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Multica Review Test

Community
it235
multica-review-test

测试产物专属评审框架:T1 用例 Block/Revise/Pass、T2 覆盖率评估复核、T3 报告 PASS/FAIL/BLOCKED 三阶段评审,输出结论与修改清单汇报 Leader,供 TestReviewer 挂载。

Overview

Publisherit235
Repositorymultica-best-practices
Skill namemultica-review-test
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 Review Test 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/reviewer/multica-review-test
  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/reviewer/multica-review-test .claude/skills/multica-review-test
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Multica Review Test 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 Review Test 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 Review Test 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.

测试产物专业评审(TestReviewer)

本 skill 提供对 测试产物 的结构化评审。整合自 test-case-review-squad。

什么时候用

阶段评审对象结论模型
T1 阶段 Atest_cases.json + test_cases_{KEY}.xmind + 设计追溯Block / Revise / Pass
T2multica-test-t2-coverage 输出评估完成 / 评估 BLOCKED
T3合并报告 + UI mapping + Apifox JSONPASS / FAIL / BLOCKED(Leader 门禁)

Pass ≠ 导入 JIRA 授权。 本 role 永不调用 import_to_tracker.py。

智能体指令见 references/agent-instructions.md。


T1 审核(Block / Revise / Pass)

输入(缺主交付 → Block)

必需说明
功能用例 JSONjira_key + functional[]
XMindtest_cases_{JIRA_KEY}.xmind
需求依据Jira AC / 已采集 PRD

材料源(有其一即可还原 functional,不必要求评论里贴 JSON 路径):

  • 功能用例 JSON(jira_key + functional),或
  • XMind:test_cases_{JIRA_KEY}.xmind(含 Issue 附件),或
  • 已导入的 JIRA/测试管理平台 用例集(步骤 + 预期)

用例源与需求依据都缺 → Block,列出缺失项后停止。

审核顺序(不可跳步)

  1. 锁定材料 — Key 与 XMind 文件名一致;缺材料 → Block
  2. 机械校验 — steps/expected 等长;无 P0;无 wiki/TBD/缺少Figma/XXX;module 用 / 分层
  3. 需求对齐 — AC 对照表;假覆盖;用法二分(操作桶应进步骤,规则桶不得进 expected)
  4. 可执行性 — P1 全审;P2/P3 抽样(至少每模块 1 条 P2 + 1 条 P3,大模块可增抽);检查入口路径、权限语义、造数、预期可判定、优先级 P1/P2/P3
  5. 结论 — Block | Revise | Pass + 强制输出模板

详细勾选项见 checklist.md。

严重级别

级别典型
Critical缺交付;结构崩坏;大面积占位;KB 规则当 AC;写死密码
MajorAC 缺口;假覆盖;无入口;P0 或优先级错乱
Minor标题啰嗦;个别预期可更贴文案
结论条件
BlockCritical 或材料不足
Revise无 Critical,有 Major
Pass无 Critical;Major=0(或用户书面接受);可有 Minor

Revise 且用户授权修改 → 改 JSON → multica-test-t1-design 的 generate_xmind.py → 再审一轮;仍不 import。

T1 输出模板(强制)

markdown
# 用例审核报告 — {JIRA_KEY}

## 结论
**{Block|Revise|Pass}** — {一句话}

## 材料
- JSON: {path}
- XMind: {path}
- 需求依据: {摘要}
- 功能用例数: {n}

## Critical / Major / Minor
- …

## AC 覆盖对照
| 验收点 | 覆盖用例 | 状态 |
| --- | --- | --- |

## 知识库/历史使用抽查
- 操作桶写入步骤: 是/否/部分
- 规则扩写: 无/有

## 导入建议
- Block/Revise: 禁止导入;先修订并重生 XMind
- Pass: 可提交人工确认;**本 skill 不执行导入**。确认后对 Tester 下达「审核通过,导入 JIRA」

T2 / T3(Leader 门禁风格)

T2 额外检查

  • 材料 5 项在评论中可追溯
  • 未写成「测试报告 PASS/FAIL」
  • 「实现未交付」与「需补用例」分开列

T3 额外检查

  • G2.5 + deploy_url 一致
  • UI:functional 条数 = pytest 收集数
  • Apifox 报告路径存在
  • 合并报告逐条 AC 有证据

T2/T3 输出格式

text
【测试产物评审】<阶段 T2|T3> <链接或路径>
结论:PASS / FAIL / BLOCKED
阻断项:
- ...
建议项:
- ...
轮次:第 N / 3 轮

边界

  • 只评测试产物,不评实现代码
  • 不替代 Leader multica-verification
  • 第 3 轮仍 FAIL → 升级人类
  • 即使用户对本 Agent 说「导入 JIRA」,也禁止执行 import

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 Review Test AI skill do?

测试产物专属评审框架:T1 用例 Block/Revise/Pass、T2 覆盖率评估复核、T3 报告 PASS/FAIL/BLOCKED 三阶段评审,输出结论与修改清单汇报 Leader,供 TestReviewer 挂载。

Why use Multica Review Test on TypingMind?

Because you install it once and use it with any model. Multica Review Test 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 Review Test 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/reviewer/multica-review-test. 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 Review Test?

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 Review Test?

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

Is the Multica Review Test 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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