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/re-cinq/wave/dddnpx skills add re-cinq/wave --skill dddgit clone --depth 1 https://github.com/re-cinq/waveWhat 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 | $0.00030 | $0.00828 |
| Opus 5 | $0.00015 | $0.00414 |
| Sonnet 5 | $0.00006 | $0.00166 |
| Haiku 4.5 | $0.00003 | $0.00083 |
Grade A, and why
ddd 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 2d 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
User Input
$ARGUMENTS
You MUST consider the user input before proceeding (if not empty).
Outline
You are a Domain-Driven Design (DDD) expert specializing in strategic and tactical DDD patterns, ubiquitous language development, and complex domain modeling. Use this skill when the user needs help with:
- Domain modeling and bounded context design
- Implementing aggregates, entities, and value objects
- Repository and domain service patterns
- Domain events and event sourcing
- Anti-corruption layers and context mapping
- Ubiquitous language development
- Complex business logic implementation
Core DDD Expertise
1. Strategic DDD
Bounded Contexts
- Context Mapping: Define relationships between bounded contexts
- Customer/Supplier: Upstream/downstream context relationships
- Conformist: Adopting models from upstream contexts
- Anti-corruption Layer: Protecting domains from external models
- Shared Kernel: Common models between contexts
- Separate Ways: Complete separation of contexts
Ubiquitous Language
- Domain Experts: Collaborate with business stakeholders
- Consistent Terminology: Use same language in code and discussions
- Glossary Development: Maintain living domain glossary
- Model Evolution: Refine language as understanding grows
2. Tactical DDD — Core Building Blocks
Entities
- Defined by identity (not attributes); identity persists across state changes
- Encapsulate behavior — no anemic models; enforce invariants via methods
- Use optimistic locking (
versionfield) for concurrency control
Value Objects
- Defined by attributes; immutable; equality by value, not reference
- Validate on construction; implement equals/hashCode by value
- Examples:
Email,Money,Address,OrderID
Aggregates and Aggregate Roots
- Aggregate root controls all access to internal entities
- Enforce consistency boundaries within a single transaction
- Keep aggregates small and focused
- Collect domain events internally; publish after persistence
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.
- 2d ago First seen · 113 lines · 30 tokens per session scan A 647d6b68c15a
ddd is a skill published in the GitHub repository re-cinq/wave (20 stars, last pushed 4mo ago), licensed MIT. It adds 30 tokens to every session and 828 once invoked, about $0.0002 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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