ubiquitous-language

name: ubiquitous-language description: Use when evaluating the ubiquitous language in a codebase - produces a glossary of domain terms with references and commentary on inconsistencies, awkward names, or overlapping concepts

Ubiquitous Language Review

Overview

The ubiquitous language is the shared vocabulary a team uses for domain concepts — in conversation, code, tests, and docs. When the language is consistent, the code communicates clearly. When it drifts, misunderstandings hide in plain sight.

This skill produces a docs/terms.md glossary and a commentary on the health of the language.

When to Use

• Onboarding to a codebase — to understand the domain
• After significant refactoring or feature work
• When naming debates keep recurring
• When new team members are confused by terms
• Periodically, as a hygiene check

Process

1. Harvest Terms

Scan the codebase for domain terms. Focus on these layers, in order of authority:

Reading Feature Files

Feature files deserve special attention. They are scenarios written in natural language — the closest thing to how the team talks about the domain. Read them carefully, extracting terms from:

• Rule names — these state business rules and often name domain distinctions that have no type in the code. A rule like "reserved fields cannot be overwritten" reveals the concept "reserved field" even though the code only has constants and a validation function.
• Scenario names — name specific instances and may reveal subcategories of a concept.
• Given/When/Then step phrasing — the steps are the ubiquitous language in sentence form. Terms that appear in steps but have no type in the code are candidates for missing abstractions.
• Feature-level descriptions — often name high-level concepts or provide context for why a group of rules exists.

Also ask: "For each noun in the glossary, are there named subcategories?" Many domain concepts have subtypes that aren't in the type system but live in conventions, validation rules, or feature file Rules. "Reserved field" vs "custom field" is a classification within the Field concept. These boundaries matter.

Look for "negative space" terms — concepts the team uses in conversation that have no name in the code. If a feature file says "reserved fields" but the code has no ReservedField type or even a comment using that phrase, that's a gap worth flagging.

Judging Feature File Quality

Feature files should use declarative style — they describe what the system does using domain language, not how the user interacts with the UI or implementation.

Declarative (good): Steps express business intent.

Given a yak "make the tea" with state "done"
When I prune
Then "make the tea" should be removed

Imperative (bad): Steps spell out mechanics.

Given I run "yx add make the tea"
And I run "yx state make the tea done"
When I run "yx prune"
Then I run "yx ls" and the output does not contain
  "make the tea"

Signs of imperative Gherkin:

When reviewing feature files for this glossary, flag:

• Steps that use implementation language instead of domain language — these are missed opportunities to exercise the ubiquitous language.
• Rules or scenarios that reveal domain concepts not reflected in the code's type system.
• Steps that could name a domain concept but instead describe a mechanism.

The test is: "Would this wording need to change if the implementation changed?" If yes, the step is imperative and the domain language is being bypassed.

Separate nouns and verbs. Domain nouns are the things the system talks about. Domain verbs are the operations performed on them. This separation matters because nouns tend to have multiple representations (structs, views, snapshots) while verbs tend to have multiple expressions (CLI commands, use cases, events).

For each term, record:

• Term: The canonical name
• Definition: What it means in this domain (one sentence)
• References: Where it appears (files, with line numbers for definitions; just filenames for usage)
• Aliases: Any synonyms or variations found

2. Identify Issues

Look for these specific problems:

Synonyms

Different words for the same concept. Example: "task" in docs but "yak" in code. Pick one. Synonyms cause people to wonder whether the two words mean subtly different things.

Homonyms

Same word meaning different things in different contexts. Example: "state" meaning both "yak lifecycle state" and "application state". Flag these even if the context usually disambiguates — they trip up newcomers.

Technical Leak

Implementation terms in the domain layer. The domain model should speak the language of the problem, not the solution. Examples: "aggregate" in a CLI message, "projection" in a feature file, "store" where "repository" or a domain term would be clearer.

Awkward Names

Terms that don't convey their meaning without explanation. A name is awkward if you'd need a comment or a conversation to understand it. Abbreviations, inside jokes, and legacy names that no longer match the concept all qualify.

Missing Terms

Places where the code uses generic language ("item", "thing", "data", "info") instead of a domain-specific term. Also look for concepts that exist in conversation but have no name in the code.

Inconsistent Granularity

Some terms are very specific while related terms are vague. Example: detailed lifecycle states (todo, wip, done) alongside a vague update event that covers many things.

Imperative Steps in Feature Files

Gherkin steps that describe how (shell commands, output parsing, UI mechanics) instead of what (domain operations and outcomes). Imperative steps bypass the ubiquitous language — they test the implementation without exercising the vocabulary. Flag specific steps and suggest declarative rewrites using domain terms.

3. Inventory Every Name Honestly

For each domain concept, list every type, struct, enum, or alias in the code that represents it. Do not gloss over the fact that one concept may have multiple representations. Name each one, say where it lives, explain why it exists, and note what it relates to.

For example, if the concept "Yak" appears as YakView, YakEntry, YakSnapshot, YakTreeNode, and YakChildView, list all five. Explain: YakView is the read-model DTO, YakEntry is the aggregate's internal state, YakSnapshot is used for compaction, etc. This is the inventory — no smoothing over, no "the code calls this a Yak".

Ask for each representation:

• Why does this one exist separately?
• What would break if it were merged with another?
• Does its name communicate its role clearly?
• Could a newcomer tell it apart from the others?

4. Write the Glossary

Create or update docs/terms.md with this structure:

# Ubiquitous Language — Glossary

> This glossary defines the shared vocabulary for this
> project. When naming things in code, tests, CLI, or
> docs, use these terms consistently.
>
> Last reviewed: YYYY-MM-DD

## Nouns

### Term Name

Definition in one sentence.

**Representations in code:**

| Type | File | Role |
|------|------|------|
| `TypeName` | `path/to/file.rs:42` | Why this one exists |
| `OtherType` | `path/to/other.rs:10` | Why this one exists |

- **Used in**: `features/foo.feature`, `docs/bar.md`
- **Related to**: Other Term, Another Term
- **Aliases**: (list any synonyms — flag if problematic)

## Verbs

### Term Name

Definition in one sentence.

| Expression | Where | Form |
|------------|-------|------|
| `yx command` | CLI | User-facing command |
| `UseCaseName` | `src/application/file.rs:1` | Use case |
| `EventName` | `src/domain/event.rs:14` | Domain event |

- **Used in**: `features/foo.feature`
- **Related to**: Other Term

## Commentary

### Strengths
What the codebase does well with its language.

### Issues Found

#### Issue Title
- **Type**: Synonym / Homonym / Technical Leak / Awkward
  Name / Missing Term / Inconsistent Granularity
- **Where**: files and lines
- **Problem**: what's wrong
- **Suggestion**: how to fix it

5. Review Criteria

A healthy ubiquitous language has these properties:

What This Skill Does NOT Do

• It does not rename anything. It produces a glossary and commentary. Renaming is a separate decision.
• It does not judge architectural choices. "Event sourcing" is a valid implementation term in infrastructure — the issue is only when it leaks into domain language.
• It does not require perfection. Every codebase has some inconsistency. The goal is awareness, not zero issues.

Sources

• Eric Evans: "Domain-Driven Design" (2003), Chapter 2
• Vaughn Vernon: "Implementing Domain-Driven Design" (2013)
• Alberto Brandolini: "Introducing EventStorming" — the shared language emerges from collaborative modelling
• "Declarative vs Imperative Gherkin Scenarios" (itsadeliverything.com) — why steps should express business intent, not implementation mechanics
• Cucumber docs: "Writing Better Gherkin" (cucumber.io/docs/bdd/better-gherkin/) — the litmus test: "will this wording need to change if the implementation does?"