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/ai-driven-dev/framework/domain-modelnpx skills add ai-driven-dev/framework --skill domain-modelgit clone --depth 1 https://github.com/ai-driven-dev/frameworkWhat 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 | $0.00088 | $0.00522 |
| Opus 5 | $0.00044 | $0.00261 |
| Sonnet 5 | $0.00018 | $0.00104 |
| Haiku 4.5 | $0.00009 | $0.00052 |
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 yesterday.
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 — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Domain Model
Builds and places the typed vocabulary of the application: value objects, discriminant types,
and aggregate roots that live in src/domain/models/. The domain layer must never import from
application/ or infrastructure/.
Available actions
| # | Action | Role | Input |
|---|---|---|---|
| 01 | choose-shape |
Decide between value object, discriminant type, or aggregate | concept description |
| 02 | define-invariants |
Encode readonly fields, validation, factory | chosen shape from 01 |
| 03 | place |
Pick canonical file location, add named export | defined type from 02 |
| 04 | test |
Write unit tests for the domain type | placed type from 03 |
Default flow
01 → 02 → 03 → 04
Transversal rules
- All domain types must be free of
application/andinfrastructure/imports. - All fields are
readonly; return new instances for mutations. - Never inline a discriminant union used in ≥2 use-cases; place it in
src/domain/models/. - Named export only, no default export.
- File name is
kebab-case.ts. - Validate invariants in constructor or static factory; throw a typed domain error on invalid input.
- Module-level
constinCONSTANT_CASEfor any literal used more than once.
References
references/value-objects.md— value object conventions (readonly, equals, constructor params)references/discriminant-types.md— discriminant union placement rules and canonical locationsreferences/manifest.md— aggregate root conventions for the Manifest model
Invariant rules
references/value-objects.md— authoritative value object rulesreferences/discriminant-types.md— authoritative discriminant type rules
What ships with it
8 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.
- yesterday First seen · 50 lines · 88 tokens per session scan A 6141908d9238
domain-model is a skill published in the GitHub repository ai-driven-dev/framework (445 stars, last pushed 2d ago), licensed MIT. It adds 88 tokens to every session and 522 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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