Use when designing a product or feature and the project lacks shared, agreed terminology - builds and maintains a pure domain glossary so agents speak one language.
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
## Overview
A project's agents are verbose and misaligned when they lack a shared language: the same concept gets three names, one name covers three concepts, and every prompt re-explains what a term means. A tight glossary fixes this — it collapses ambiguity and tokens at once.
**Core principle:** the glossary is **terminology only**. One authoritative definition per term, and nothing else. It is never a spec, never an architecture doc, never a scratchpad. The moment an entry carries implementation detail or a design decision, it stops being a glossary and starts rotting.
The glossary lives at `{{specs_dir}}/glossary.md`.
## When to Use
- Defining a product or feature and a term keeps getting used loosely or two ways ([[design-product]], [[understanding-feature-requests]])
- Architecture work surfaces concepts the team has no agreed name for ([[design-architecture]])
- You catch yourself re-explaining the same concept across prompts or docs
## Lazy Creation
…
The line at the top of this page pins the label git:20260622.3609064, 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.
A 17 of 17 checks passed. Fixed automatic checks with no AI involved, so the same checks give the same answer every run.
pass: Frontmatter block present
pass: Frontmatter declares a name
pass: Frontmatter declares a description
pass: Size between 200 bytes and 200 KB (5011 bytes)
pass: No zero-width or bidi control characters
pass: No instruction hidden inside an HTML comment
pass: No link to an exfiltration or paste host
pass: No credential-shaped string
pass: No instruction to send local credentials anywhere
pass: No text hidden with inline styles
pass: No prompt-injection phrasing
pass: No curl or wget piped into a shell
pass: No recursive delete of root, home or parent
pass: No instruction to read or print local credentials
pass: No base64 blob over 200 characters
pass: No link to a raw IP address
pass: No script tag
Source
GitHub
etr/groundwork · 44 stars · license MIT · pushed 2026-10-30 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_owtj2grbxkmqx6md
GET https://markdownregistry.com/api/v1/resolve?ref=etr/groundwork/domain-modeling
GET https://markdownregistry.com/api/v1/blob/045da7f32a371b87f8fb2a4bcb6e65d8222cd7ad7cc57d274a2d9049283492aa
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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