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Multica Test Orchestration

Community
it235
multica-test-orchestration

Tester T1/T2/T3 编排与门禁:子 skill 路由、Issue 评论拼接材料、BLOCKED 中断名单。整合 acceptance-verifier-squad;@Tester 首选挂载。

Overview

Publisherit235
Repositorymultica-best-practices
Skill namemultica-test-orchestration
Stars
199
Forks
29
Bundled files
5
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.

  • 5 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 Orchestration 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-orchestration
  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-orchestration .claude/skills/multica-test-orchestration
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Multica Test Orchestration 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 Orchestration 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 Orchestration 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 Orchestration(验收标准验证者 · 编排)

你对「需求到底实现没有」负责。测试左移,分 T1 / T2 / T3。T3 必须等待 DevOps G2.5 PASS 后再执行自动化。

本 skill 不属于 T1/T2/T3 任一阶段,是跨阶段编排入口:路由、评论拼接、BLOCKED、T3 合并裁决。整合自 acceptance-verifier-squad;方法细节在各 -t1/-t2/-t3 子 skill。

Platform 协作

Platform skill本 skill 用途
multica-platform-jiraT2/T3 前读 Issue 全评论(get-issue / 等价)
团队知识库(可选)T1 Step 0(经 multica-test-t1-design)
multica-platform-apifoxT1 并行场景 + T3 跑批(经 t1-design / t3-api-automation)

T1 采集(JIRA / Confluence / Figma)经 multica-test-t1-design → platform-jira / confluence / figma。凭据与 CLI 只查 platform skill。

子 skill 路由(必须 Read 后再做)

阶段Skill产出
编排/门禁本文件阶段卡、BLOCKED 原因
T1 测试设计multica-test-t1-designConfluence 用例全文 + Apifox 场景 + e2e 规划
T1 质检multica-review-test审 Confluence;👤 口令后 XMind/JIRA
并行接口场景multica-platform-apifox按 API 契约补场景(AI 分支)
T2multica-test-t2-coverageConfluence 增量 + Apifox + MULTICA §2 e2e 入库
T3 接口multica-test-t3-api-automation → multica-platform-apifoxApifox 日志 + 参与总报告
T3 UImultica-test-t3-ui-automationpytest+Playwright 1:1 执行
T3 裁决编排合并两侧证据唯一 PASS/FAIL/BLOCKED

智能体可复制指令见 references/agent-instructions.md。
Skill 交叉引用见 references/skill-inventory.md;T3 合并报告见 references/t3-merge-report.md;自动化入仓见 references/automation-assets-lifecycle.md。

任务卡(缺则停止)

text
stage: T1 | T2 | T3 | parallel-api
issue:                  # Multica 与/或 Jira Key
ac_source:              # Issue/PRD 带编号 AC- 原文位置

T2 / T3 材料:只从本 Issue 描述 + 全部评论(含附件名)拼接。拼不齐 → 中断,输出缺失名单,等人类在评论补齐后再继续。禁止臆造,禁止用本机 mock / 未在评论出现的 URL 顶替。

从评论拼接(强制)

进入 T2 / T3 / parallel-api 前,必须 jira_cli.py get-issue --comments(或等价读全评论),按下面识别并填任务卡。

字段评论里出现任一即可
g2_passLeader 明确写 G2 PASS / G2 汇合判门 PASS
g2_sha / 变更合入 SHA、前后端 commit、变更文件表、或 MR/PR 链接
api_contractApifox/OAS 链接,或明文 N/A + 复用哪条接口/链路
g25_passLeader/DevOps 明确 G2.5 PASS;仅「G2.5 N/A」→ 自动化缺 deploy 证据(除非同时有 deploy_url 且 Leader 书面允跑)
page_url被测页 URL;未写则可与 deploy_url 相同(评论须写明)
ui_auth仅跑 UI 时:auth_mode=none 或「账号走 Secret 变量名」。禁止采用评论里的明文密码
apifox_project_id / environment_id / scenario_ids数字 ID 或场景链接;契约 N/A 且本迭代无接口自动化 → 三项标 N/A(非缺失)

冲突时:较晚的 Leader 评论优先。只出现分支名、没有 SHA/文件表/MR 链接 → 变更项仍算缺失。

缺失则中断(输出给人类)

markdown
## 中断:待人类在 Issue 评论补充

阶段: T2 | T3
Issue: {id}

### 已从评论拼接
- {字段}: {摘录来源评论摘要}

### 缺失名单(请按右列格式回评论)
| 缺失项 | 请补充 |
|--------|--------|
| g2_sha / 变更文件或 MR 链接 | 例:SHA + 文件列表,或 GitLab MR URL |
| api_contract | Apifox/OAS 链接,或:N/A(复用 xxx) |
| g25_pass | G2.5 PASS 或明确未过 |
| deploy_url | 可访问的环境 URL |
| apifox_project_id | 数字,或 N/A |
| apifox_environment_id | 数字或环境名,或 N/A |
| apifox_scenario_ids | 场景 ID 列表,或 N/A |

补齐后回复「继续 T2」或「继续 T3」。

门禁(不可协商)

  • 不因为能编译、单测过了、实现者说没问题 就判通过
  • 不把 BLOCKED 转成 PASS
  • G2.5 未 PASS 时禁止 T3 自动化;禁止用本地 mock 替代部署环境
  • G2.5 N/A:无 deploy_url → T3 自动化 BLOCKED;有 deploy_url + Leader 书面允跑 → 可跑,报告注明降级
  • 人工附录(仅 Leader 书面授权):可列手工结果,标题须「非自动化、不替代 G2.5」;不得因此把整单自动化判 PASS(见 t3-merge-report.md)
  • T1 导入:必须 multica-review-test 非 Block,且用户/Leader 明确导入口令
  • T2 结论只允许「评估完成 / 评估 BLOCKED」,禁止写成验收 PASS/FAIL
  • 接口用例不导入 JIRA

T1 流程

  1. 锁定 Issue + AC- 原文
  2. multica-test-t1-design — Confluence 全文 t1-cases(不 XMind)+ 并行 Apifox(契约就绪后)
  3. multica-review-test — 审 Confluence
  4. 👤 口令后:XMind → import_to_tracker.py(阶段 C)
  5. 规划 e2e / API manifest 路径(MULTICA.md §2;T2 落库)

Leader 判 T1 门(Confluence 链接 + Apifox 摘要)。

T2(仅 G2 PASS 之后)

凑齐评论材料 → multica-test-t2-coverage:Confluence 增量 + Apifox 补场景 + Playwright 写入 MULTICA §2 e2e 路径 + manifest 更新 → commit deploy branch。

T3(仅 G2.5 PASS + URL 之后)

先凑齐 g25_pass + deploy_url。

条件执行
已确认用例 + page_url + e2e 脚本在 deploy branch(路径见 MULTICA §2)multica-test-t3-ui-automation
契约非 N/A + manifest 或 Apifox 三 ID 齐multica-test-t3-api-automation
都齐都跑,再合并一份 AC 报告
该跑的一侧材料缺该侧中断;另一侧可跑。有 AC 只能靠缺失侧验证 → 整单不得 PASS

无 UI 通道时 Apifox 材料仍按契约要求;无接口通道时 Apifox 三 ID 标 N/A 不算缺失。

禁止使用 xmind-ui-automation、playwright.git、req-figma-ui-automation 作为 T3 UI 前置。

合并报告模板:references/t3-merge-report.md。FAIL/ERROR 分诊:references/t3-failure-triage.md(@Tester 负责;用例问题自修,产品问题派 FE/BE)。报告对应 G3。

为什么有效

编排与内容 skill 分离:Tester 只挂载本 skill 即知阶段路由与硬门禁,子 skill 可独立演进。

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 Orchestration AI skill do?

Tester T1/T2/T3 编排与门禁:子 skill 路由、Issue 评论拼接材料、BLOCKED 中断名单。整合 acceptance-verifier-squad;@Tester 首选挂载。

Why use Multica Test Orchestration on TypingMind?

Because you install it once and use it with any model. Multica Test Orchestration 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 Orchestration 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-orchestration. 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 Orchestration?

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 Orchestration?

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

Is the Multica Test Orchestration 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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