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/helderberto/agent-skills/domain-modelingnpx skills add helderberto/agent-skills --skill domain-modelinggit clone --depth 1 https://github.com/helderberto/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/helderberto/agent-skills/domain-modeling)<a href="https://agentmods.dev/skills/helderberto/agent-skills/domain-modeling"><img src="https://agentmods.dev/badge/skills/helderberto/agent-skills/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 5.1 | $0.00084 | $0.00378 |
| Opus 5 | $0.00042 | $0.00189 |
| Sonnet 5 | $0.00017 | $0.00076 |
| Haiku 4.5 | $0.00008 | $0.00038 |
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 6d 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
The active discipline of shaping the model — challenging terms, inventing edge-case scenarios, and writing vocabulary down the moment it crystallizes. Merely reading a glossary for vocabulary is a one-line habit any skill does; this skill is for when you're changing the model, not consuming it.
Artifact
Maintain the project's glossary in its existing convention. If there is none, create CONTEXT.md at the repo root. Record decisions that settle a term via /create-adr so the why is preserved separately from the what.
Ubiquitous language
One term, one meaning, used everywhere — prose, code, docs, and the glossary agree. For each term write:
- A crisp definition.
- An Avoid list of the rejected synonyms, so the same concept never travels under two names.
When the same word means two things, that ambiguity is the work — split it into two named terms and retire the overloaded one.
Sharpen by stress
Invent edge-case scenarios that a term must survive. A definition that can't classify the edge case is still fuzzy — refine it until it can, then write it down immediately. Flag ambiguities you can't yet resolve; record the resolution once decided.
Completion: every term touched has a definition and an Avoid list, and every flagged ambiguity is either resolved or explicitly parked.
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.
- 6d ago First seen · 29 lines · 84 tokens per session scan A c874d0a27a84
domain-modeling is a skill published in the GitHub repository helderberto/agent-skills (14 stars, last pushed 5d ago), licensed MIT. It adds 84 tokens to every session and 378 once invoked, about $0.0004 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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