Borrowing it
Nothing to install: this file belongs to andrewesweet/ropey. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/andrewesweet/ropey/main/.agents/skills/domain-driven-design/SKILL.mdgit clone --depth 1 https://github.com/andrewesweet/ropeyWrote 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/andrewesweet/ropey/domain-driven-design)<a href="https://agentmods.dev/skills/andrewesweet/ropey/domain-driven-design"><img src="https://agentmods.dev/badge/skills/andrewesweet/ropey/domain-driven-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/andrewesweet/ropey/domain-driven-design"><img src="https://agentmods.dev/badge/skills/andrewesweet/ropey/domain-driven-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 | $0.00120 | $0.03087 |
| Opus 5 | $0.00060 | $0.01543 |
| Sonnet 5 | $0.00024 | $0.00617 |
| Haiku 4.5 | $0.00012 | $0.00309 |
Grade A, and why
domain-driven-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 8d 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.
This is a copy
86% identical to domain-driven-design — 21 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Domain-Driven Design Framework
Framework for tackling software complexity by modeling code around the business domain. The greatest risk in software is not technical failure -- it is building a model that does not reflect how the business actually works.
Core Principle
The model is the code; the code is the model. Software should embody a deep, shared understanding of the business domain. When domain experts and developers speak the same language and that language is directly expressed in the codebase, complexity becomes manageable and the system evolves gracefully as the business changes.
Scoring
Goal: 10/10. Rate any domain model 0-10 against the principles below. A 10/10 means full alignment with all guidelines; lower scores indicate gaps. Report the current score and the specific improvements needed to reach 10/10.
Framework
1. Ubiquitous Language
Core concept: A shared, rigorous language between developers and domain experts, used consistently in conversation, documentation, and code. When the language changes, the code changes -- and awkward naming in code feeds back into refining the language.
Why it works: Ambiguity is the root cause of most modeling failures. When a developer says "order" and an expert means "purchase request," bugs are inevitable; a ubiquitous language forces every name in code to map to a concept the business recognizes and validates.
Key insights:
- The language emerges from deep collaboration, not a glossary bolted on after the fact
- If a concept is hard to name, the model is likely wrong -- naming difficulty is a design signal
- Technical jargon (
DataProcessorvs.ClaimAdjudicator) hides domain logic from the experts who could correct it - Different bounded contexts may use the same word with different meanings -- and that is fine
Code applications:
| Context | Pattern | Example |
|---|---|---|
| Class/method naming | Name after domain concepts and verbs | LoanApplication, policy.underwrite() -- not RequestHandler, process() |
| Module structure | Organize by domain concept | shipping/, billing/ -- not controllers/, services/ |
| Code review | Reject technical-only names | Flag Manager, Helper, Processor, Utils as naming smells |
What ships with it
6 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.
- 8d ago First seen · 204 lines · 120 tokens per session scan A 204684a2d1bc
domain-driven-design is a skill published in the GitHub repository andrewesweet/ropey (0 stars, last pushed 2mo ago), licensed MIT. It adds 120 tokens to every session and 3,087 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to domain-driven-design, differing in 21 lines, and is treated as a copy.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…