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Ubiquitous Language

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citypaul
ubiquitous-language

One ubiquitous language per bounded context, with a repository-declared glossary as the naming authority and explicit decisions for language changes. Covers the five-step language protocol (detect, propose, decide, record, rename), optional glossary-driven lint ratchets, and safe context-wide renames. Use when naming domain concepts, when a term is missing from or fights the glossary, when spotting synonyms or near-duplicates, when bootstrapping a glossary, or when renaming across a bounded context. For DDD building blocks see domain-driven-design; for acceptance-DSL vocabulary use the project's relevant installed skill.

Overview

Publishercitypaul
Repository.dotfiles
Skill nameubiquitous-language
Stars
740
Forks
96
Bundled files
2
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by citypaul on GitHub. Read the source before you install it.

Installation

Install the Ubiquitous Language AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/citypaul/.dotfiles/tree/main/claude/.claude/skills/ubiquitous-language
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/citypaul/.dotfiles.git /tmp/.dotfiles
mkdir -p .claude/skills
cp -r /tmp/.dotfiles/claude/.claude/skills/ubiquitous-language .claude/skills/ubiquitous-language
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ubiquitous Language in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ubiquitous Language on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ubiquitous Language is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Ubiquitous Language: Evolution by Decision, Never Drift

A change in the language is a change to the model (Evans). One ubiquitous language per bounded context — apply a single language to a whole enterprise and you will fail (Vernon). This skill makes the repository's declared glossary the naming authority. The language is expected to evolve, but only through an explicit, recorded decision.

The documented agent failure mode this kills: terminology drift — processUserData vs processUserInfo near-duplicates, "booking" quietly becoming "reservation" mid-feature. Prompt-level glossaries steer; deterministic gates enforce.

ResourceLoad when...
resources/language-protocol.mdA term is missing, awkward, or duplicated — running the five steps and recording model-changing decisions
resources/glossary-format.mdCreating or editing a glossary — repository schema, optional Contextive example, context scoping, aliases, bootstrap paths

Where the Language Lives

  1. The declared glossary is the naming authority for its bounded context. Canonical spelling and capitalization matter whenever the concept is named. Use the repository's chosen file location and schema; do not impose a different glossary format.
  2. Domain code and executable examples consume and exercise that language. Identifiers, branded types, DSL verbs, and test titles should use the canonical terms.
  3. The rest of the maintained system propagates approved terms through schemas, APIs, documentation, diagrams, fixtures, and generated clients. Public wire vocabulary may need deliberate versioning even after an internal rename.
  4. Speech, plans, research, comments, historical documents, and existing code expose candidate vocabulary and possible drift. They are evidence for a proposal, not authority that can silently canonize a term.

Absence from the glossary means not yet agreed. It does not automatically approve or forbid a word.

Working names must be visibly provisional and name how they will be ratified or replaced. Do not let a placeholder spread as if it were approved language.

The Language Protocol (the centrepiece)

Silent evolution is forbidden. Every new, changed, or suspect domain term runs five steps — full guidance in resources/language-protocol.md:

  1. DETECT — you need a term the glossary lacks; you spot a synonym or near-duplicate (one word per concept; two phrases must mean two concepts); a term feels awkward in conversation; or a targeted identifier lint flags an unknown domain word. A missing term is not yet agreed.
  2. PROPOSE — never adopt. STOP and present to the human: the term, a proposed definition, the bounded context, an example sentence, and the alternatives considered. Use the host's structured question mechanism when available; otherwise ask one concise plain-text question with a recommendation. An agent never coins a domain term silently.
  3. DECIDE — the human approves, renames, or rejects. Cheap because it's one term at a time with a recommendation.
  4. RECORD — glossary entry added or updated in the same reviewable change; rejected or replaced aliases point to the owning bounded context and canonical term. Renames that change the model, not just a label, use the repository's decision-record mechanism; routine additions need only the glossary diff.
  5. RENAME — in that order: decision first, then refactor code, tests, and glossary together in one reviewable change. Boundary rule: rename freely inside the bounded context; the published language at context boundaries — APIs, events, schemas — is versioned deliberately, never casually renamed.

Mechanical Enforcement

Where a mechanical enforcement layer is installed (lint rules generated from the glossary), it runs inside the agent's loop, scoped to domain paths — and enforces:

  • Project-declared vocabulary smells — a repository may choose to flag vague suffixes such as Manager, Helper, or Impl in selected domain paths. Treat them as prompts for review, never universal bans: a canonical glossary term overrides a generic smell list.
  • Glossary-driven identifier check (the flagship, novel-in-TypeScript rule) — split identifiers into words; validate domain words against the context's glossary using token classes: glossary terms / deprecated aliases (flagged with their replacement) / path-scoped technical stopwords / exempt framework identifiers.
  • Test-title check — titles validated against the glossary; deprecated aliases rejected naming the canonical replacement.
  • DSL vocabulary check — a new acceptance-DSL verb either exists in the glossary or triggers the protocol.
  • Context-scoped aliases and forms — validate aliases within their owning context; enforce canonical case, plurals, identifier forms, and public wire names where the glossary declares them.
  • Language scope only — delegate architecture and dependency enforcement to domain-driven-design, hexagonal-architecture, and structure-codebase; do not smuggle structural policy into a vocabulary lint.

Every rule teaches: three-part messages — what fired, why the rule exists, what to do instead, with a skill pointer. A gate with no exit invites bypass, so the documented-exception process (justified in the PR, recorded) stays open.

Enforcement status: until a generated lint layer or equivalent gate is installed in the project, everything here is convention — say so; never imply protection that isn't there. Even when the command is green, report its configured paths and token classes: a targeted ratchet is not proof that every prose passage and identifier uses the ubiquitous language correctly.

Every language guard needs a positive control: a fixture or focused test that introduces known-forbidden vocabulary and proves the guard fails with the owning context and canonical replacement. Scanning the current tree and finding nothing proves only that the scanner completed.

The Adoption Spectrum

  • Greenfield — start with the glossary and the smallest useful positive-control guard; add enforcement only where it catches a demonstrated drift risk.
  • Brownfield — the protected-core ratchet. A rule that fires thousands of times on day one will be bypassed. Start with one capability or bounded context, enforce its domain/application boundary, and expand as work touches adjacent behavior. Pre-existing violations may freeze in a baseline whose counter only goes down. The glossary can roll out warn-first and diff-scoped, with its limited scope stated explicitly.
  • Spikes — treat vocabulary as provisional and route it through the protocol before promotion into maintained code. Follow the repository's existing spike or experimental-code policy rather than inventing a global marker.

Boundaries

SituationSkill
Value objects, entities, aggregates, bounded contextsdomain-driven-design
Ports/adapters structure the arch rules enforcehexagonal-architecture
DSL and test-title vocabulary in the outer loopThe project's acceptance/specification skill
Recording a model-changing renameThe repository's decision-record mechanism
Naming questions during discoveryThe host's available question mechanism

Verification Checklist

  • One repository-declared glossary authority per bounded context
  • The repository's declared glossary format and location are preserved
  • Canonical spelling and capitalization are used when naming declared concepts
  • Missing terms are treated as not yet agreed
  • No domain term coined without the five-step protocol — proposals presented, never adopted silently
  • Glossary diff rides the same reviewable change as the code that introduces the vocabulary
  • Replaced terms recorded as deprecated aliases with replacements
  • Rejected aliases point to the owning bounded context and canonical term
  • Model-changing renames use the repository's decision-record mechanism
  • Published language at context boundaries versioned, never casually renamed
  • Approved terms propagated through code, types, schemas, APIs, tests, docs, diagrams, fixtures, and generated clients in scope
  • Working names are visibly provisional with a ratification or replacement path
  • Alias and word-form checks are scoped to their owning bounded context
  • Lint rules carry three-part teaching messages
  • Green lint results are reported as targeted ratchets, not whole-repository language proof
  • Positive controls prove forbidden vocabulary is detected
  • Brownfield enforcement is scoped, its baseline does not grow silently, and its limits are reported
  • Provisional spike vocabulary is ratified or replaced before promotion

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ubiquitous Language AI skill do?

One ubiquitous language per bounded context, with a repository-declared glossary as the naming authority and explicit decisions for language changes. Covers the five-step language protocol (detect, propose, decide, record, rename), optional glossary-driven lint ratchets, and safe context-wide renames. Use when naming domain concepts, when a term is missing from or fights the glossary, when spotting synonyms or near-duplicates, when bootstrapping a glossary, or when renaming across a bounded context. For DDD building blocks see domain-driven-design; for acceptance-DSL vocabulary use the proj...

Why use Ubiquitous Language on TypingMind?

Because you install it once and use it with any model. Ubiquitous Language is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ubiquitous Language in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/citypaul/.dotfiles/tree/main/claude/.claude/skills/ubiquitous-language. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ubiquitous Language?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ubiquitous Language?

As many as you like. As long as a model supports skills, you can use Ubiquitous Language with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ubiquitous Language AI skill free?

It is published on GitHub by citypaul. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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