ubiquitous-language
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- Author updated Jun 12, 2026, 08:25 AM
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- @mattpocock · no license declared
- Token usage
- Lean
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- Manual integration
- External API key
- Not required
- Operating systems
- Docker
- Runtime requirements
- Docker
- Permissions
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- Read-only
- Write / modify
- Shell exec
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools,默认拥有全部工具权限。
---
name: ubiquitous-language
description: Extract a DDD-style ubiquitous language glossary from the current conversation, flagging ambigui…
category: engineering
runtime: Docker
---
# ubiquitous-language output preview
## PART A: Task fit
- Use case: Extract a DDD-style ubiquitous language glossary from the current conversation, flagging ambiguities and proposing canonical terms. Saves to 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"..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Process / Output Format / Order lifecycle” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Extract a DDD-style ubiquitous language glossary from the current conversation, flagging ambiguities and proposing canonical terms. Saves to 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".”.
- **02** When the source has headings, the agent prioritizes “Process / Output Format / Order lifecycle” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files, run shell commands.
Start with a small task and check whether the result follows “Process / Output Format / Order lifecycle”. Inspect diffs, logs, previews, or tests before expanding scope.
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: ubiquitous-language
description: Extract a DDD-style ubiquitous language glossary from the current conversation, flagging ambigui…
category: engineering
source: mattpocock/skills
---
# ubiquitous-language
## When to use
- Extract a DDD-style ubiquitous language glossary from the current conversation, flagging ambiguities and proposing can…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “Process / Output Format / Order lifecycle” and keep inference separate from source facts.
- read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "ubiquitous-language" {
input -> user goal + target files + boundaries + acceptance criteria
context -> Process / Output Format / Order lifecycle
rules -> SKILL.md triggers / order / output contract
runtime -> Docker | read files, write/modify files, run shell commands | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
} Ubiquitous Language
Extract and formalize domain terminology from the current conversation into a consistent glossary, saved to a local file.
Process
- Scan the conversation for domain-relevant nouns, verbs, and concepts
- Identify problems:
- Same word used for different concepts (ambiguity)
- Different words used for the same concept (synonyms)
- Vague or overloaded terms
- Propose a canonical glossary with opinionated term choices
- Write to
UBIQUITOUS_LANGUAGE.mdin the working directory using the format below - Output a summary inline in the conversation
Output Format
Write a 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**.
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 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.
Example dialogue
Dev: "How do I test the sync service without Docker?"
Domain expert: "Provide the filesystem layer instead of the Docker layer. It implements the same Sandbox service interface but uses a local directory as the sandbox."
Dev: "So sync-in still creates a bundle and unpacks it?"
Domain expert: "Exactly. The sync service doesn't know which layer it's talking to. It calls
execandcopyIn— the filesystem layer just runs those as local shell commands."
Re-running
When invoked again in the same conversation:
- Read the existing
UBIQUITOUS_LANGUAGE.md - Incorporate any new terms from subsequent discussion
- Update definitions if understanding has evolved
- Re-flag any new ambiguities
- Rewrite the example dialogue to incorporate new terms
Decide Fit First
Design Intent
How To Use It
Boundaries And Review