License

MIT

Install

🧩 Let your agent install it (recommended)

Install the catalog once, then DeepSeek Harness can find and install any plugin from this site automatically:

dsh plugin add dshbase-catalog

Then say "install dsh-test-workbench for me" — your agent finds it in the directory and installs it. Docs: dshbase-catalog · verified packs.

This plugin is GitHub source (not published to npm) — install it straight from the repo:

Web profile:

dsh plugin --profile web add github:dmsobtl/dsh-test-workbench

Headless (CLI) profile:

dsh plugin --profile headless add github:dmsobtl/dsh-test-workbench

Test report

Verified: L1 install + L2 load + L3 runtime from GitHub source on dsh 0.1.0-rc.6.

When to use it

Automate a repetitive job — scheduling, chaining tasks, or reacting to events — so it runs without you starting it.

Who it's for

Users with recurring work who want it cron-style and hands-off rather than manually triggered.

For developers — extending it

Triggers and task templates are the seams — add event-driven or file-watch triggers, and richer workflow composition.

Security: not yet scanned — our daily static scan will cover it shortly.

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