Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Kokxi/qa-test-skills --skill qa-combination-strategygit clone --depth 1 https://github.com/Kokxi/qa-test-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/kokxi/qa-test-skills/qa-combination-strategy)<a href="https://agentmods.dev/skills/kokxi/qa-test-skills/qa-combination-strategy"><img src="https://agentmods.dev/badge/skills/kokxi/qa-test-skills/qa-combination-strategy/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/kokxi/qa-test-skills/qa-combination-strategy"><img src="https://agentmods.dev/badge/skills/kokxi/qa-test-skills/qa-combination-strategy.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00130 | $0.02832 |
| Opus 5 | $0.00065 | $0.01416 |
| Sonnet 5 | $0.00026 | $0.00566 |
| Haiku 4.5 | $0.00013 | $0.00283 |
Grade A, and why
qa-combination-strategy scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 270 lines — stays where its author put it; the contents beside it link to each section on GitHub.
⚠️ 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。
三种简化策略
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
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
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
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
组合测试流程
步骤1：识别参数
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
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
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：[这个组合要特别关注什么]
前置条件：[测试前需要准备什么]
测试步骤：[具体操作步骤]
预期结果：[期望的输出]
组合测试示例
场景：电商下单功能
参数识别：
- 用户类型：新用户、老用户、VIP
- 商品类型：普通商品、虚拟商品、预售商品
- 支付方式：微信、支付宝、银行卡
- 优惠券：无、满减券、折扣券
风险评估：
- 高风险：VIP+预售+折扣券（复杂业务规则）
- 中风险：新用户+虚拟商品（特殊流程）
- 低风险：老用户+普通商品+微信（常规流程）
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago Changed 50919939d544
- 10d ago First seen · 270 lines · 130 tokens per session scan A 0bb5c7670e8b
qa-combination-strategy is a skill published in the GitHub repository Kokxi/qa-test-skills (25 stars, last pushed 8d ago), licensed MIT. It adds 130 tokens to every session and 2,832 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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