ubiquitous-language

name	ubiquitous-language
description	Extracts a DDD-style ubiquitous language glossary from the current conversation, flagging ambiguities and proposing canonical terms. Saves to docs/UBIQUITOUS_LANGUAGE.md. Use when user wants to define domain terms, build a glossary, harden terminology, create a ubiquitous language, or mentions "domain model" or "DDD".

Ubiquitous Language

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

Workflow

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 docs/UBIQUITOUS_LANGUAGE.md (create the docs/ directory if it doesn't exist) using the format below
5. Output a summary inline in the conversation

Output Format

Write a docs/UBIQUITOUS_LANGUAGE.md file with this structure:

# Ubiquitous Language

## Order lifecycle

| Term        | Definition                                              | Aliases to avoid      |
| ----------- | ------------------------------------------------------- | --------------------- |
| **Order**   | A customer's request to purchase one or more items      | Purchase, transaction |
| **Invoice** | A request for payment sent to a customer after delivery | Bill, payment request |

## People

| Term         | Definition                                  | Aliases to avoid       |
| ------------ | ------------------------------------------- | ---------------------- |
| **Customer** | A person or organization that places orders | Client, buyer, account |
| **User**     | An authentication identity in the system    | Login, account         |

## Relationships

- An **Invoice** belongs to exactly one **Customer**
- An **Order** produces one or more **Invoices**

## Example dialogue

> **Dev:** "When a **Customer** places an **Order**, do we create the **Invoice** immediately?"
> **Domain expert:** "No — an **Invoice** is only generated once a **Fulfillment** is confirmed. A single **Order** can produce multiple **Invoices** if items ship in separate **Shipments**."
> **Dev:** "So if a **Shipment** is cancelled before dispatch, no **Invoice** exists for it?"
> **Domain expert:** "Exactly. The **Invoice** lifecycle is tied to the **Fulfillment**, not the **Order**."

## Flagged ambiguities

- "account" was used to mean both **Customer** and **User** — these are distinct concepts: a **Customer** places orders, while a **User** is an authentication identity that may or may not represent a **Customer**.

Constraints

• 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 the names of modules or classes unless they have meaning in the domain language.
• 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. If all terms belong to a single cohesive domain, one table is fine — don't force groupings.
• Write an example dialogue. A short conversation (3–5 exchanges) between a dev and a domain expert that demonstrates how the terms interact naturally. The dialogue should clarify boundaries between related concepts and show terms being used precisely.

Re-running

When invoked again in the same conversation:

1. Read the existing docs/UBIQUITOUS_LANGUAGE.md
2. Incorporate any new terms from subsequent discussion
3. Update definitions if understanding has evolved
4. Re-flag any new ambiguities
5. Rewrite the example dialogue to incorporate new terms

Quality

• Once the glossary file is written, run the shared self-review checklist on it. Surface issues in the chat only if found.