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Domain

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boshu2
domain

Settle what domain terms mean per context, and which repository conventions or language standards (Go, Python) apply. Use when: names disagree or a rename is proposed.

Overview

Publisherboshu2
Repositoryagentops
Skill namedomain
Stars
450
Forks
42
Bundled files
18
LicenseApache-2.0
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.

  • 18 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by boshu2 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/boshu2/agentops/tree/main/plugin/skills/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/boshu2/agentops.git /tmp/agentops
mkdir -p .claude/skills
cp -r /tmp/agentops/plugin/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

Two independent uses: vocabulary work makes a term, name or rule boundary precise enough for acceptance examples, code and conversation; a standards lookup finds the language, data-format, risk or test-design standard for a change. Neither needs the other. Plan owns unified discovery and resumption; Domain resolves only the needed vocabulary or rule boundary and returns it to the existing intent.

Vocabulary work

  1. Cite the existing owner. Find the vocabulary owner in the caller's instructions, domain docs or contracts and read only the relevant terms. A lookup is read-only. With no owner, label each definition as observed in code or proposed.
  2. Keep one meaning per bounded context, the area where a term has one agreed meaning and an owner for its rules. When a word crosses contexts, name each meaning and the translation at the boundary instead of imposing one global definition.
  3. Report code that disagrees with intent. Identify the actor, state, operation and observable result the term denotes, then compare the intended meaning with callers, types and tests. A mismatch is a finding to resolve: renaming code does not add missing behavior, and redefining the term to match the code hides the defect.
  4. Price renames of shared names. Exported names, serialized fields, stored values and API payloads need compatibility work (migration, versioning, client updates), not a cosmetic replacement. Keep naming changes within authorized scope.
  5. Distinguish with an example. Separate competing meanings with a concrete case; express a branching boundary as Given/When/Then and reuse it in implementation and validation. Use the settled term in scenario names, operations, types and documentation. Ask only when an unresolved distinction would change behavior or ownership; a lookup needs no interview.
  6. Update the owner; add no glossary by default. When refinement is authorized, update the existing owner with the meaning, context and example. Admit a term only when a newcomer to the context would need it defined and it is a concept of its own, not a property of another entry or a general programming word. Each entry stands alone: one sentence of meaning, behavioral rules (lifecycle, ownership) only where they exist, and any project term it relies on defined as well. Record behavior, not today's values, dates, owners, status or links. Keep a useful alias from another context as an explicit translation; list a synonym retired within this context on the surviving entry as a word to avoid. Without an owner, return the proposal in the caller's intent or conversation rather than creating a new glossary file.
  7. Propose a decision record only when it earns one. When settling a boundary or rename becomes a design decision, propose an ADR only if it is costly to reverse, would puzzle a later reader without its context, and chose between real alternatives for stated reasons. Missing any of the three, the owner update and its example are enough. Use the repository's decision-record format when it has one; otherwise propose a short title and one to three sentences of context, choice and reason, adding status, rejected options or consequences only when a later reader needs them. Keep the vocabulary owner free of implementation detail; that belongs in the decision or code.

Return vocabulary work in this shape, then stop once the next change can be named and judged consistently:

TermContextMeaningSourceDistinguishing exampleOpen question
<path:line, observed in code, or proposed><Given/When/Then or concrete case><caller decision, or none>

After the table, list the translations between contexts, each located code-versus-intent disagreement, the compatibility cost of any proposed rename, and the owner updated or the text proposed for it. See caller vocabulary examples.

The synonym smuggling failure substitutes a word that changes a term's authority: calling a verdict a closure quietly assigns a tracker transition to judgment. Keep the original term when a substitute would move responsibility.

AgentOps terms

When AgentOps is the subject, its owners remain docs/contracts/ubiquitous-language.md and, for responsibilities and ports, docs/contracts/bounded-contexts.yaml. Return their exact definitions and source paths. Do not apply AgentOps vocabulary to an unrelated caller domain. The operations layer, federated integration graph, semantic work-and-proof protocol and RPI traversal retain their distinct meanings in the live contract. Queue, claim, lease, close, land, release and delivery remain caller-system responsibilities. Vocabulary edits do not authorize those transitions.

Standards lookup

Load only the language or risk guidance needed for the current change, starting from the common standards. Repository contracts and the actual toolchain take precedence. These references do not create a second approval or validation lane.

Idea provenance: Matt Pocock's engineering skills (domain modeling), adapted for AgentOps.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Domain AI skill do?

Settle what domain terms mean per context, and which repository conventions or language standards (Go, Python) apply. Use when: names disagree or a rename is proposed.

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/boshu2/agentops/tree/main/plugin/skills/domain. TypingMind reads its SKILL.md and bundles its files 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 boshu2 under the Apache-2.0 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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