Bevy Ecs Expert

Bevy ECS Expert

Overview

A guide to building high-performance game logic using Bevy's data-oriented ECS architecture. Learn how to structure systems, optimize queries, manage resources, and leverage parallel execution.

When to Use This Skill

• Use when developing games with the Bevy engine in Rust.
• Use when designing game systems that need to run in parallel.
• Use when optimizing game performance by minimizing cache misses.
• Use when refactoring object-oriented logic into data-oriented ECS patterns.

Step-by-Step Guide

1. Defining Components

Use simple structs for data. Derive Component and Reflect.

#[derive(Component, Reflect, Default)]
#[reflect(Component)]
struct Velocity {
    x: f32,
    y: f32,
}

#[derive(Component)]
struct Player;

2. Writing Systems

Systems are regular Rust functions that query components.

fn movement_system(
    time: Res<Time>,
    mut query: Query<(&mut Transform, &Velocity), With<Player>>,
) {
    for (mut transform, velocity) in &mut query {
        transform.translation.x += velocity.x * time.delta_seconds();
        transform.translation.y += velocity.y * time.delta_seconds();
    }
}

3. Managing Resources

Use Resource for global data (score, game state).

#[derive(Resource)]
struct GameState {
    score: u32,
}

fn score_system(mut game_state: ResMut<GameState>) {
    game_state.score += 10;
}

4. Scheduling Systems

Add systems to the App builder, defining execution order if needed.

fn main() {
    App::new()
        .add_plugins(DefaultPlugins)
        .init_resource::<GameState>()
        .add_systems(Update, (movement_system, score_system).chain())
        .run();
}

Examples

Example 1: Spawning Entities with Require Component

use bevy::prelude::*;

#[derive(Component, Reflect, Default)]
#[require(Velocity, Sprite)]
struct Player;

#[derive(Component, Default)]
struct Velocity {
    x: f32,
    y: f32,
}

fn setup(mut commands: Commands, asset_server: Res<AssetServer>) {
    commands.spawn((
        Player,
        Velocity { x: 10.0, y: 0.0 },
        Sprite::from_image(asset_server.load("player.png")), 
    ));
}

Example 2: Query Filters

Use With and Without to filter entities efficiently.

fn enemy_behavior(
    query: Query<&Transform, (With<Enemy>, Without<Dead>)>,
) {
    for transform in &query {
        // Only active enemies processed here
    }
}

Best Practices

• ✅ Do: Use Query filters (With, Without, Changed) to reduce iteration count.
• ✅ Do: Prefer Res over ResMut when read-only access is sufficient to allow parallel execution.
• ✅ Do: Use Bundle to spawn complex entities atomically.
• ❌ Don't: Store heavy logic inside Components; keep them as pure data.
• ❌ Don't: Use RefCell or interior mutability inside components; let the ECS handle borrowing.

Troubleshooting

Problem: System panic with "Conflict" error. Solution: You are likely trying to access the same component mutably in two systems running in parallel. Use .chain() to order them or split the logic.

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Retrieve prior decisions and patterns to avoid re-discovering solutions. Cache results for instant retrieval in future sessions.

# Check for prior development context before starting
python3 execution/memory_manager.py auto --query "prior work and patterns related to Bevy Ecs Expert"

Storing Results

After completing work, store development decisions for future sessions:

python3 execution/memory_manager.py store \
  --content "Completed task with key insights documented for future reference" \
  --type decision --project <project> \
  --tags bevy-ecs-expert default

Multi-Agent Collaboration

Share outcomes with other agents so the team stays aligned and avoids duplicate work.

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Task completed — results documented and shared with team" \
  --project <project>