ubiquitous-language

$ARGUMENTS

Extract and formalize domain terminology from the current conversation into a consistent glossary, saved to a local file.

Process

1. Scan the conversation for domain-relevant nouns, verbs, and concepts
2. Identify problems:
   • Same word used for different concepts (ambiguity)
   • Different words used for the same concept (synonyms)
   • Vague or overloaded terms
3. Propose a canonical glossary with opinionated term choices
4. Write to UBIQUITOUS_LANGUAGE.md in the working directory using the format below
5. Output a summary inline in the conversation

Output Format

Write a UBIQUITOUS_LANGUAGE.md file with this structure:

# Ubiquitous Language

## [Group 1]

| Term | Definition | Aliases to avoid |
| ---- | ---------- | ---------------- |
| **Term** | One-sentence definition | Other names to avoid |

## [Group 2]

| Term | Definition | Aliases to avoid |
| ---- | ---------- | ---------------- |
| **Term** | One-sentence definition | Other names to avoid |

## Relationships

- A **Thing** belongs to exactly one **Other Thing**
- An **X** produces one or more **Y**

## Example dialogue

> **Dev:** "..."
> **Domain expert:** "..."
> **Dev:** "..."
> **Domain expert:** "..."

## Flagged ambiguities

- "word" was used to mean both **Concept A** and **Concept B** — these are distinct because...

Rules

• Be opinionated. When multiple words exist for the same concept, pick the best one and list the others as aliases to avoid.
• Flag conflicts explicitly. If a term is used ambiguously in the conversation, call it out in the "Flagged ambiguities" section with a clear recommendation.
• Only include terms relevant for domain experts. Skip module or class names unless they have domain meaning.
• Keep definitions tight. One sentence max. Define what it IS, not what it does.
• Show relationships. Use bold term names and express cardinality where obvious.
• Only include domain terms. Skip generic programming concepts (array, function, endpoint) unless they have domain-specific meaning.
• Group terms into multiple tables when natural clusters emerge (e.g. by subdomain, lifecycle, or actor). Each group gets its own heading and table.
• Write an example dialogue. A short conversation (3-5 exchanges) between a dev and a domain expert that demonstrates how the terms interact naturally.