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 asherzj/ashers-agent-skills --skill domain-modelinggit clone --depth 1 https://github.com/asherzj/ashers-agent-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/asherzj/ashers-agent-skills/domain-modeling)<a href="https://agentmods.dev/skills/asherzj/ashers-agent-skills/domain-modeling"><img src="https://agentmods.dev/badge/skills/asherzj/ashers-agent-skills/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/asherzj/ashers-agent-skills/domain-modeling"><img src="https://agentmods.dev/badge/skills/asherzj/ashers-agent-skills/domain-modeling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00036 | $0.00874 |
| Opus 5 | $0.00018 | $0.00437 |
| Sonnet 5 | $0.00007 | $0.00175 |
| Haiku 4.5 | $0.00004 | $0.00087 |
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 10d 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
领域建模
在设计的同时，主动构建并打磨项目的领域模型（domain model）。这是一项主动的纪律：挑战术语、发明边缘案例场景，并在术语敲定的那一刻把词汇表和决策写下来。（仅仅为了查词汇而阅读 CONTEXT.md 不算本 skill：那是任何 skill 都能顺手做的一行习惯。本 skill 用于你要改变模型的时候，而不只是消费它。）
文件结构
大多数仓库只有单一上下文（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，仓库就有多个上下文。这份 map 指出每个上下文所在的位置：
/
├── CONTEXT-MAP.md
├── docs/
│ └── adr/ ← system-wide decisions
├── src/
│ ├── ordering/
│ │ ├── CONTEXT.md
│ │ └── docs/adr/ ← context-specific decisions
│ └── billing/
│ ├── CONTEXT.md
│ └── docs/adr/
惰性创建文件：只在有内容可写时才创建。如果不存在 CONTEXT.md，在第一个术语敲定时创建它。如果不存在 docs/adr/，在需要第一个 ADR（架构决策记录）时创建它。
会话期间
对照词汇表发起挑战
当用户使用的术语与 CONTEXT.md 中的既有语言冲突时，立即指出。"你的词汇表把 'cancellation' 定义为 X，但你似乎指的是 Y。到底是哪个？"
打磨含糊的语言
当用户使用含糊或多义的术语时，提出一个精确的规范术语。"你在说 'account'：你指的是 Customer 还是 User？它们是不同的东西。"
讨论具体场景
在讨论领域关系时，用具体场景对它们做压力测试。发明能够探查边缘案例的场景，迫使用户精确说明概念之间的边界。
与代码交叉验证
当用户陈述某样东西如何工作时，检查代码是否同意。发现矛盾时，把它摆出来："你的代码取消的是整个 Order，但你刚才说部分取消是可能的。哪个是对的？"
就地更新 CONTEXT.md
术语一旦敲定，当场更新 CONTEXT.md。不要攒起来批量处理：随发生随记录。使用 CONTEXT-FORMAT.md 中的格式。
CONTEXT.md 应当完全不含实现细节。不要把 CONTEXT.md 当作 spec、草稿本或实现决策的存放处。它是词汇表，仅此而已。
谨慎提议 ADR
只在以下三条全部成立时，才提议创建 ADR：
- 难以逆转：之后再改变主意的代价是实质性的
- 脱离上下文令人意外：未来的读者会问"他们为什么这么做？"
- 一次真实权衡的结果：存在真正的备选方案，而你出于具体理由选了其中一个
三条中任何一条不满足，就跳过 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.
- 10d ago First seen · 75 lines · 36 tokens per session scan A f1c96fda7648
domain-modeling is a skill published in the GitHub repository asherzj/ashers-agent-skills (2 stars, last pushed 13d ago), licensed MIT. It adds 36 tokens to every session and 874 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 2026-08-31.
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