Domain Modeling logo

Domain Modeling

CommunityPopular
rohitg00
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. Use at the start of a project or feature, or when the codebase and the people describing it speak different languages.

Overview

Publisherrohitg00
Repositorypro-workflow
Skill namedomain-modeling
Stars
2.9K
Forks
286
Bundled files
Instructions only
Links
Skill sourceRepositoryHomepage
  • 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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by rohitg00 on GitHub. Read the source before you install it.

Installation

Install the Domain Modeling 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.
  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/rohitg00/pro-workflow.git /tmp/pro-workflow
mkdir -p .claude/skills
cp -r /tmp/pro-workflow/skills/domain-modeling .claude/skills/domain-modeling
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Domain Modeling 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 Domain Modeling 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 Domain Modeling 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.

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.

Frequently asked questions

What does the Domain Modeling AI skill do?

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.

Why use Domain Modeling on TypingMind?

Because you install it once and use it with any model. Domain Modeling 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 Domain Modeling in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rohitg00/pro-workflow/tree/main/skills/domain-modeling. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Domain Modeling?

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 Domain Modeling?

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

Is the Domain Modeling AI skill free?

It is published on GitHub by rohitg00. 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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