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Architecture Aware Init

Community
athola
architecture-aware-init

Selects architecture paradigm via research before scaffolding. Use when architecture is undecided and the choice needs justification and documentation.

Overview

Publisherathola
Repositoryclaude-night-market
Skill namearchitecture-aware-init
Stars
343
Forks
37
Bundled files
4
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.

  • 4 bundled files

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

  • Open source

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

Installation

Install the Architecture Aware Init 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/athola/claude-night-market/tree/master/plugins/attune/skills/architecture-aware-init
  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/athola/claude-night-market.git /tmp/claude-night-market
mkdir -p .claude/skills
cp -r /tmp/claude-night-market/plugins/attune/skills/architecture-aware-init .claude/skills/architecture-aware-init
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Architecture Aware Init 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 Aware Init 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 Aware Init 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-Aware Project Initialization

Overview

Project initialization that combines online research, archetype selection, template customization, and decision documentation into one workflow. Use this skill when the architecture is undecided and the choice deserves justification.

When To Use This Skill

  • Starting a new project and unsure which architecture fits best
  • Wanting modern, industry-standard architecture choices
  • Needing justification for architectural decisions
  • Wanting templates customized to the chosen paradigm

Use instead of project-init when architecture is undecided. Use before project-specification to establish the architectural foundation.

Required TodoWrite Items

  1. arch-init:research-completed: Online research completed
  2. arch-init:paradigm-selected: Architecture paradigm chosen
  3. arch-init:templates-customized: Templates adapted to paradigm
  4. arch-init:decision-recorded: ADR created

5-Step Workflow

Steps 1-2: Gather context, research best practices

Load modules/research-flow.md for the full project-context questionnaire and the three-tier search strategy. Output: a synthesis brief that feeds Step 3.

Step 3: Select the architecture paradigm

Load modules/paradigm-selection.md for the decision matrix (team size by domain complexity) and the special-case overrides (streaming, serverless, microkernel, etc.). Two routes:

  • Use the archetypes:architecture-paradigms skill for guided exploration.
  • Use the matrix directly for a fast recommendation.

Step 4-5: Customize templates and record the decision

Load modules/scaffold-generation.md for the paradigm-specific directory layouts (Functional Core / Hexagonal / Microservices shown; others delegated to the corresponding archetypes:architecture-paradigm-{name} skill) and the ADR template.

Output: Initialization Package

After completing the workflow, the project has:

  1. A directory structure matched to the chosen architecture
  2. Configuration that reflects the paradigm (test layout, tooling, dependency hints)
  3. An ADR explaining why this paradigm was chosen
  4. Links to the relevant paradigm skill for ongoing implementation guidance
  5. References to similar real-world projects from Step 2

Script Integration

The interactive workflow above is the default. For automation, load modules/script-integration.md for the three Python helpers under plugins/attune/scripts/ (architecture researcher, template customizer, full interactive flow) and library-style import examples.

Integration with Existing Commands

This skill enhances /attune:project-init by adding an architecture-selection phase before scaffolding:

bash
# Standard initialization (no architecture decision)
/attune:project-init --lang python --name my-project

# Architecture-aware initialization
/attune:brainstorm                  # Explore project needs
Skill(architecture-aware-init)      # Select architecture
/attune:project-init --arch <paradigm> --name my-project

Example Session

User: "I'm creating a Python web API for a fintech application. Team of 8 developers, complex business rules, need high security and audit trails."

  • Step 1 context: Web API, highly complex domain, 5-15 engineers, security and auditability requirements.
  • Step 2 research: queries for fintech API patterns, audit-trail architecture, CQRS+ES Python examples.
  • Step 3 selection: research plus decision matrix yields CQRS + Event Sourcing.
  • Step 4 templates: command-handler module, query-handler module, event store, aggregate patterns, projection handlers.
  • Step 5 ADR: documents why CQRS/ES fits (auditability, complex rules, regulatory compliance).

Result: project initialized with paradigm-appropriate structure and clear decision rationale.

Related Skills

  • Skill(archetypes:architecture-paradigms): paradigm catalog
  • Skill(archetypes:architecture-paradigm-*): per-paradigm implementation guidance
  • Skill(attune:project-brainstorming): ideation before architecture
  • Skill(attune:project-specification): requirements after the paradigm is chosen

See Also

  • /attune:project-init: basic project initialization
  • /attune:blueprint: architecture planning after paradigm selection
  • plugins/archetypes/README.md: full paradigm reference

Exit Criteria

  • All four TodoWrite items are checked off: arch-init:research-completed, arch-init:paradigm-selected, arch-init:templates-customized, arch-init:decision-recorded.
  • An ADR file exists in the initialized project explaining why the selected paradigm was chosen, referencing evidence from the Step 2 research synthesis.
  • The project directory structure matches the layout prescribed by the chosen archetypes:architecture-paradigm-* skill (verified by listing the created directories).
  • If the paradigm is undecided after research, the skill surfaces the top two candidates with trade-offs and waits for explicit user selection rather than defaulting silently.
  • When research ran, research.json holds preferred and avoid lists and was passed with --research-file; the recommendation's rationale contains a rule naming "research"

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 Aware Init AI skill do?

Selects architecture paradigm via research before scaffolding. Use when architecture is undecided and the choice needs justification and documentation.

Why use Architecture Aware Init on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/athola/claude-night-market/tree/master/plugins/attune/skills/architecture-aware-init. 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 Aware Init?

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 Aware Init?

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

Is the Architecture Aware Init AI skill free?

Yes. It is published on GitHub by athola 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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