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Architecture

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owainlewis
architecture

Designs and maintains root ARCHITECTURE.md for the intended system, including its data model and shared technical rules. Use for new systems or changes to system boundaries, ownership, storage, or deployment.

Overview

Publisherowainlewis
Repositoryblueprint
Skill namearchitecture
Stars
419
Forks
73
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Architecture 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/owainlewis/blueprint/tree/main/skills/architecture
  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/owainlewis/blueprint.git /tmp/blueprint
mkdir -p .claude/skills
cp -r /tmp/blueprint/skills/architecture .claude/skills/architecture
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Architecture 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 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 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

Create or update root ARCHITECTURE.md. Design the system we intend to build. Keep this as a long-running system document alongside REQUIREMENTS.md. Use /requirements for product needs and /spec for a feature's technical change.

Process

  1. Read the requirements, repository instructions, existing architecture, and relevant code, schemas, tests, and infrastructure. A new system may have no code yet.
  2. Identify the decisions that shape the system: boundaries, responsibilities, data ownership, dependencies, and quality constraints. Ask for missing consequential decisions; recommend an answer with its tradeoff when evidence supports one.
  3. Choose the simplest structure that satisfies the requirements. For consequential decisions, explain the chosen approach, strongest alternative, reason, and main cost. Consider keeping the existing system when relevant. Skip comparisons that cannot change the choice.
  4. Write the architecture using the shape below. Always include an architecture and data flow diagram. Reference requirements instead of copying them.
  5. Trace critical success and failure flows. Check ownership, contracts, data consistency, security, recovery, and measurable limits against the requirements.
  6. Stop with the architecture ready for review. Do not plan tasks or implement it.

Document shape

  • Summary and goals: the system, its main parts, source of truth, and the requirements driving the design.
  • System boundary: users, outside systems, components, and important interfaces. Include the required architecture and data flow diagram.
  • Responsibilities and dependencies: what each part owns, what it does not own, and which way dependencies may point.
  • Data model: core entities, relationships, identifiers, ownership, lifecycle, constraints, consistency rules, retention, and storage choices. Include an entity diagram when useful. Link larger schemas rather than repeating them.
  • Runtime flows: important execution order, data movement, transactions, side effects, failures, retries, and recovery.
  • Security and operations: trust boundaries, authorization, deployment, observability, resource limits, and operator recovery where they affect the design.
  • Operating expectations: expected workload, latency, availability, recovery, and cost constraints where they shape the design. Use supported targets; record consequential unknowns and their effects instead of inventing numbers. Link requirements for product limits.
  • Path to the intended system: for an existing system, explain important gaps, compatibility constraints, migration, and rollback where needed. Keep feature details in specs and delivery tasks in /plan.
  • Decisions and status: mark decisions proposed or accepted. Separately state what is implemented, missing, or different in the current system. Acceptance does not mean implementation.
  • Risks and proof: unresolved decisions, assumptions, quality scenarios, and checks needed to validate the design.

Diagram

Always include a Mermaid diagram showing the main components, external actors, data stores, and data flow. A single diagram may cover architecture and flow; use a second when combining them would make either hard to read.

Use clear labels, labeled arrows, and groups for system or trust boundaries. Show what moves between parts, not just which parts connect. Keep spacing generous and the layout easy to follow. Use a restrained, consistent palette with readable contrast. Avoid crossing arrows and decorative detail. Split crowded diagrams by flow and keep an overview.

Render the diagram and inspect it before finishing. Check labels, arrow direction, legibility, and agreement with the written design. Include a caption explaining the main flow and any boundary the reader needs to understand.

The diagram is required. Use other sections only when they help explain this system; combine related points and omit empty sections. Describe shared rules here; put feature-specific schema changes and migrations in its spec.

Verify claims about existing implementation against authoritative files or runtime evidence. Label future behavior and assumptions clearly. Do not mark a proposal accepted without human approval. Give important architectural rules stable INV-n IDs and name their intended enforcement and proof.

Update this document when an accepted change alters ownership, dependencies, interfaces, stored data, trust boundaries, deployment, or hard limits. Keep this document current. Preserve useful content and stable references; retain feature decision history in specs.

Return

Report the path, main decisions and tradeoffs, implementation gaps, and any blocking question.

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 AI skill do?

Designs and maintains root ARCHITECTURE.md for the intended system, including its data model and shared technical rules. Use for new systems or changes to system boundaries, ownership, storage, or deployment.

Why use Architecture on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/owainlewis/blueprint/tree/main/skills/architecture. 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?

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?

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

Is the Architecture AI skill free?

Yes. It is published on GitHub by owainlewis 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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