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Test Case Review

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fishzjp
test-case-review

Review existing test cases (legacy, others', AI) for coverage and executability: build a testable-points baseline, assess independently, revise in place with records. Not for: writing cases from scratch (test-case-writing), pipeline. 审查已有用例(存量/他人/AI 产出)的覆盖与可执行性:先建基准再独立评估,修订留审查记录。不用于:从零写用例、写时自审、流水线。

Overview

Publisherfishzjp
Repositoryqa-skills
Skill nametest-case-review
Stars
60
Forks
10
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Test Case Review 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/fishzjp/qa-skills/tree/main/skills/test-case-review
  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/fishzjp/qa-skills.git /tmp/qa-skills
mkdir -p .claude/skills
cp -r /tmp/qa-skills/skills/test-case-review .claude/skills/test-case-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Test Case Review 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 Test Case Review 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 Test Case Review 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.

测试用例审查(test-case-review)

回答"这些测试用例到底测得好不好"——事后、独立的审查(写时自审归 test-case-writing 阶段四)。

  • 输入:已有用例文件(markmap + Schema,若无可先行抽取)、PRD / 需求模型、代码仓库
  • 输出(落盘):直接修订用例文件(修订后重新抽取 Schema)+ 审查记录(文件末尾附录)
  • 审查记录内容:缺失 / 冗余 / 错误 / 高风险未覆盖,按 TC 编号列出

When to Use

  • 审存量用例资产(祖传用例、他人编写)是否覆盖到位、能否执行
  • 审 AI 产出的用例(覆盖 + 可执行性双线)
  • 需要一份独立于编写者的审查结论(写时自审不能替代)

When NOT to Use

  • 从零写用例 → test-case-writing
  • 写用例过程中的自审 → test-case-writing 阶段四(4A/4B 两层审查)
  • 端到端流水线中的审查环节 → 由 qa 调度本 skill,但单用户直接触发本 skill 同样适用
  • 代码变更后的回归范围选择 → regression-testing

工作流

1. 建立可测点基准(分母)

覆盖审查需要一个合法分母,按优先级取:

  1. 人工标注的可测点清单(存在时,最权威)
  2. 需求模型 + 与用户共同确认的可测点清单(审查开始前列出,请用户补漏确认)
  3. 仅有原始输入源 → 从 PRD/代码自行提炼可测点清单,**标注"未经确认"**并在交付时请用户复核

没有分母的覆盖率是给自己批改作业——基准缺失时如实说明,不编造覆盖率。

2. 覆盖审查(此时执行 ../core/coverage.md:核心 7 维逐项全检 + 横切可执行性)

  • 核心七维度逐项:功能主流程 / 输入校验 / 逆向操作与生命周期 / 状态流转 / 数据一致性 / 文档隐含需求 / 代码审查发现(有代码时);扩展维度(8–19)按 coverage.md「维度选取速查」按项目类型选取,不做机械全检(执行强度按消费方分流,见其文件头)
  • 状态流转维度复核时加载 ../core/methods/state-machine.md:按其"状态集 × 事件集 × 转换边"清单逐边核对用例覆盖(每边至少一条 + 非法转换/并发竞态/逆向边三类必补),用例没按状态机组织时反向自行提取状态机再核对,防"看起来有覆盖"
  • 二阶交叉(../core/testing-principles.md 第 3 节):写入路径 × 校验规则、失败 × 重试、标识 × 重复——存量用例最常见的系统性缺口
  • 对照基准逐点核记:已覆盖(TC 编号)/ 未覆盖 / 覆盖但断言错误 / 冗余(多条测同一点)/ 无效(测的不是本需求)

3. 可执行性审查(此时执行 ../core/executability.md 全部检查项)

逐条用例过八条硬标准,重点命中:

  • 占位符数据({xxx}、"某数据")、虚构入口、模糊判定("功能正常")、异步无时限、断言超强度、前置不可得无 TODO、正文代码内部、缺导读四件套

4. 正确性审查(有 PRD/代码时)

  • 用例预期结果与 PRD 规则 / 代码实现是否一致(静态裁决:代码为准,见 ../core/evidence.md)
  • 优先级标注合理性(P0 逐条过自检:失败则核心不可用?);风险等级对齐 ../core/risk-model.md(Critical 必有 P0)

5. 修订与落盘

  • 直接在用例文件中修订:补缺失用例(追加 TC 编号)、删除冗余、改正错误断言、补可执行性要素(导读区/时限/入口路径/具体数据);新增与改写的用例同样执行 ../core/case-format.md 格式硬约束(四段式/协作五段式、TC 编号、正文零代码内部)
  • 修订后重新抽取 Schema(字段与转义规则见 ../core/schema-extraction.md),并用 ../core/scripts/validate_schema.py 复验通过后再落盘
  • 文件末尾追加审查记录:
markdown
## 审查记录(test-case-review {日期})
- 基准:{人工标注 / 需求模型确认 / 自行提炼(未经确认)}
- 审查前:XX 条用例,XX 个模块
- 审查后:XX 条用例,XX 个模块
- 缺失(已补):TC-xx…({场景})
- 冗余(已删):TC-xx…
- 错误(已改):TC-xx…({问题→修正})
- 高风险未覆盖:{风险点 + 建议用例,无代码证据则标注}
- 可执行性修复:{占位符/时限/入口 等 XX 处}

6. 交付

给用户:修订后文件路径 + 审查记录摘要 + 基准可信度声明(是否经确认)+ 遗留建议(如"建议补充代码模式审查")。

Common Mistakes

错误后果正确做法
无基准直接评覆盖率自批自改,数字无意义先建可测点基准并声明可信度
只查覆盖不查可执行性覆盖 100% 但执行者无法开工覆盖 + 可执行性双线审查
只报问题不修订文件审查报告与用例文件两张皮直接修订 + 修订后重抽 Schema
审查记录写成独立报告文件产物分散,下游找不到记录追加在用例文件末尾
修订用例引入占位符/代码内部制造新的不可执行问题修订同样执行 ../core/executability.md 标准
把写时自审的活抢过来与 test-case-writing 职责重叠本 skill 是事后、独立审查

Frequently asked questions

What does the Test Case Review AI skill do?

Review existing test cases (legacy, others', AI) for coverage and executability: build a testable-points baseline, assess independently, revise in place with records. Not for: writing cases from scratch (test-case-writing), pipeline. 审查已有用例(存量/他人/AI 产出)的覆盖与可执行性:先建基准再独立评估,修订留审查记录。不用于:从零写用例、写时自审、流水线。

Why use Test Case Review on TypingMind?

Because you install it once and use it with any model. Test Case Review 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 Test Case Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/fishzjp/qa-skills/tree/main/skills/test-case-review. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Test Case Review?

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 Test Case Review?

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

Is the Test Case Review AI skill free?

Yes. It is published on GitHub by fishzjp 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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