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Domain

Organization
AgentiveStack
domain

Cook a plan against the project's domain model. Stress-tests terminology, surfaces contradictions with code, and updates CONTEXT.md, UBIQUITOUS_LANGUAGE.md, and ADRs inline as decisions crystallize. Use when planning a feature, refactoring, or any work that touches domain boundaries.

Overview

PublisherAgentiveStack
Repositoryskills
Skill namedomain
Stars
72
Forks
17
Bundled files
Instructions only
LicenseMIT
Links
  • 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 AgentiveStack on GitHub. Read the source before you install it.

Installation

Install the Domain 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.
    https://github.com/AgentiveStack/skills/tree/main/domain
  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/AgentiveStack/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/domain .claude/skills/domain
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Domain 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 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 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 Model Cooking Session

Interview relentlessly about every aspect of this plan until we reach a shared understanding. Walk down each branch of the design tree, resolving dependencies between decisions one-by-one. For each question, provide your recommended answer.

Ask questions one at a time, waiting for feedback before continuing.

If a question can be answered by exploring the codebase, explore the codebase instead of asking.

Discover project context

Before starting, find and read existing domain documentation. Check these locations (in order):

  1. docs/CONTEXT_MAP.md — if it exists, this is a multi-context project. Read it to understand bounded contexts and their relationships.
  2. docs/UBIQUITOUS_LANGUAGE.md — the shared glossary. Read it to understand canonical terms.
  3. docs/contexts/*/CONTEXT.md — per-context documentation. Read the ones relevant to the current plan.
  4. CLAUDE.md — project-level context.

If none of these exist, that's fine — create them lazily as decisions are made (see below).

During the session

Identify which context(s) this plan touches

If a context map exists, determine which bounded context(s) the plan falls within. If the plan spans multiple contexts, call that out explicitly — cross-context work needs extra care around contracts and shared kernels.

Challenge against the glossary

When the user uses a term that conflicts with the ubiquitous language, call it out immediately:

"Your glossary defines 'change' as a persisted set of DOM mutations (ChangeSpec). You're using 'change' to mean something different here — which meaning do you intend?"

Sharpen fuzzy language

When the user uses vague or overloaded terms, propose a precise canonical term:

"You're saying 'user data' — do you mean the Visitor (anonymous deviceId), the Profile (identified via identify()), or the Member (org membership)? Those are three different concepts."

Discuss concrete scenarios

When domain relationships are discussed, stress-test them with specific scenarios. Invent edge cases that probe boundaries:

"What happens when an experiment is running on /pricing and someone deploys a change spec to the same page? Do they conflict? Which takes priority?"

Cross-reference with code

When the user states how something works, verify against the code. If you find a contradiction, surface it:

"You said goals are hard-deleted, but the code sets isActive = false — which is the intended behavior?"

Update documentation inline

When a term is resolved or a decision is made, update the relevant file immediately. Don't batch these up.

For terminology changes:

  • Update docs/UBIQUITOUS_LANGUAGE.md with the new or refined term
  • Update the relevant docs/contexts/<name>/CONTEXT.md if the term is context-specific

For domain model changes:

  • Update the relevant CONTEXT.md with new invariants, contracts, or ownership changes
  • Update docs/CONTEXT_MAP.md if relationships between contexts changed

For decisions:

  • Only create an ADR when ALL THREE conditions are met:
    1. Hard to reverse — the cost of changing your mind later is meaningful
    2. Surprising without context — a future reader will wonder "why did they do it this way?"
    3. Result of a real trade-off — there were genuine alternatives and you picked one for specific reasons
  • ADRs go in docs/contexts/<name>/adrs/ (context-specific) or docs/adrs/ (cross-context)
  • Use sequential numbering: 001-slug.md
  • Keep ADRs minimal: title + 1-3 sentences (context, decision, why). Optional sections (considered options, consequences) only when they add genuine value.

Lazy file creation

If documentation files don't exist yet, create them only when you have something to write:

  • First term resolved → create UBIQUITOUS_LANGUAGE.md
  • First context-specific decision → create that context's CONTEXT.md
  • First cross-context relationship → create CONTEXT_MAP.md
  • First ADR-worthy decision → create the adrs/ directory and first ADR

Related skills

  • /spec — after cooking, formalize the plan into a spec
  • /holistic — zoom out to understand how this plan fits the bigger picture
  • /architect — surface architectural friction and design deep module interfaces

Frequently asked questions

What does the Domain AI skill do?

Cook a plan against the project's domain model. Stress-tests terminology, surfaces contradictions with code, and updates CONTEXT.md, UBIQUITOUS_LANGUAGE.md, and ADRs inline as decisions crystallize. Use when planning a feature, refactoring, or any work that touches domain boundaries.

Why use Domain on TypingMind?

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

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

Which AI models can use Domain?

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?

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

Is the Domain AI skill free?

Yes. It is published on GitHub by AgentiveStack under the MIT license. 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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