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 devcxl/mattpocock-skills-zh --skill domain-modelinggit clone --depth 1 https://github.com/devcxl/mattpocock-skills-zhWrote 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/devcxl/mattpocock-skills-zh/domain-modeling)<a href="https://agentmods.dev/skills/devcxl/mattpocock-skills-zh/domain-modeling"><img src="https://agentmods.dev/badge/skills/devcxl/mattpocock-skills-zh/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/devcxl/mattpocock-skills-zh/domain-modeling"><img src="https://agentmods.dev/badge/skills/devcxl/mattpocock-skills-zh/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 | $0.00035 | $0.00827 |
| Opus 5 | $0.00017 | $0.00413 |
| Sonnet 5 | $0.00007 | $0.00165 |
| Haiku 4.5 | $0.00003 | $0.00083 |
Grade A, and why
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 11d 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.
What it actually says
Domain Modeling（领域建模）
在设计过程中主动构建和完善项目的领域模型。这是一门主动的学科——质疑术语、创造边界场景、在术语和决策形成的瞬间将其记录在案。（仅仅阅读 CONTEXT.md 来获取词汇不是此 skill 的用途——那是任何 skill 都能做的单行习惯。此 skill 用于你在修改模型，而非仅仅消费它。）
文件结构
大多数 repo 只有一个 context：
/
├── CONTEXT.md
├── docs/
│ └── adr/
│ ├── 0001-event-sourced-orders.md
│ └── 0002-postgres-for-write-model.md
└── src/
如果根目录存在 CONTEXT-MAP.md，则该 repo 有多个 context。该 map 指向每个 context 的位置：
/
├── CONTEXT-MAP.md
├── docs/
│ └── adr/ ← 系统级决策
├── src/
│ ├── ordering/
│ │ ├── CONTEXT.md
│ │ └── docs/adr/ ← context 特定决策
│ └── billing/
│ ├── CONTEXT.md
│ └── docs/adr/
懒加载创建文件——仅当有内容需要写入时才创建。如果 CONTEXT.md 不存在，在第一个术语确定时创建。如果 docs/adr/ 不存在，在第一个 ADR 需要时创建。
在会话期间
对照 glossary 提出质疑
当用户使用与 CONTEXT.md 中已有语言冲突的术语时，立即指出。"你的 glossary 将 'cancellation' 定义为 X，但你似乎指的是 Y——到底是哪个？"
优化模糊语言
当用户使用模糊或过载的术语时，提出精确的标准术语。"你说 'account'——指的是 Customer 还是 User？它们是不同的东西。"
讨论具体场景
当领域关系被讨论时，用具体的场景进行压力测试。创造能够探测边界案例的场景，迫使用户精确描述概念之间的界限。
与代码交叉引用
当用户陈述某事物的工作方式时，检查代码是否一致。如果发现矛盾，指出："你的代码取消了整个 Orders，但你刚才说部分取消是可能的——哪个是对的？"
内联更新 CONTEXT.md
当术语确定时，立即更新 CONTEXT.md。不要批量处理——在发生时即时捕获。使用 CONTEXT-FORMAT.md 中的格式。
CONTEXT.md 应完全不含实现细节。不要将 CONTEXT.md 视为 spec、草稿本或实现决策的仓库。它是 glossary，仅此而已。
谨慎提供 ADR
仅当以下三个条件同时满足时才提供创建 ADR：
- 难以逆转——事后改变想法的成本很高
- 脱离上下文会令人困惑——未来的读者会想"他们为什么这样做？"
- 是真实权衡的结果——存在真正的替代方案，你因特定原因选择了其中一个
如果缺少任何一条，跳过 ADR。使用 ADR-FORMAT.md 中的格式。
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 75 lines · 35 tokens per session scan A 20bcb652ce2c
domain-modeling is a skill published in the GitHub repository devcxl/mattpocock-skills-zh (340 stars, last pushed 6d ago), licensed MIT. It adds 35 tokens to every session and 827 once invoked, about $0.0002 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
chinese-documentation
A Chinese technical-documentation style guide covering spacing, punctuation, numbers, terminology, and links when Chinese and English appear together.
chinese-code-review
A Chinese-language code-review communication guide with templates and severity levels for review comments.
chinese-commit-conventions
A Chinese-language guide to Conventional Commits, a format for writing consistent Git commit messages, plus related changelog, commit-checking, and commit-helper configuration.
systematic-debugging
A step-by-step method for finding the underlying cause of technical problems before changing code. It covers reading errors, reproducing failures, checking recent changes, and tracing data across system components.
dispatching-parallel-agents
A workflow for assigning separate coding agents to independent tasks that do not share state or depend on one another. Each agent receives a focused scope, constraints, and an expected result.
executing-plans
A procedure for carrying out a written software implementation plan in a separate session, including reviewing the plan, completing its tasks, and checking the results at defined review points.