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domain-modeling

Build the project's shared language and bounded contexts before writing code, so names stay consistent and the agent stops paraphrasing domain concepts. Produces a CONTEXT.md glossary and decision records.

Install / Use

npx skills add rohitg00/pro-workflow --skill domain-modeling

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

81/100

Supported Platforms

Universal

Tags

Our assessment of domain-modeling

domain-modeling scores 81/100 on our quality scale, 891st of 1,160 Content & Media skills we index.

Its SKILL.md is 2.9 KB long, split into 5 sections and no code examples: a solid amount of guidance for an agent.

With 2,876 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
26/30
Structure
11/20
Description
15/15
Adoption
15/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 8 days ago, so domain-modeling is actively maintained.
  • No license is declared. By default that means all rights are reserved: you can read it, but reusing or redistributing it is not clearly permitted. Ask the author before building on it commercially.
  • Its trust signals score 88/100, with 1 caution from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

domain-modeling compared with similar skills

All 4 of these similar skills score higher than domain-modeling; compare them before choosing.

SkillScoreStarsUpdatedFormat
domain-modeling (this skill)by rohitg00812.9k8d agoSKILL.md
siyuanby siyuan-note10046.6ktodayMCP Server
algorithmic-artby anthropics100177.9k9d agoSKILL.md
pptxby anthropics100177.9k9d agoSKILL.md
designby nextlevelbuilder100130.2k11d agoSKILL.md

Frequently asked questions

How do I install domain-modeling?
Run npx skills add rohitg00/pro-workflow --skill domain-modeling. The install tabs above show the steps for each supported agent.
Which AI agents does domain-modeling work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is domain-modeling safe to use?
It declares no license and scores 88/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is domain-modeling still maintained?
The repository was last updated 8 days ago, so domain-modeling is actively maintained.

name: domain-modeling description: Build the project's shared language and bounded contexts before writing code, so names stay consistent and the agent stops paraphrasing domain concepts. Produces a CONTEXT.md glossary and decision records. Use at the start of a project or feature, or when the codebase and the people describing it speak different languages. user-invocable: true

domain-modeling

Most misbuilds start as a language gap: the agent is dropped into a project and left to infer the jargon, so it uses twenty words where the domain has one. A shared language closes the gap. When code, conversation, and the model all draw from the same vocabulary, names line up, navigation gets cheaper, and the model spends fewer tokens reasoning because it has a tighter language to reason in.

Method

  1. Harvest the terms. From the request, the codebase, and the user's own words, list the nouns and verbs that carry domain meaning - the concepts a newcomer would have to ask about. Prefer the user's word over a synonym you like better.
  2. Pin each one. Write a one-line definition in the project's own language, not a dictionary definition. If two terms blur together, force the distinction or collapse them - ambiguity here becomes inconsistent names in code.
  3. Draw the boundaries. Where the same word means different things in different parts of the system, that is a boundary. Name each context and note which terms belong to it. A term that means two things is two terms.
  4. Record the hard calls. When a modeling choice was contested or will be questioned later, write a short decision record: context, choice, alternatives rejected, why.

Outputs

  • CONTEXT.md - the shared-language glossary. One term per line: term - what it means in this project. Grouped by bounded context when there is more than one. Point every future session at this file. On re-run, add new terms and update definitions that changed; do not rewrite the file wholesale.
  • Bounded-context sketch - the contexts and which terms live in each, short enough to read in fifteen seconds.
  • Decision records in docs/decisions/NNNN-slug.md for the contested modeling calls only. Read the directory first and number from the highest existing record so two records never collide. Skip the obvious ones.

Guardrails

  • The glossary is for the model as much as the human - write it to be loaded, not framed on a wall.
  • Do not invent terms the project does not use. Reflect the domain; do not rename it.
  • Keep it small and current. A glossary that lists everything and updates nothing is worse than none. Prune terms that fall out of use.

Where it fits

Run this before plan-interrogate on a new area, or let plan-interrogate call back here when it hits terms it cannot pin. The CONTEXT.md this produces is the same file plan-interrogate emits - one shared-language artifact, two ways in.

Related Skills

View on GitHub
GitHub Stars2.9k
CategoryContent
Updated8d ago
Forks289

Languages

JavaScript

Trust signals

88/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

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