Borrowing it
Nothing to install: this file belongs to rhi-zone/normalize. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/rhi-zone/normalize/master/.claude/skills/domain-model/SKILL.mdgit clone --depth 1 https://github.com/rhi-zone/normalizeWrote 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/rhi-zone/normalize/domain-model)<a href="https://agentmods.dev/skills/rhi-zone/normalize/domain-model"><img src="https://agentmods.dev/badge/skills/rhi-zone/normalize/domain-model/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/rhi-zone/normalize/domain-model"><img src="https://agentmods.dev/badge/skills/rhi-zone/normalize/domain-model.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 | $0.00048 | $0.00628 |
| Opus 5.5 | $0.00019 | $0.00251 |
| Sonnet 5.5 | $0.00010 | $0.00126 |
| Haiku 4.5 | $0.00005 | $0.00063 |
Grade A, and why
domain-model 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 2mo 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 — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Interview me relentlessly about every aspect of this plan until we reach a shared understanding. Walk down each branch of the design tree, resolving dependencies between decisions one-by-one. For each question, provide your recommended answer.
Ask the questions one at a time, waiting for feedback on each question before continuing.
If a question can be answered by exploring the codebase, explore the codebase instead.
Domain awareness
Read CONTEXT.md at the repo root before the interview begins. If it doesn't exist, create it now — don't wait.
During the session
Challenge against the glossary
When the user uses a term that conflicts with the existing language in CONTEXT.md, call it out immediately. "Your glossary defines 'cancellation' as X, but you seem to mean Y — which is it?"
Sharpen fuzzy language
When the user uses vague or overloaded terms, propose a precise canonical term. "You're saying 'account' — do you mean the Customer or the User? Those are different things."
Cross-reference with code
When the user states how something works, check whether the code agrees. If you find a contradiction, surface it: "Your code does X, but you just said Y — which is right?" Note: a contradiction between the stated design and the code requires the user to decide which is authoritative — don't silently patch the docs to match the code.
Update CONTEXT.md inline
When a term is resolved, update CONTEXT.md right there. Don't batch — capture while the decision is live.
Per-term format:
## TermName
_Avoid:_ synonym or related term easily confused with this one
One-sentence definition capturing what makes this term precise.
What goes wrong when it's confused with the avoided term.
Optional sections (use when they earn their place):
- Relationships — when terms have structural connections (cardinality, ownership, lifecycle), add a
## Relationshipssection with prose statements: "An Authority owns exactly one Room", "A Session belongs to one Room and one client". Useful when terms can be defined individually but connections between them are non-obvious. - Grouping — when the glossary grows past ~15 terms and natural clusters emerge (subdomain, lifecycle, actor), group terms under
## Group Nameheadings. Don't force grouping below that threshold.
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.
- 2mo ago First seen · 63 lines · 48 tokens per session scan A df4c2b679135
domain-model is a skill published in the GitHub repository rhi-zone/normalize (5 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 48 tokens to every session and 628 once invoked, about $0.0002 per session on Opus 5.5. 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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