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Multica Test T3 Ui Automation

Community
it235
multica-test-t3-ui-automation

T3 UI 自动化:pytest+Playwright,1 功能用例 = 1 个 e2e 测试;路径读 MULTICA.md §2;在 deploy URL 执行。

Overview

Publisherit235
Repositorymultica-best-practices
Skill namemultica-test-t3-ui-automation
Stars
199
Forks
29
Bundled files
2
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by it235 on GitHub. Read the source before you install it.

Installation

Install the Multica Test T3 Ui Automation AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/it235/multica-best-practices/tree/main/templates/zh_CN/skills/tester/multica-test-t3-ui-automation
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/it235/multica-best-practices.git /tmp/multica-best-practices
mkdir -p .claude/skills
cp -r /tmp/multica-best-practices/templates/zh_CN/skills/tester/multica-test-t3-ui-automation .claude/skills/multica-test-t3-ui-automation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Multica Test T3 Ui Automation in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Multica Test T3 Ui Automation on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Multica Test T3 Ui Automation is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Test UI Automation(功能用例 → UI 1:1)

把 T1 Confluence / T2 入库 的 Playwright 用例在 部署环境 执行。

T2 应在 MULTICA.md §2 UI E2E 路径 编写/积累脚本(见 docs/test-automation-in-repo.md)。T3 门禁见 multica-test-orchestration。

Platform 协作

Platform skill本 skill 用途
—无直接 platform 调用;page_url / 用例集来自 Issue 评论与 T1 产物

UI 在部署环境执行;环境 URL 由 DevOps / Leader 写入 JIRA 评论(见 multica-artifact-cicd-sync)。

原则

  • 入仓路径:Read 目标 repo MULTICA.md §2(分仓按 Issue 矩阵各读一份);缺文件 → BLOCKED
  • 无 e2e 目录时:在 MULTICA 指定路径按 framework-template.md 初始化(禁止 /tmp/ui-auto-*)
  • 禁止前置:xmind-ui-automation、req-figma-ui-automation、公司 playwright.git 外挂仓库(与本仓 MULTICA §2 路径不矛盾)
  • 1:1:每条自动化 CASE → 恰好一个 test_*.py
  • 步骤对齐:steps[i] → 第 i 个操作;expected_results[i] → 该步断言(等长)
  • 不编造:locator 来自 打开 page_url 后的真实页面;找不到 → BLOCKED

输入(缺则中断)

text
repo_root:               # Issue 仓库矩阵;Read MULTICA.md
e2e_dir:                 # MULTICA.md §2 UI E2E 路径(默认 tests/e2e/)
cases_source:            # T1 Confluence 或 e2e_dir 内 test_*.py
page_url / deploy_url:   # Issue 评论
jira_key:
auth_mode:               # none | form_login | sso | cookie
auth_user_env / auth_pass_env:

账号密码(强制)

  • page_url 不等于已登录;需登录系统必须给 auth_mode + env 变量名
  • 禁止把账号/密码写进:用例 JSON、脚本、XMind、JIRA 评论、Allure 正文
  • 用例前置只写权限语义;真实凭据只放 Agent Secret / gitignore .env
  • 执行前检查 auth_user_env/auth_pass_env 非空;为空 → 中断,提示配置 Secret 勿回贴 Issue
  • auth_mode=none:仅无需登录页面
  • auth_mode=cookie:本地 storage_state 路径,不把 cookie 贴评论

未确认、无 URL、需登录但 env 空、steps/expected 不等长 → 停止。

流程

Phase A — 确认用例集

  1. 只读 functional(忽略 api)
  2. 校验 len(steps)==len(expected_results) 且非空
  3. 列出 case_id → test 方法对照表(条数 = functional 条数)

Phase B — 框架(e2e_dir)

若 {repo_root}/{e2e_dir}/ 无 pytest.ini:按 framework-template.md 生成于 该目录。
已有本 skill 骨架 → 增量加用例。
勿在 repo 外另建平行 playwright 工程。

bash
cd {e2e_dir}   # MULTICA.md §2 UI E2E 路径
python -m venv .venv
.venv/Scripts/pip install -r requirements.txt
.venv/Scripts/playwright install chromium

Phase C — 一对一生成/补全

T2 应已建骨架;T3 补 locator、修断言。规则见 references/case-mapping.md。

Phase D — 执行

bash
cd {e2e_dir}
.venv/Scripts/pytest . -q --alluredir artifacts/allure-results

输出

产物说明
{e2e_dir}/ 变更commit 到 deploy branch(FIX 后同样)
artifacts/case_map.mdcase_id ↔ 文件 ↔ 结果
Allure / pytest 日志按条 PASS/FAIL/SKIP(BLOCKED)
Issue 评论映射表 + commit SHA + 失败明细

本 skill 不对整单 AC 做 PASS 裁决;T3 总报告由 multica-test-orchestration 合并。

FAIL / ERROR 分诊

UI 自动化 FAIL 时:@Tester 按 multica-test-orchestration/references/t3-failure-triage.md 分诊。locator/步骤错 → Tester 修;页面行为不符 AC → 派 FrontendDev。禁止 Frontend 未分诊就改 pytest 降标。

禁止

  • 使用 xmind-ui-automation、req-figma-ui-automation、外部 playwright.git 替代本仓 MULTICA §2 e2e 目录
  • 用例只存在于临时目录、不入 Git
  • 一条功能用例拆成多个 test 或多条合成一个
  • 把接口请求写进 UI 用例(接口走 Apifox)
  • G2.5 前把结果当成 T3 PASS
  • 把明文密码写进 Issue 或脚本

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Multica Test T3 Ui Automation AI skill do?

T3 UI 自动化:pytest+Playwright,1 功能用例 = 1 个 e2e 测试;路径读 MULTICA.md §2;在 deploy URL 执行。

Why use Multica Test T3 Ui Automation on TypingMind?

Because you install it once and use it with any model. Multica Test T3 Ui Automation is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Multica Test T3 Ui Automation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/it235/multica-best-practices/tree/main/templates/zh_CN/skills/tester/multica-test-t3-ui-automation. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Multica Test T3 Ui Automation?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Multica Test T3 Ui Automation?

As many as you like. As long as a model supports skills, you can use Multica Test T3 Ui Automation with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Multica Test T3 Ui Automation AI skill free?

Yes. It is published on GitHub by it235 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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