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 tomzx/agents --skill create-domain-modelgit clone --depth 1 https://github.com/tomzx/agentsWrote 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/tomzx/agents/create-domain-model)<a href="https://agentmods.dev/skills/tomzx/agents/create-domain-model"><img src="https://agentmods.dev/badge/skills/tomzx/agents/create-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/tomzx/agents/create-domain-model"><img src="https://agentmods.dev/badge/skills/tomzx/agents/create-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 | $0.00090 | $0.01279 |
| Opus 5.5 | $0.00036 | $0.00512 |
| Sonnet 5.5 | $0.00018 | $0.00256 |
| Haiku 4.5 | $0.00009 | $0.00128 |
Grade A, and why
create-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 22d 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create Domain Model
Captures the structure of a domain: the entities that matter, how they relate, the precise meaning of each term, the rules that always hold, and the quantities the problem depends on.
It makes an unfamiliar domain understandable before solutioning, and gives requirements and specifications a shared vocabulary.
It is a one-off skill, most useful when entering an unfamiliar domain during the design or architecture process.
It is richer than the project-level .sdlc/context/vocabulary.md, which it reuses rather than redefines.
Prerequisites
- Apply the shared SDLC conventions in
skills/sdlc/references/shared.md. - If working within a feature, locate its directory under
.sdlc/features/N-<slug>/. - Read
.sdlc/context/vocabulary.mdif present, and reuse its terms instead of redefining them - Read any relevant requirements, specification, or architecture documents for context
Steps
- Read available context (requirements, specification, architecture, vocabulary) to identify the domain.
- Read
.sdlc/context/vocabulary.mdif present. Reuse existing project terms; only add terms that are specific to this domain and not already defined. - Identify the core entities: the nouns in the domain that carry meaning (people, things, events, records, concepts). Capture each with a short description and the attributes that matter.
- Identify the relationships between entities, with cardinality (one-to-one, one-to-many, many-to-many) and any constraint or rule that governs the relationship.
Render the entity model as a Mermaid
classDiagram: one class per entity with its key attributes, edges carrying cardinality, and each invariant attached as anoteon the entity it constrains. - Build a glossary: give each term a precise definition, and disambiguate any overloaded term (one word used two ways). Note where a term differs from general usage.
- State invariants: rules that always hold in this domain (business rules, constraints, identities). These are testable truths, not implementation details.
- Identify the key quantities and metrics the domain depends on, why each matters, and its current value if known.
- Define boundaries: what is in this domain versus adjacent domains it touches but does not model.
- Record open questions where the model is uncertain.
- Write the output to
domain-model.mdunder the relevant feature directory (.sdlc/features/N-<slug>/domain-model.md), or to a path provided by the user.
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.
- 22d ago Changed · +6 lines c2e49bc354c3
- 24d ago Changed da82cc2c4c01
- 2mo ago First seen · 84 lines · 90 tokens per session scan A 08f0fbe2be20
create-domain-model is a skill published in the GitHub repository tomzx/agents (13 stars, last pushed yesterday), licensed MIT. It adds 90 tokens to every session and 1,279 once invoked, about $0.0004 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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