ubiquitous-language▌
mattpocock/skills · updated Jun 10, 2026
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Extract and formalize domain terminology from the current conversation into a consistent glossary, saved to a local file.
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
How to use ubiquitous-language on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add ubiquitous-language
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches ubiquitous-language from GitHub repository mattpocock/skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate ubiquitous-language. Access the skill through slash commands (e.g., /ubiquitous-language) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★51 reviews- ★★★★★Pratham Ware· Dec 16, 2024
ubiquitous-language is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Diya Abebe· Dec 8, 2024
ubiquitous-language fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Maya Torres· Dec 4, 2024
Useful defaults in ubiquitous-language — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Sakura Robinson· Dec 4, 2024
Registry listing for ubiquitous-language matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Kiara Verma· Nov 27, 2024
Registry listing for ubiquitous-language matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Mia Menon· Nov 23, 2024
I recommend ubiquitous-language for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Ishan Jain· Nov 23, 2024
ubiquitous-language fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Sakshi Patil· Nov 7, 2024
Keeps context tight: ubiquitous-language is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Kiara Gonzalez· Nov 3, 2024
We added ubiquitous-language from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Chaitanya Patil· Oct 26, 2024
Registry listing for ubiquitous-language matched our evaluation — installs cleanly and behaves as described in the markdown.
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