# roboryantron/Unite2017

Sample project for Game Architecture with Scriptable Objects from Unite Austin 2017

Repository: https://github.com/roboryantron/Unite2017
Canonical: https://ross.abutalabs.com/products/unite2017
Language: C#
License: MIT
License Family: permissive
Topics: unity3d, scriptableobject, uniteaustin, unite2017
Last push: 2020-09-21T17:13:22+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3250, "days_push": 2172, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1253, forks 206 (observed 2026-08-28T04:04:08.575421+00:00)

## What it is
A sample Unity project demonstrating the game architecture patterns presented in the 'Game Architecture with Scriptable Objects' talk at Unite Austin 2017. It contains simplified implementations of event channels, variables, and runtime sets built on ScriptableObjects.

## Use cases
- learn scriptable object architecture in unity
- decouple game systems with scriptable objects
- study event-driven unity game architecture
- find unity design pattern examples
- replace singleton managers in unity projects

## When to choose
- you want to learn the Scriptable Object architecture pattern from the well-known Unite 2017 talk
- you need a small, readable reference implementation to adapt into your Unity project

## When to avoid
- you need production-ready, fully featured systems rather than simplified teaching code
- your project does not use Unity or C#
- you need actively maintained or updated code

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: game-engine, developer-tools
- domain: tutorials
- platform: game-engine, cross-platform
- tags: unity, scriptable-objects, sample-project, architecture, csharp, game-development

## Member repositories
- roboryantron/Unite2017 (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:08.575421+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T05:07:30.404688+00:00, confidence not recorded.
  - readme: https://github.com/roboryantron/Unite2017 (fetched 2026-08-28T04:04:08.575421+00:00, sha aa8cdb8bfa15)
- Data as of 2026-08-30T08:39:29.467469+00:00.