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-domain-modelinggit 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-domain-modeling)<a href="https://agentmods.dev/skills/kokxi/qa-test-skills/qa-domain-modeling"><img src="https://agentmods.dev/badge/skills/kokxi/qa-test-skills/qa-domain-modeling/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-domain-modeling"><img src="https://agentmods.dev/badge/skills/kokxi/qa-test-skills/qa-domain-modeling.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 | $0.00111 | $0.02512 |
| Opus 5 | $0.00056 | $0.01256 |
| Sonnet 5 | $0.00022 | $0.00502 |
| Haiku 4.5 | $0.00011 | $0.00251 |
Grade A, and why
qa-domain-modeling 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 7d 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 — 237 lines — stays where its author put it; the contents beside it link to each section on GitHub.
领域建模
核心原则
不只是画流程图，而是画出状态机、数据流向、一致性约束点。
三种建模视图
视图1：状态转换图
用途：跟踪关键对象的状态变化
状态机要素：
1. 状态：对象可能处于的状态
2. 事件：触发状态变更的事件
3. 转换：状态变更的路径
4. 守卫：状态转换的条件
5. 动作：状态转换时执行的操作
绘制方法：
1. 识别关键对象：什么对象有状态？
2. 列举状态：这个对象有哪些状态？
3. 标注转换：什么事件触发什么转换？
4. 标注条件：转换需要满足什么条件？
5. 标注动作：转换时执行什么操作？
示例（订单状态机）：
┌─────────┐ 用户下单 ┌─────────┐
│ 待支付 │──────────────→│ 已支付 │
└─────────┘ └─────────┘
│ │
│ 超时未支付 │ 商家发货
▼ ▼
┌─────────┐ ┌─────────┐
│ 已取消 │ │ 已发货 │
└─────────┘ └─────────┘
│
用户确认收货
▼
┌─────────┐
│ 已完成 │
└─────────┘
视图2：数据流图
用途：追踪数据在模块间的流转
数据流要素：
1. 数据源：数据从哪里来？
2. 数据处理：数据经过什么处理？
3. 数据存储：数据存储在哪里？
4. 数据消费：数据被谁使用？
5. 数据一致性：各处数据是否一致？
绘制方法：
1. 识别数据对象：什么数据在流转？
2. 追踪数据路径：数据经过哪些模块？
3. 标注数据操作：CRUD在哪里发生？
4. 标注一致性检查点：哪里需要验证数据一致？
5. 标注数据转换：数据格式在哪里变化？
示例（订单数据流）：
用户下单
│
▼
┌─────────┐
│ 订单服务 │──── 创建订单 ────→ ┌─────────┐
└─────────┘ │ 订单表 │
│ └─────────┘
│ 扣减库存 │
▼ │
┌─────────┐ │
│ 库存服务 │←──── 查询库存 ──────────┘
└─────────┘
│
│ 发起支付
▼
┌─────────┐
│ 支付服务 │──── 创建支付单 ────→ ┌─────────┐
└─────────┘ │ 支付表 │
│ └─────────┘
│ 支付回调
▼
┌─────────┐
│ 回调处理 │──── 更新订单状态 ────→ 订单表
└─────────┘
视图3：服务依赖图
用途：识别服务间依赖关系和故障影响
依赖图要素：
1. 服务节点：有哪些服务？
2. 依赖关系：谁依赖谁？
3. 调用方式：同步/异步？
4. 故障影响：挂了会怎样？
5. 降级方案：怎么容错？
绘制方法：
1. 识别服务：系统有哪些服务？
2. 识别依赖：服务间怎么调用？
3. 标注调用方式：同步/异步/MQ？
4. 标注故障影响：挂了影响什么？
5. 标注降级策略：怎么容错？
示例（电商服务依赖）：
┌─────────┐ 同步 ┌─────────┐
│ 订单服务 │──────────────→│ 库存服务 │
└─────────┘ └─────────┘
│ │
│ 同步 │ 同步
▼ ▼
┌─────────┐ ┌─────────┐
│ 支付服务 │ │ 商品服务 │
└─────────┘ └─────────┘
│
│ 异步(MQ)
▼
┌─────────┐
│ 通知服务 │
└─────────┘
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.
- 7d ago Changed 65ac69951f4c
- 12d ago First seen · 237 lines · 111 tokens per session scan A f1c5ff76d908
qa-domain-modeling is a skill published in the GitHub repository Kokxi/qa-test-skills (27 stars, last pushed 10d ago), licensed MIT. It adds 111 tokens to every session and 2,512 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.
Other skills, from other repositories
sparc-methodology
SPARC (Specification, Pseudocode, Architecture, Refinement, Completion) comprehensive development methodology with multi-agent orchestration.
swarm-advanced
Advanced swarm orchestration patterns for research, development, testing, and complex distributed workflows.
code-review
Reviews Spec Kit code changes for positive and negative test coverage, regression evidence for bug fixes, and consistent repository terminology. Use when reviewing a diff or pull request. Do not use for implementing changes or posting GitHub review actions.
agent-harness-fault-injection
Use when an agent workflow needs deterministic recovery evidence for sandbox, MCP/tool, worker, checkpoint, memory, or orchestration failures.
octocode-benchmark
Use when planning, running, grading, or reporting the by-hand Octocode research benchmark — pairwise matchups (Octocode anchor vs one baseline: gh+RTK, gh+Headroom, or plain gh) over markdown questions, with a fresh isolated runner agent per (question, arm, pass), one blind judge per question grading two answers X/Y…
octocode-graph-eval
Use when you need a measurable keep/discard loop — goal→KPI, baseline vs target, held-out checks, eval suites, or don't-stop-till-done against a runnable sensor. Not for ordinary ship checks where 'tests passed' is enough.