techwavedev Game Development
Orchestrator skill that provides core principles and routes to specialized sub-skills.
When to Use This Skill
You are working on a game development project. This skill teaches the PRINCIPLES of game development and directs you to the right sub-skill based on context.
Sub-Skill Routing
Platform Selection
| If the game targets... | Use Sub-Skill |
|---|---|
| Web browsers (HTML5, WebGL) | game-development/web-games |
| Mobile (iOS, Android) | game-development/mobile-games |
| PC (Steam, Desktop) | game-development/pc-games |
| VR/AR headsets | game-development/vr-ar |
Dimension Selection
| If the game is... | Use Sub-Skill |
|---|---|
| 2D (sprites, tilemaps) | game-development/2d-games |
| 3D (meshes, shaders) | game-development/3d-games |
Specialty Areas
| If you need... | Use Sub-Skill |
|---|---|
| GDD, balancing, player psychology | game-development/game-design |
| Multiplayer, networking | game-development/multiplayer |
| Visual style, asset pipeline, animation | game-development/game-art |
| Sound design, music, adaptive audio | game-development/game-audio |
Core Principles (All Platforms)
1. The Game Loop
Every game, regardless of platform, follows this pattern:
INPUT → Read player actions
UPDATE → Process game logic (fixed timestep)
RENDER → Draw the frame (interpolated)
Fixed Timestep Rule:
- Physics/logic: Fixed rate (e.g., 50Hz)
- Rendering: As fast as possible
- Interpolate between states for smooth visuals
2. Pattern Selection Matrix
| Pattern | Use When | Example |
|---|---|---|
| State Machine | 3-5 discrete states | Player: Idle→Walk→Jump |
| Object Pooling | Frequent spawn/destroy | Bullets, particles |
| Observer/Events | Cross-system communication | Health→UI updates |
| ECS | Thousands of similar entities | RTS units, particles |
| Command | Undo, replay, networking | Input recording |
| Behavior Tree | Complex AI decisions | Enemy AI |
Decision Rule: Start with State Machine. Add ECS only when performance demands.
3. Input Abstraction
Abstract input into ACTIONS, not raw keys:
"jump" → Space, Gamepad A, Touch tap
"move" → WASD, Left stick, Virtual joystick
Why: Enables multi-platform, rebindable controls.
4. Performance Budget (60 FPS = 16.67ms)
| System | Budget |
|---|---|
| Input | 1ms |
| Physics | 3ms |
| AI | 2ms |
| Game Logic | 4ms |
| Rendering | 5ms |
| Buffer | 1.67ms |
Optimization Priority:
- Algorithm (O(n²) → O(n log n))
- Batching (reduce draw calls)
- Pooling (avoid GC spikes)
- LOD (detail by distance)
- Culling (skip invisible)
5. AI Selection by Complexity
| AI Type | Complexity | Use When |
|---|---|---|
| FSM | Simple | 3-5 states, predictable behavior |
| Behavior Tree | Medium | Modular, designer-friendly |
| GOAP | High | Emergent, planning-based |
| Utility AI | High | Scoring-based decisions |
6. Collision Strategy
| Type | Best For |
|---|---|
| AABB | Rectangles, fast checks |
| Circle | Round objects, cheap |
| Spatial Hash | Many similar-sized objects |
| Quadtree | Large worlds, varying sizes |
Anti-Patterns (Universal)
| Don't | Do |
|---|---|
| Update everything every frame | Use events, dirty flags |
| Create objects in hot loops | Object pooling |
| Cache nothing | Cache references |
| Optimize without profiling | Profile first |
| Mix input with logic | Abstract input layer |
Routing Examples
Example 1: "I want to make a browser-based 2D platformer"
→ Start with game-development/web-games for framework selection
→ Then game-development/2d-games for sprite/tilemap patterns
→ Reference game-development/game-design for level design
Example 2: "Mobile puzzle game for iOS and Android"
→ Start with game-development/mobile-games for touch input and stores
→ Use game-development/game-design for puzzle balancing
Example 3: "Multiplayer VR shooter"
→ game-development/vr-ar for comfort and immersion
→ game-development/3d-games for rendering
→ game-development/multiplayer for networking
Remember: Great games come from iteration, not perfection. Prototype fast, then polish.
AGI Framework Integration
Qdrant Memory Integration
Before executing complex tasks with this skill:
python3 execution/memory_manager.py auto --query "<task summary>"
Decision Tree:
- Cache hit? Use cached response directly — no need to re-process.
- Memory match? Inject
context_chunksinto your reasoning. - No match? Proceed normally, then store results:
python3 execution/memory_manager.py store \
--content "Description of what was decided/solved" \
--type decision \
--tags game-development <relevant-tags>
Note: Storing automatically updates both Vector (Qdrant) and Keyword (BM25) indices.
Agent Team Collaboration
- Strategy: This skill communicates via the shared memory system.
- Orchestration: Invoked by
orchestratorvia intelligent routing. - Context Sharing: Always read previous agent outputs from memory before starting.
Local LLM Support
When available, use local Ollama models for embedding and lightweight inference:
- Embeddings:
nomic-embed-textvia Qdrant memory system - Lightweight analysis: Local models reduce API costs for repetitive patterns