Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add rjmurillo/ai-agents/plugin install project-toolkitWrote 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/rjmurillo/ai-agents/domain-design)<a href="https://agentmods.dev/skills/rjmurillo/ai-agents/domain-design"><img src="https://agentmods.dev/badge/skills/rjmurillo/ai-agents/domain-design/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/rjmurillo/ai-agents/domain-design"><img src="https://agentmods.dev/badge/skills/rjmurillo/ai-agents/domain-design.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 | $0.00082 | $0.02146 |
| Opus 5.5 | $0.00033 | $0.00858 |
| Sonnet 5.5 | $0.00016 | $0.00429 |
| Haiku 4.5 | $0.00008 | $0.00215 |
Grade A, and why
domain-design 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 4d 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 — 202 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Domain Design
Design from the behavior the business must observe and control. Source shape and implementation convenience are inputs, not the domain model.
Owns one invariant: an API, schema, or module boundary is derived from business
questions and operations, then validated against them. It does not mandate DDD
tactical patterns, ban CRUD, or replace architecture review or the ADR
workflow. Quality principles (cohesion, coupling, YAGNI, minimal
implementation) belong to the code-qualities-assessment skill; this skill
applies them and does not restate them.
Triggers
design this schema, API design, where should this module boundary go,
CRUD or domain operation, is this explicit state or reconstruction
When It Applies
A change adds or reshapes an API contract, a table or document schema, or a module boundary. A change that reuses an existing contract unchanged skips this skill.
Decision flow
business question or operation
-> observable behavior + invariant
-> state that must be explicit
-> API/module contract, and schema/history model
-> validate against use cases
-> simpler adequate model? yes: adopt. no: revise boundaries and state.
Process
- Discover the business question. Name the decisions, actions, and questions the system must support. Who observes or invokes each? Which outcomes, invariants, and time semantics matter? Write each question as a sentence a business owner would say.
- Derive state. For each fact a question needs, decide: safe derivation or hidden reconstruction. A value computed cheaply from state you already hold is a derivation. A first-class fact rebuilt by joining or probing incidental rows (for example, "what have we fetched?" answered by scanning fact rows) is a reconstruction obligation. Row presence cannot tell a partial fetch from a complete one. Record that fact as explicit state. Mark which state changes over time and so needs history or effective-time.
- Derive operations. Use a domain operation when the behavior carries invariants or multi-step transaction semantics that generic CRUD would leak (for example, "replace this site's reporting window"). Use CRUD when the entity is plain create, read, update, delete with no extra invariant. Do not invent domain verbs to avoid CRUD.
- Derive the schema. Model domain state and invariants first. Treat an external or source payload as an integration contract that feeds the model, not as the model. Make important business facts queryable without rebuilding them from incidental traces.
- Derive boundaries. Align module and API boundaries with behavior, invariants, ownership, and reasons to change. Reject a boundary that exists only because the payload, ORM, or transport format has that shape.
- Validate against use cases. Walk three to five representative questions and commands through the design. Record any that need unrelated-table joins, inferred time, derived-state reconstruction, or coordination across boundaries. Treat repeated reconstruction or change amplification as design evidence, not query inconvenience. If a simpler adequate model exists, adopt it. Otherwise revise state or boundaries and walk again.
What ships with it
1 file 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.
- 4d ago First seen · 202 lines · 82 tokens per session scan A 8ebfff6a8c38
domain-design is a skill published in the GitHub repository rjmurillo/ai-agents (48 stars, last pushed today), licensed MIT. It adds 82 tokens to every session and 2,146 once invoked, about $0.0003 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 2026-10-14.
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