3d-games
Free3D game development principles. Rendering, shaders, physics, cameras.
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2D game development principles. Sprites, tilemaps, physics, camera.
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Principles for 2D game systems.
| Component | Purpose |
|---|---|
| Atlas | Combine textures, reduce draw calls |
| Animation | Frame sequences |
| Pivot | Rotation/scale origin |
| Layering | Z-order control |
| Factor | Recommendation |
|---|---|
| Size | 16x16, 32x32, 64x64 |
| Auto-tiling | Use for terrain |
| Collision | Simplified shapes |
| Layer | Content |
|---|---|
| Background | Non-interactive scenery |
| Terrain | Walkable ground |
| Props | Interactive objects |
| Foreground | Parallax overlay |
| Shape | Use Case |
|---|---|
| Box | Rectangular objects |
| Circle | Balls, rounded |
| Capsule | Characters |
| Polygon | Complex shapes |
| Type | Use |
|---|---|
| Follow | Track player |
| Look-ahead | Anticipate movement |
| Multi-target | Two-player |
| Room-based | Metroidvania |
| ❌ Don't | ✅ Do |
|---|---|
| Separate textures | Use atlases |
| Complex collision shapes | Simplified collision |
| Jittery camera | Smooth following |
| Pixel-perfect on physics | Choose one approach |
Remember: 2D is about clarity. Every pixel should communicate.
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3D game development principles. Rendering, shaders, physics, cameras.
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Create aesthetically beautiful interfaces following proven design principles. Use when building UI/UX, analyzing designs from inspiration sites, generating design images with ai-multimodal, implementing visual hierarchy and color theory, adding micro-interactions, or creating design documentation. Includes workflows for capturing and analyzing inspiration screenshots with chrome-devtools and ai-multimodal, iterative design image generation until aesthetic standards are met, and comprehensive design system guidance covering BEAUTIFUL (aesthetic principles), RIGHT (functionality/accessibility), SATISFYING (micro-interactions), and PEAK (storytelling) stages. Integrates with chrome-devtools, ai-multimodal, media-processing, ui-styling, and web-frameworks skills.
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Use when implementing or managing persistent, hierarchical memory systems for AI agents. Covers cross-session state, fact supersession, and self-managed memory tools to enable long-term recall and adaptive agent behavior.
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Use when monitoring, tracing, or debugging agentic workflows in production. Keywords: observability, tracing, OpenTelemetry, Langfuse, latency, token cost, loop detection, telemetry.
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Use when building self-correcting retrieval systems for AI agents. Keywords: RAG, retrieval, Corrective RAG, Self-RAG, query decomposition, reranking, hallucination, grounding.
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Use when building autonomous LLM agents operating in live environments. Protects against prompt injection, tool abuse, data exfiltration, and runaway loops. Keywords: guardrails, prompt injection, IPI, safety, sandboxing, NeMo, LLM Guard.
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