NOASSERTION 授權；具備執行記憶的自然語言 Web/行動裝置自我改進測試框架，透過 MCP 工具與技能接入編碼代理；README 未明確提及 DeepSeek Harness，dsh 整合屬推斷。
DSH 整合
相容
作者聲明
安全稽核
未稽核
最後核實
2026-08-21
授權條款
NOASSERTION
01它能幫你完成什麼？
Author natural-language regression tests for web and mobile apps and run them from an agent-driven workflow
Self-healing test runs that recover from UI drift and flaky interactions, plus execution memory that improves future runs and catches regressions before shipping
QA engineers and software teams that want to write tests in plain language instead of brittle selectors
Wire environment setup, API calls, fixture seeding and teardown into test runs via sandboxed hooks
Node, Bun, Python or Bash hooks executed in isolated Docker containers, with structured outputs passed back into the active test run
Teams that need scripted environment or data preparation around UI tests
02如何將外掛接入 DeepSeek Harness？
先決條件
- Node.js project with npm — agent-qa is installed as a dev dependency via `npm install -D agent-qa`
- Docker installed before using hooks — Docker is required for the Node, Bun, Python and Bash hook containers
- Optional: `@vostride/agent-qa-subscription-auth` for Codex or Claude Code subscription auth
安裝步驟
- 01
`npm install -D agent-qa`
$ npm install -D agent-qa
- 02
Optional (Codex or Claude Code subscription auth): `npm install -D @vostride/agent-qa-subscription-auth`
$ npm install -D @vostride/agent-qa-subscription-auth
- 03
`npx agent-qa init`
$ npx agent-qa init
- 04
`npx agent-qa install-browsers --chromium`
$ npx agent-qa install-browsers --chromium
- 05
Mobile projects: `npx agent-qa install-mobile-drivers --all`
$ npx agent-qa install-mobile-drivers --all
- 06
`npx agent-qa dashboard --open` — start the dashboard, complete auth, and run tests from the UI
$ npx agent-qa dashboard --open
驗證整合成功
作者未說明
03DSH 整合程度與能力邊界
Exposes MCP and skills for coding agents (per README); DeepSeek Harness/dsh is not mentioned in the README, so usage inside dsh is inferred via MCP
Natural-language tests for web and mobile
test definitions written in human language (actions and assertions)→agents execute tests from visible roles, labels, and screen state
launches installed browsers / mobile drivers to execute test runssends test context to the LLM endpoint you configureSelf-healing execution with memory
failing sub-actions (click, fill, select) and past-run observations→re-observed UI and alternate paths within the same run; execution memory added to future runs
persists execution memory built from product, suite, and test observations for future runsSandboxed test hooks
Node, Bun, Python or Bash hook scripts→hooks run in isolated Docker containers; structured outputs passed back into the active run
runs Docker containers for hooks (Docker required)Bring your own LLM + dashboard/CLI/MCP
any OpenAI-/Anthropic-compatible endpoint, Gemini, local or open-source model, or Codex / Claude Code subscription→tests executed with the model of your choice; dashboard and CLI for humans, MCP and skills for coding agents
04適合誰？何時不該用？
適合
- QA engineers and software teams that want to write tests in plain language instead of brittle selectors
- Teams that need scripted environment or data preparation around UI tests
不適合
- Hooks require Docker — agent-qa runs Node, Bun, Python and Bash hooks in isolated Docker containers, so Docker must be installed before using hooks.
- The README never mentions DeepSeek Harness or dsh; running agent-qa inside dsh is inferred from the stated MCP and skills support for coding agents, not documented by the project.
05相容性、維護與安全提醒
- Hooks require Docker — agent-qa runs Node, Bun, Python and Bash hooks in isolated Docker containers, so Docker must be installed before using hooks.
- No bundled model — you must bring your own LLM via OpenAI-/Anthropic-compatible endpoints, Gemini, local or open-source models, or Codex / Claude Code subscriptions, which means network access and your own credentials/subscription.
- The README never mentions DeepSeek Harness or dsh; running agent-qa inside dsh is inferred from the stated MCP and skills support for coding agents, not documented by the project.
NOASSERTION · actively maintained (last push 2026-08-03)
06常見問題
agent-qa 官方支援 DeepSeek Harness 嗎？
README 沒有提及 DeepSeek Harness 或 dsh。agent-qa 提供給編碼代理的 MCP 與技能，並支援自帶模型，因此可作為基於 MCP 的測試工具搭配 dsh 使用，但專案本身並未記載此整合。
如何安裝 agent-qa？
執行 `npm install -D agent-qa`，接著執行 `npx agent-qa init` 並安裝執行階段支援，例如 `npx agent-qa install-browsers --chromium`（行動裝置專案：`install-mobile-drivers --all`）。用 `npx agent-qa dashboard --open` 啟動儀表板。若使用 Codex 或 Claude Code 訂閱驗證，還需安裝 `@vostride/agent-qa-subscription-auth`。
一定要安裝 Docker 嗎？
只有使用 hook 時才需要——agent-qa 在隔離的 Docker 容器中執行 Node、Bun、Python 與 Bash hook，使用 hook 前必須先安裝 Docker。
可以用哪些模型執行測試？
支援 OpenAI 或 Anthropic 相容端點、Gemini、本地或開源模型，以及 Codex、Claude Code 等訂閱，任選其一。
測試與記憶存放在哪裡？
測試、設定、hook、記憶與套件邏輯都以版本控制的程式碼形式存放在你的儲存庫中，任何變更都可以 diff、審查、重複使用並跨團隊分享。
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08資料與來源
**Built for humans and machines**: A polished dashboard and CLI for developers, plus MCP and skills for coding agents.
此頁面根據專案公開文件、儲存庫中繼資料與 DSH Plugins 的結構化解析所產生；最後核實於 2026-08-21。發現錯誤？提交更正。
最佳 DeepSeek Harness 外掛
從全目錄挑出的 12 個值得優先安裝的外掛，涵蓋各個分類。