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 hujianbest/harness-flow --skill hf-domain-modelinggit clone --depth 1 https://github.com/hujianbest/harness-flowWrote 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/hujianbest/harness-flow/hf-domain-modeling)<a href="https://agentmods.dev/skills/hujianbest/harness-flow/hf-domain-modeling"><img src="https://agentmods.dev/badge/skills/hujianbest/harness-flow/hf-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/hujianbest/harness-flow/hf-domain-modeling"><img src="https://agentmods.dev/badge/skills/hujianbest/harness-flow/hf-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 | $0.00042 | $0.00855 |
| Opus 5 | $0.00021 | $0.00428 |
| Sonnet 5 | $0.00008 | $0.00171 |
| Haiku 4.5 | $0.00004 | $0.00085 |
Grade A, and why
hf-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 12d 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
在设计过程中主动构建并打磨项目的领域模型。这是一种主动的实践——质疑术语、构造边界情况，并在术语表和决策一经明确时立即将其写下。（仅仅通过读取 CONTEXT.md 获取词汇不属于本技能——那是任何技能都能做到的一行式习惯。本技能用于你正在改变模型时，而不只是使用模型时。）
文件结构
大多数仓库只有一个上下文：
/
├── CONTEXT.md
├── docs/
│ └── adr/
│ ├── 0001-event-sourced-orders.md
│ └── 0002-postgres-for-write-model.md
└── src/
如果根目录中存在 CONTEXT-MAP.md，仓库就有多个上下文。该映射会指出每个上下文所在的位置：
/
├── CONTEXT-MAP.md
├── docs/
│ └── adr/ ← 系统级决策
├── src/
│ ├── ordering/
│ │ ├── CONTEXT.md
│ │ └── docs/adr/ ← 特定上下文的决策
│ └── billing/
│ ├── CONTEXT.md
│ └── docs/adr/
延迟创建文件——只在有内容需要写入时创建。如果不存在 CONTEXT.md，就在第一个术语确定时创建。如果不存在 docs/adr/，就在需要第一份 ADR 时创建。
会话期间
根据术语表提出质疑
当用户使用的术语与 CONTEXT.md 中的现有语言冲突时，立即指出：“你的术语表将‘取消’定义为 X，但你似乎想表达 Y——究竟是哪一个？”
打磨模糊语言
当用户使用含糊或含义过载的术语时，提出一个精确的规范术语：“你说的是‘账户’——你指的是客户还是用户？它们是不同的概念。”
讨论具体场景
讨论领域关系时，用具体场景对其进行压力测试。构造能够探查边界情况的场景，促使用户精确说明概念之间的边界。
与代码交叉核对
当用户说明某事如何运作时，检查代码是否与之相符。如果发现矛盾，就明确指出：“你的代码会取消整个订单，但你刚才说可以部分取消——哪一个才是正确的？”
就地更新 CONTEXT.md
术语一旦确定，就立即在原处更新 CONTEXT.md。不要批量积攒——发生时就记录。使用 CONTEXT-FORMAT.md 中的格式。
CONTEXT.md 应当完全不包含实现细节。不要把 CONTEXT.md 当作规格、草稿区或实现决策的存储库。它只是术语表，不作他用。
谨慎建议使用 ADR
只有以下三个条件全部满足时，才建议创建 ADR：
- 难以逆转——日后改变主意会付出实质性成本
- 脱离上下文会令人意外——未来的读者会疑惑“他们为什么要这样做？”
- 真实权衡的结果——确实存在其他可选方案，而你出于特定原因选择了其中一个
只要缺少其中任何一个条件，就不要创建 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.
- 12d ago First seen · 75 lines · 42 tokens per session scan A c2e3ee9b9fa1
hf-domain-modeling is a skill published in the GitHub repository hujianbest/harness-flow (53 stars, last pushed 11d ago), licensed MIT. It adds 42 tokens to every session and 855 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.
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