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 agentmods add skills/infra403/agentic-engineering-lab/domain-modelingnpx skills add infra403/agentic-engineering-lab --skill domain-modelinggit clone --depth 1 https://github.com/infra403/agentic-engineering-labWrote 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/infra403/agentic-engineering-lab/domain-modeling)<a href="https://agentmods.dev/skills/infra403/agentic-engineering-lab/domain-modeling"><img src="https://agentmods.dev/badge/skills/infra403/agentic-engineering-lab/domain-modeling.svg" alt="Measured on agentmods" 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 | $0.00142 | $0.01584 |
| Opus 5 | $0.00071 | $0.00792 |
| Sonnet 5 | $0.00028 | $0.00317 |
| Haiku 4.5 | $0.00014 | $0.00158 |
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 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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Domain Modeling — 领域建模
核心理念
知识先行，角色后置。
每个建模决策必须引用 knowledge/ 中的决策框架 [K]，不凭直觉。
生成（Generator）和评估（Evaluator）由不同角色执行。
文件结构
.claude/skills/domain-modeling/
├── SKILL.md ← 你在这里
├── knowledge/domain-modeling.md ← DDD: 限界上下文/聚合/事件/CQRS
├── knowledge/software-design-philosophy.md ← APoSD: 深模块/信息隐藏/分层
├── roles/product-analyst.md ← Generator: 业务验证
├── roles/reviewer.md ← Evaluator: 对抗性评估
├── references/PROTOCOL.md ← 反 LLM 缺陷协议（全程约束）
└── templates/
├── checkpoint.md ← 阶段检查点格式
└── progress.md ← design-progress.json 格式
前置条件
本 skill 的输入是 product-discovery 的 checkpoint-1-discovery.yaml。
如果尚未完成需求发现，建议先使用 /product-design 需求分析。
启动步骤
- 读取本文件 — 了解工作流
- 读取
.claude/skills/domain-modeling/references/PROTOCOL.md— 了解反 LLM 缺陷约束 - 上下文重置 — 声明"阶段 2 开始，以下仅依赖 checkpoint-1"
- 加载
.claude/skills/domain-modeling/knowledge/domain-modeling.md— 建模的核心知识 - 加载
.claude/skills/domain-modeling/knowledge/software-design-philosophy.md— APoSD 深模块原则 - 走收敛循环 — GENERATE → EVALUATE → RESOLVE → CHECK
- 产出 checkpoint — checkpoint-2-modeling.yaml
Sprint 契约
- 输入： checkpoint-1-discovery.yaml（需求发现产出）
- 产出： 限界上下文图 + 上下文映射 + 聚合设计 + checkpoint-2-modeling.yaml
- 收敛标准： Rubric 每项 ≥ 7/10
- 迭代： 持续直到收敛（不限轮次）
- Generator：
.claude/skills/domain-modeling/roles/product-analyst.md（业务验证） - Evaluator：
.claude/skills/domain-modeling/roles/reviewer.md（或独立 Subagent）
收敛循环
┌─────────────────────────────────────────────────┐
│ GENERATE → EVALUATE → RESOLVE → CHECK │
│ (生成方案) (独立评估) (修正) (收敛检查) │
│ ▲ │ │
│ │ 未收敛 │ │
│ └──────────────────────────────────┘ │
│ 已收敛 ↓ │
│ CHECKPOINT（固化 + 交接） │
└─────────────────────────────────────────────────┘
What ships with it
7 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.
- 5d ago First seen · 125 lines · 142 tokens per session scan A d6a7fae32377
domain-modeling is a skill published in the GitHub repository infra403/agentic-engineering-lab (5 stars, last pushed 4mo ago), licensed MIT. It adds 142 tokens to every session and 1,584 once invoked, about $0.0007 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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