Build the project's shared language and bounded contexts before writing code, so names stay consistent and the agent stops paraphrasing domain concepts. Produces a CONTEXT.md glossary and decision records. Use at the start of a project or feature, or when the codebase and the people describing it speak different languages.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# domain-modeling
Most misbuilds start as a language gap: the agent is dropped into a project
and left to infer the jargon, so it uses twenty words where the domain has
one. A shared language closes the gap. When code, conversation, and the model
all draw from the same vocabulary, names line up, navigation gets cheaper, and
the model spends fewer tokens reasoning because it has a tighter language to
reason in.
## Method
1. **Harvest the terms.** From the request, the codebase, and the user's own
words, list the nouns and verbs that carry domain meaning - the concepts a
newcomer would have to ask about. Prefer the user's word over a synonym you
like better.
2. **Pin each one.** Write a one-line definition in the project's own language,
not a dictionary definition. If two terms blur together, force the
distinction or collapse them - ambiguity here becomes inconsistent names in
code.
3. **Draw the boundaries.** Where the same word means different things in
different parts of the system, that is a boundary. Name each context and
note which terms belong to it. A term that means two things is two terms.
…
The line at the top of this page pins the label git:20260718.7f7209d, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
GET https://markdownregistry.com/api/v1/artifacts/art_2zvp5rc6npjryqky
GET https://markdownregistry.com/api/v1/resolve?ref=rohitg00/pro-workflow/domain-modeling
GET https://markdownregistry.com/api/v1/blob/0e9ba07d6d34b1ee05ac40072e345a1d7abc7e00d9778db98e025bee8db37eb1
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
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