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Architecture Ownership

Organization
instructa
architecture-ownership

Determine runtime owner, first-fix layer, and canonical long-term module or package owner in layered codebases. Use when placing code across UI vs platform shell vs runtime orchestration vs domain or application vs shared core vs adapter or integration layers, debugging ownership issues, removing duplicate policy paths, or answering "where should this live?" architecture questions.

Overview

Publisherinstructa
Repositoryagent-skills
Skill namearchitecture-ownership
Stars
141
Forks
16
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Architecture Ownership 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/instructa/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/skills/engineering/architecture-ownership .claude/skills/architecture-ownership
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Architecture Ownership 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 Architecture Ownership 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 Architecture Ownership 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.

Architecture Ownership

Use this skill for repo-specific ownership and placement decisions in layered systems.

Focus on long-term canonical ownership, not only the layer where the current bug appears.

Required Discovery

Before deciding ownership:

  • Read the repo docs that define architecture, boundaries, or responsibilities.
  • Read ADRs or design docs that define runtime flow, request flow, or package boundaries.
  • Inspect the top-level project structure to map the repo's concrete module or package names onto the generic layers in this skill.

When docs are incomplete:

  • infer the current layer model from the codebase
  • state the assumption explicitly
  • keep the recommendation aligned to one canonical owner

Required Output

When answering an ownership or placement question, explicitly separate:

  • Runtime owner
  • First fix owner
  • Canonical long-term owner
  • Competing owners that are wrong
  • Cleanup direction

Do not collapse these into a single answer.

Decision Order

  1. Identify the runtime concern:
    • visible UI state
    • platform shell or OS bridge
    • runtime composition or request dispatch
    • canonical domain or application workflow
    • pure shared logic
    • concrete adapter or integration behavior
  2. Name the layer where the wrong behavior currently happens.
  3. Decide whether that layer is only the First fix owner or also the Canonical long-term owner.
  4. If the behavior is reusable product or business policy, move the long-term owner out of the runtime orchestration layer.
  5. Remove duplicate policy, fallback logic, or dual paths once the canonical owner is clear.

Layer Map

  • UI layer
    • owns visible UI state, navigation, rendering, presentation-oriented derived state, and view composition
  • Platform shell
    • owns native shell concerns, OS bridges, local device integrations, filesystem or permission bridges, process or session plumbing, and platform-local persistence helpers
  • Runtime orchestration layer
    • owns runtime composition, request dispatch, background coordination, worker management, event publication, and process-level orchestration
    • this is often the First fix owner
    • this is not automatically the Canonical long-term owner
  • Domain or application layer
    • owns canonical product workflow, business rules, reusable application services, and cross-interface behavior
  • Shared core layer
    • owns shared types, enums, validation, normalization, capability logic, and pure logic used across runtimes
  • Adapter or integration layer
    • owns concrete protocol, provider, vendor, transport, or API integration behavior
    • does not own cross-provider or cross-integration product policy

Translate these generic layers into the repo's actual module, package, crate, or service names before making a recommendation.

Hard-Cut Rules

  • Do not leave reusable domain policy in the runtime orchestration layer just because the wrong behavior currently happens there.
  • Do not put UI state, layout state, restore state, or user-facing presentation ownership in the runtime orchestration layer.
  • Do not put platform shell or native integration concerns in the domain or application layer.
  • Do not put cross-provider or cross-vendor product policy in adapter or integration layers.
  • Do not put pure validation, normalization, or capability logic in orchestration code when it can live in shared core or a narrow domain module.

Common Judgments

  • If the question is "who should decide this for all future interfaces?" the answer is usually the domain or application layer, a narrow domain package, or shared core, not the runtime orchestration layer.
  • If the question is "where do I patch this bug first so the product stops doing the wrong thing?" the answer may still be the runtime orchestration layer.
  • If the question is "who owns the wire shape or payload type?" the answer is usually the shared API or core type layer, not the runtime orchestration layer.
  • If the question is "who owns vendor-specific behavior?" the answer is the relevant adapter or integration layer, but only for adapter behavior, not canonical product policy.

Example Pattern

Example: a planning policy bug currently lives in a runtime runner module.

  • Runtime owner: runtime orchestration layer
  • First fix owner: runtime orchestration layer
  • Canonical long-term owner: domain or application layer
  • Type or capability owner: shared core layer
  • Competing owners that are wrong: UI layer, platform shell, adapter or integration layers

Additional Resource

For a reusable classification matrix and example splits, see references/ownership-matrix.md.

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 Architecture Ownership AI skill do?

Determine runtime owner, first-fix layer, and canonical long-term module or package owner in layered codebases. Use when placing code across UI vs platform shell vs runtime orchestration vs domain or application vs shared core vs adapter or integration layers, debugging ownership issues, removing duplicate policy paths, or answering "where should this live?" architecture questions.

Why use Architecture Ownership on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/instructa/agent-skills/tree/main/skills/engineering/architecture-ownership. 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 Architecture Ownership?

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 Architecture Ownership?

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

Is the Architecture Ownership AI skill free?

It is published on GitHub by instructa. 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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