# architecture-design

Lays out the structure of an ML project built on factories and registries. Useful when adding a new dataset or model that needs a registration decorator.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 379 installs · ★ 5906
- Author: Galaxy-Dawn
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/Galaxy-Dawn/claude-scholar/tree/HEAD/skills/architecture-design/SKILL.md
- Updated: 2026-10-19
- HTML: https://affpapa.org/en/nevibecode/skills/architecture-design

## What it does

The skill describes a modular layout: `run/` holds pipelines and Hydra configs, `src/` holds data, model, trainer and analysis modules, plus `data/` and `outputs/`. Three patterns carry it. A factory builds an object by name, a `@register_*` decorator drops the class into a registry, and auto-import pulls in submodules from a directory.

Reach for it when you create a dataset class with `@register_dataset`, a model with `@register_model`, a new module directory wired through a factory in `__init__.py`, or a project from scratch. Editing existing functions, fixing bugs or refactoring without new registrable components is out of scope. No decorator, no factory — skip it.

Models here are config-driven: `__init__` takes only `cfg`, hyperparameters come from the config, and `forward()` returns a dict with `loss`, `labels` and `logits`. Training and inference split on `self.training`. Install runs through `npx skills add`, or `gh skill install` for GitHub Copilot; the skill file lands at `~/.claude/skills/architecture-design/SKILL.md`.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add Galaxy-Dawn/claude-scholar --skill architecture-design -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/architecture-design/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/architecture-design/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add Galaxy-Dawn/claude-scholar --skill architecture-design -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/architecture-design/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add Galaxy-Dawn/claude-scholar --skill architecture-design -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/architecture-design/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install Galaxy-Dawn/claude-scholar architecture-design
```

2. **Manually: put the files in place**
- File: `.github/skills/architecture-design/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add Galaxy-Dawn/claude-scholar --skill architecture-design -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/Galaxy-Dawn/claude-scholar --path skills/architecture-design
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/architecture-design/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add Galaxy-Dawn/claude-scholar --skill architecture-design -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/Galaxy-Dawn/claude-scholar/tree/HEAD/skills/architecture-design/SKILL.md

## When to use it
- add a dataset class wired into the registry
- create a model that reads hyperparameters from config
- set up a module directory with a factory in `__init__.py`
- scaffold an ML project structure from scratch
- plug in a new component type such as augmentation or metrics

## Example prompts
- Create a new dataset with `@register_dataset` following the skill patterns
- Add `MyModel` with `@register_model`, `__init__` taking only `cfg`
- Scaffold the structure for a new ML project from the skill template

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 9fa0924c898fed38.

BEGIN THIRD-PARTY FILE 9fa0924c898fed38
````text
---
name: architecture-design
description: Use only when creating new registrable ML components that require Factory or Registry patterns.
version: 1.2.0
---

# Architecture Design - ML Project Template

This skill defines the standard code architecture for machine learning projects based on the template structure. When modifying or extending code, follow these patterns to maintain consistency.

## Overview

The project follows a modular, extensible architecture with clear separation of concerns. Each module (data, model, trainer, analysis) is independently organized using factory and registry patterns for maximum flexibility.

## When to Use

Use this skill when:
- Creating a new Dataset class that needs `@register_dataset`
- Creating a new Model class that needs `@register_model`
- Creating a new module directory with `__init__.py` factory wiring
- Initializing a new ML project structure from scratch
- Adding new component types such as Augmentation, CollateFunction, or Metrics

## When Not to Use

Do not use this skill when:
- Modifying existing functions or methods
- Fixing bugs in existing code
- Adding helper functions or utilities
- Refactoring without adding new registrable components
- Making simple code changes to a single file
- Modifying configuration files
- Reading or understanding existing code

Key indicator: if the task does not require a `@register_*` decorator or a Factory pattern, skip this skill.

## Core Design Patterns

### Factory Pattern

Each module uses a factory to create instances dynamically:

```python
# Example from data_module/dataset/__init__.py
DATASET_FACTORY: Dict = {}

def DatasetFactory(data_name: str):
    dataset = DATASET_FACTORY.get(data_name, None)
    if dataset is None:
        print(f"{data_name} dataset is not implementation, use simple dataset")
        dataset = DATASET_FACTORY.get('simple')
    return dataset
```

For detailed guidance, refer to `references/factory_pattern.md`.

### Registry Pattern

Components register themselves via decorators:

```python
# Example from data_module/dataset/simple_dataset.py
@register_dataset("simple")
class SimpleDataset(Dataset):
    def __init__(self, data):
        self.data = data
```

For detailed guidance, refer to `references/registry_pattern.md`.

### Auto-Import Pattern

Modules automatically discover and import submodules:

```python
# Example from data_module/dataset/__init__.py
models_dir = os.path.dirname(__file__)
import_modules(models_dir, "src.data_module.dataset")
```
````
END THIRD-PARTY FILE 9fa0924c898fed38

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