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Unity ECS Patterns

unity-ecs-patterns

Master Unity ECS (Entity Component System) with DOTS, Jobs, and Burst for high-performance game development. Use when building data-oriented games, optimizing performance, or working with large ent...

techwavedev/agi-agent-kit0installs4stars

SKILL.md

Full skill instructions

Unity ECS Patterns

Production patterns for Unity's Data-Oriented Technology Stack (DOTS) including Entity Component System, Job System, and Burst Compiler.

Use this skill when

  • Building high-performance Unity games
  • Managing thousands of entities efficiently
  • Implementing data-oriented game systems
  • Optimizing CPU-bound game logic
  • Converting OOP game code to ECS
  • Using Jobs and Burst for parallelization

Do not use this skill when

  • The task is unrelated to unity ecs patterns
  • You need a different domain or tool outside this scope

Instructions

  • Clarify goals, constraints, and required inputs.
  • Apply relevant best practices and validate outcomes.
  • Provide actionable steps and verification.
  • If detailed examples are required, open resources/​implementation-playbook.md.

Resources

  • resources/​implementation-playbook.md for detailed patterns and examples.
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AGI Framework Integration

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

Memory-First Protocol

Retrieve game design decisions, performance benchmarks, and engine configuration. Cache asset pipeline settings and build configurations.

# Check for prior game development context before starting
python3 execution/​memory_manager.py auto --query "game architecture and engine patterns for Unity Ecs Patterns"

Storing Results

After completing work, store game development decisions for future sessions:

python3 execution/​memory_manager.py store \
  --content "Game: ECS architecture with 60fps target, spatial partitioning for collision, asset pipeline with LOD" \
  --type technical --project <project> \
  --tags unity-ecs-patterns gaming

Multi-Agent Collaboration

Share engine decisions and performance budgets with art/​design agents and QA agents.

python3 execution/​cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Game feature implemented — performance profiled, asset pipeline updated, QA test plan created" \
  --project <project>
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