ubiquitous-language
Skill246 starsChanged 46 days agoApache-2.0View on GitHub
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".
We split this skill into the rules it gives an agent
8 rules. 4 are common, 2 are distinctive, and 2 are very specific to this project.
4
Common
Most of the 29 edited versions we read keep these.
2
Distinctive
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 24
Write a UBIQUITOUS_LANGUAGE.md file with this structure:
- Common: line 62
Be opinionated: When multiple words exist for the same concept, pick the best one and list the others as aliases to avoid.
- Common: line 63
Flag conflicts explicitly: If a term is used ambiguously in the conversation, call it out in the "Flagged ambiguities" section with a clear …
- Common: line 66
Show relationships: Use bold term names and express cardinality where obvious.
- Distinctive: line 68
Group terms into multiple tables: when natural clusters emerge (e.g.
- Common: line 87
When invoked again in the same conversation:
- Very specific: line 89
Read the existing UBIQUITOUS_LANGUAGE.md
- Distinctive: line 91
Update definitions if understanding has evolved
“Common” is measured: kept by at least 6 in 10 of the 29 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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>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."
</example>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
Other versions64 projects use a version of this.
Word for word · 31
- amazingloft999-droid/mattpocock-skills★ 422100% same
- loulanyue/skills★ 231100% same
- aurorasoft2/Matt_Pocock_Skills★ 133100% same
- VisualxIntelligence/mattpocok-skills★ 55100% same
- jan-bogaerts/md2★ 39100% same
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- GAGAA-HUE/ASHUO-claude-skills★ 5100% same
- Roentek/Claude_Code_Boilerplate_Framework★ 4100% same
- 47thtechcorner/RayCodes_Mattpocock_Skills★ 2100% same
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21 more not listed
Edited · 21
- karlomikus/bar-assistant★ 1.1k99% same
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- Future-CX/AI-Architecture-Toolkit★ 833% same
- Boom-Vitt/skills-thai★ 525% same
- chrislacey89/skills★ 418% same
- galsapir/skills★ 494% same
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- kayaman/skills★ 381% same
- witi-projects/Skills★ 297% same
- charlesmatte/claude_code_skills★ 189% same
11 more not listed
Wrote their own version · 12
- CodeAlive-AI/ai-driven-development★ 167same name
- jscraik/Agent-Skills★ 9same name
- mattwynne/skills★ 6same name
- aiqadam/qadam-flow★ 5same name
- amit-t/skills★ 4same name
- conjurer-rich/skills★ 1same name
- DROOdotFOO/agent-skills★ 1same name
- jpoindexter/theft-kit★ 1same name
- Sturla22/skills★ 1same name
- jorgenschaefer/skills★ 0same name
2 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
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