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qa-combination-strategy

当参数多、环境多、"全组合测不完"时运用正交试验法、Pairwise和判定表来解决组合爆炸问题。如果系统有多个输入字段的组合依赖关系（如"A=1且B=2时C不能为3"）、或者需要适配多浏览器多操作系统多语言，一定要用此技能来设计高效的组合覆盖方案。不要试图全覆盖——组合测试的核心是用最少的用例达到最高的组合覆盖率。输出组合覆盖矩阵并标注覆盖遗漏。触发场景：组合测试、参数组合、正交测试、组合爆炸、Pairwise、全组合测不完、判断表、参数多环境多时。 Use when the user asks about: parameter and environment combination explosion — orthogonal arrays, pairwise testing, decision tables, and combinatorial test reduction. Skill: qa-combination-strategy Owner: kokxi Summary: 当参数多、环境多、"全组合测不完"时运用正交试验法、Pairwise和判定表来解决组合爆炸问题。如果系统有多个输入字段的组合依赖关系（如"A=1且B=2时C不能为3"）、或者需要适配多浏览器多操作系统多语言，一定要用此技能来设计高效的组合覆盖方案。不要试图全覆盖——组合测试的核心是用最少的用例达到最高的组合覆盖率。输出组合覆盖矩阵并标注覆盖遗漏。触发场景：组合测试、参数组合、正交测试、组合爆炸、Pairwise、全组合测不完、判断表、参数多环境多时。 Use when the user asks about: parameter and environment combination explosion — orthogonal arrays, pairwise testing, decision tables, and

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Rank

Safety

Downloads

1.0k

Updated

Oct 11, 2026

Version

1.8.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1K downloadsadoption · observed Oct 11, 2026
Latest release: 1.8.0release · observed Sep 29, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s170jw3s1atcj5jwhqb4r7v7eh8912kp:qa-combination-strategy

Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-kokxi-qa-combination-strategy/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

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Extracted files

4 files captured from the source.

SKILL.md

---
name: qa-combination-strategy
description: >-
  当参数多、环境多、"全组合测不完"时运用正交试验法、Pairwise和判定表来解决组合爆炸问题。如果系统有多个输入字段的组合依赖关系（如"A=1且B=2时C不能为3"）、或者需要适配多浏览器多操作系统多语言，一定要用此技能来设计高效的组合覆盖方案。不要试图全覆盖——组合测试的核心是用最少的用例达到最高的组合覆盖率。输出组合覆盖矩阵并标注覆盖遗漏。 触发场景：组合测试、参数组合、正交测试、组合爆炸、Pairwise、全组合测不完、判断表、参数多环境多时。 Use when the user asks about: parameter and environment combination explosion — orthogonal arrays, pairwise testing, decision tables, and combinatorial test reduction.
license: MIT
allowed-tools: Read Grep Glob
metadata:
  display-name: "Combination Strategy"
  version: "1.8.0"
  when-to-use: "用户说\"组合测试\"、\"参数组合\"、\"正交测试\"、\"组合爆炸\"、\"Pairwise\"、\"全组合测不完\"、\"判断表\"、需要简化测试组合、参数多环境多时"
  related-skills: "{\"upstream\":[\"qa-scenario-tree\"],\"downstream\":[\"qa-ai-context-engineering\"]}"
  references: "[\"references/reduction-strategies.md\"]"
  input-format: "{\"required\":[{\"name\":\"场景树\",\"type\":\"object\",\"description\":\"来自qa-scenario-tree的场景树\"},{\"name\":\"需求解构表\",\"type\":\"object\",\"description\":\"来自qa-req-deconstruction的需求解构结果\"}],\"optional\":[{\"name\":\"风险评估\",\"type\":\"object\",\"description\":\"来自qa-risk-intuition的风险评估\"}]}"
  output-format: "{\"traceability\":[\"每个组合矩阵带唯一ID（COMBO-XXXX）\",\"关联场景ID：SC-{场景模块缩写}-{序号}\"],\"structure\":[\"覆盖率：标注口径（基于现有需求/输入文档），禁止\\\"全覆盖/100%\\\"绝对化表述；缺失模块标注\\\"未覆盖+原因\\\"\",{\"combination_matrix\":\"组合覆盖矩阵\"},{\"pairwise_combinations\":\"成对组合列表\"},{\"n_way_combinations\":\"N维组合分析\"},{\"coverage_gaps\":\"组合覆盖遗漏\"}]}"
  error-recovery-guidance: "{\"on_failure\":\"回退到全组合方案或增加取样率\",\"retry_behavior\":\"扩大取样范围后重新生成组合方案\"}"
  categories: "[\"Development\",\"Testing\"]"
  depth-requirement: "{\"reference_value\":\"根据参数数量和取值范围调整策略深度：简单×1/中等×2/复杂×3\",\"minimum\":\"至少使用1种组合简化策略\"}"
---
> ⚠️ 本技能单独使用效果有限，建议配合完整技能集（12 步工作流）使用。安装：npx skills add Kokxi/qa-test-skills

> **⚠️ 安全警告**：本技能的示例可能涉及订单号、支付金额、截图、身份证、手机号等敏感数据。
> 实际使用时请勿粘贴真实生产数据、客户信息或财务凭证；测试前应脱敏/掩码处理。
> 本技能仅在 workspace/ 输出评估文件，不持久化、不外传、不跨会话复用。


# 组合测试策略

## 核心原则

全组合测不完，不测又怕漏——用科学方法简化。提供 Pairwise、正交实验法、风险加权组合三种简化策略。组合数必须少于全量组合，并标注覆盖分析。

## 加载时机

| 什么时候读 | 读哪个 |
|-----------|--------|
| 设计 Pairwise/正交/风险加权时 | [`references/reduction-strategies.md`](references/reduction-strategies.md) |

> `三种简化策略`的完整内容已下沉至 `references/reduction-strategies.md`，避免每次触发都占用上下文。

## 组合测试流程

### 步骤1：识别参数
```text
参数识别清单：
├─ 输入参数
│   ├─ 用户输入：文本、数字、选择
│   ├─ 系统参数：配置、环境
│   └─ 数据参数：数据状态、数据量
│
├─ 环境参数
│   ├─ 浏览器：Chrome, Firefox, Safari
│   ├─ 操作系统：Windows, Mac, Linux
│   ├─ 设备：PC, Mobile, Tablet
│   └─ 网络：WiFi, 4G, 弱网
│
└─ 状态参数
    ├─ 用户状态：新用户、老用户、VIP
    ├─ 数据状态：空、少量、大量
    └─ 系统状态：正常、高负载、异常
```

### 步骤2：评估风险
**风险评估矩阵：**

| 参数组合 | 业务影响 | 历史缺陷 | 技术复杂度 | 风险等级 |
|---------|---------|---------|-----------|---------|
| A1+B1 | 高 | 有 | 高 | 高 |
| A1+B2 | 中 | 无 | 中 | 中 |
| A2+B1 | 低 | 无 | 低 | 低 |

### 步骤3：选择策略
```text
根据风险等级选择测试策略：

高风险组合 → 全组合测试
  示例：支付+优惠券+退款

中风险组合 → Pairwise测试
  示例：浏览器×操作系统×分辨率

低风险组合 → 单参数测试
  示例：不同字体、不同颜色
```

### 步骤4：生成用例
```text
用例生成模板：

用例编号：COMBO-001
组合内容：[参数1=值1, 参数2=值2, 参数3=值3]
风险等级：高/中/低
测试重点

_meta.json

{
  "ownerId": "kn71y9b23csfx0ykgm55d5m9x5891zt8",
  "slug": "qa-combination-strategy",
  "version": "1.8.0",
  "publishedAt": 1790655969215
}

references/reduction-strategies.md

# 组合爆炸简化三策略详解

> 本文是 `qa-combination-strategy` 的**组合爆炸简化三策略详解**。设计 Pairwise/正交/风险加权时读本文；
其余部分留在 SKILL.md，不必读本文。

---


### 策略1：Pairwise（两两组合）
**原理**：每个参数的每个值，至少与其他参数的每个值组合一次

```text
适用场景：
- 参数数量中等（3-10个）
- 每个参数取值不多（2-5个）
- 需要较全面覆盖

生成方法：
1. 列出所有参数及其取值
2. 使用Pairwise算法生成最小用例集
3. 人工审核补充高风险组合

示例：
参数A：a1, a2
参数B：b1, b2, b3
参数C：c1, c2

全组合：2×3×2=12个
Pairwise：最多4-5个用例
```

### 策略2：正交实验法
**原理**：用正交表安排实验，均衡分散、整齐可比

```text
适用场景：
- 参数数量多（>10个）
- 需要统计分析
- 因素间交互作用小

正交表选择：
- L4(2^3)：3个2水平因素，4次实验
- L8(2^7)：7个2水平因素，8次实验
- L9(3^4)：4个3水平因素，9次实验
- L16(4^5)：5个4水平因素，16次实验

示例：
因素：
- 浏览器：Chrome, Firefox, Safari（3水平）
- 操作系统：Windows, Mac, Linux（3水平）
- 分辨率：1024x768, 1920x1080（2水平）

选择L18(3^7)正交表
```

### 策略3：风险加权组合
**原理**：高风险组合多测，低风险组合少测

```text
适用场景：
- 已有历史缺陷数据
- 能识别高风险区域
- 需要重点突破

风险评估维度：
1. 业务影响：出问题影响多大？
2. 历史缺陷：这里出过Bug吗？
3. 技术复杂度：实现复杂吗？
4. 变更频率：经常改吗？

风险等级：
- 高风险：全组合测试
- 中风险：Pairwise测试
- 低风险：单参数测试
```

skill-card.md

## Description:

Helps QA teams reduce parameter and environment test combinations using pairwise testing, orthogonal designs, and risk-based selection while documenting coverage gaps.

This skill is ready for commercial/non-commercial use.

## Publisher:

[kokxi](https://clawhub.ai/user/kokxi)

### License/Terms of Use:

MIT-0

## Use Case:

QA engineers and developers use this skill to design manageable combinatorial test cases for multi-parameter features, prioritize higher-risk combinations, and identify coverage gaps.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Examples may contain customer, payment, identity, or production data.

Mitigation: Use synthetic or masked test data; do not paste real sensitive records into prompts.

Risk: The optional command installs a larger skill bundle without a pinned version.

Mitigation: Review the publisher and bundle source before running it, and pin a reviewed version where possible.

## Reference(s):

- [ClawHub skill release](https://clawhub.ai/kokxi/skills/qa-combination-strategy)
- [Combination reduction strategies](references/reduction-strategies.md)

## Skill Output:

**Output Type(s):** [Markdown, Testing guidance]

**Output Format:** [Markdown tables and test-case lists]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Combination matrices with IDs, linked scenario IDs, pairwise and N-way analyses, stated coverage basis, and coverage gaps.]

## Skill Version(s):

1.8.0 (source: ClawHub release and skill frontmatter)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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