techwavedev Game Design Principles
Design thinking for engaging games.
1. Core Loop Design
The 30-Second Test
Every game needs a fun 30-second loop:
1. ACTION → Player does something
2. FEEDBACK → Game responds
3. REWARD → Player feels good
4. REPEAT
Loop Examples
| Genre | Core Loop |
|---|---|
| Platformer | Run → Jump → Land → Collect |
| Shooter | Aim → Shoot → Kill → Loot |
| Puzzle | Observe → Think → Solve → Advance |
| RPG | Explore → Fight → Level → Gear |
2. Game Design Document (GDD)
Essential Sections
| Section | Content |
|---|---|
| Pitch | One-sentence description |
| Core Loop | 30-second gameplay |
| Mechanics | How systems work |
| Progression | How player advances |
| Art Style | Visual direction |
| Audio | Sound direction |
Principles
- Keep it living (update regularly)
- Visuals help communicate
- Less is more (start small)
3. Player Psychology
Motivation Types
| Type | Driven By |
|---|---|
| Achiever | Goals, completion |
| Explorer | Discovery, secrets |
| Socializer | Interaction, community |
| Killer | Competition, dominance |
Reward Schedules
| Schedule | Effect | Use |
|---|---|---|
| Fixed | Predictable | Milestone rewards |
| Variable | Addictive | Loot drops |
| Ratio | Effort-based | Grind games |
4. Difficulty Balancing
Flow State
Too Hard → Frustration → Quit
Too Easy → Boredom → Quit
Just Right → Flow → Engagement
Balancing Strategies
| Strategy | How |
|---|---|
| Dynamic | Adjust to player skill |
| Selection | Let player choose |
| Accessibility | Options for all |
5. Progression Design
Progression Types
| Type | Example |
|---|---|
| Skill | Player gets better |
| Power | Character gets stronger |
| Content | New areas unlock |
| Story | Narrative advances |
Pacing Principles
- Early wins (hook quickly)
- Gradually increase challenge
- Rest beats between intensity
- Meaningful choices
6. Anti-Patterns
| ❌ Don't | ✅ Do |
|---|---|
| Design in isolation | Playtest constantly |
| Polish before fun | Prototype first |
| Force one way to play | Allow player expression |
| Punish excessively | Reward progress |
Remember: Fun is discovered through iteration, not designed on paper.
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-design <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