How do I install the Vr Ar skill?

Run npx skillmds@latest add techwavedev/vr-ar in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

What does the Vr Ar skill do?

VR/AR development principles. Comfort, interaction, performance requirements. It is listed under Coding & Dev Tools on SkillMD.

Is Vr Ar safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Vr Ar?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Vr Ar free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

techwavedev (@techwavedev) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Vr Ar by techwavedev — Agent Skill

Name: Vr Ar
Rating: 4.5 (4 reviews)
Author: techwavedev

File contents

VR/AR Development

Immersive experience principles.

1. Platform Selection

VR Platforms

Platform	Use Case
Quest	Standalone, wireless
PCVR	High fidelity
PSVR	Console market
WebXR	Browser-based

AR Platforms

Platform	Use Case
ARKit	iOS devices
ARCore	Android devices
WebXR	Browser AR
HoloLens	Enterprise

2. Comfort Principles

Motion Sickness Prevention

Cause	Solution
Locomotion	Teleport, snap turn
Low FPS	Maintain 90 FPS
Camera shake	Avoid or minimize
Rapid acceleration	Gradual movement

Comfort Settings

Vignette during movement
Snap vs smooth turning
Seated vs standing modes
Height calibration

3. Performance Requirements

Target Metrics

Platform	FPS	Resolution
Quest 2	72-90	1832x1920
Quest 3	90-120	2064x2208
PCVR	90	2160x2160+
PSVR2	90-120	2000x2040

Frame Budget

VR requires consistent frame times
Single dropped frame = visible judder
90 FPS = 11.11ms budget

4. Interaction Principles

Controller Interaction

Type	Use
Point + click	UI, distant objects
Grab	Manipulation
Gesture	Magic, special actions
Physical	Throwing, swinging

Hand Tracking

More immersive but less precise
Good for: social, casual
Challenging for: action, precision

5. Spatial Design

World Scale

1 unit = 1 meter (critical)
Objects must feel right size
Test with real measurements

Depth Cues

Cue	Importance
Stereo	Primary depth
Motion parallax	Secondary
Shadows	Grounding
Occlusion	Layering

6. Anti-Patterns

❌ Don't	✅ Do
Move camera without player	Player controls camera
Drop below 90 FPS	Maintain frame rate
Use tiny UI text	Large, readable text
Ignore arm length	Scale to player reach

Remember: Comfort is not optional. Sick players don't play.

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_chunks into 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 vr-ar <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 orchestrator via 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-text via Qdrant memory system
Lightweight analysis: Local models reduce API costs for repetitive patterns

---name: vr-ardescription: VR/AR development principles. Comfort, interaction, performance requirements.---# VR/AR Development> Immersive experience principles.---## 1. Platform Selection### VR Platforms| Platform | Use Case ||----------|----------|| **Quest** | Standalone, wireless || **PCVR** | High fidelity || **PSVR** | Console market || **WebXR** | Browser-based |### AR Platforms| Platform | Use Case ||----------|----------|| **ARKit** | iOS devices || **ARCore** | Android devices || **WebXR** | Browser AR || **HoloLens** | Enterprise |---## 2. Comfort Principles### Motion Sickness Prevention| Cause | Solution ||-------|----------|| **Locomotion** | Teleport, snap turn || **Low FPS** | Maintain 90 FPS || **Camera shake** | Avoid or minimize || **Rapid acceleration** | Gradual movement |### Comfort Settings- Vignette during movement- Snap vs smooth turning- Seated vs standing modes- Height calibration---## 3. Performance Requirements### Target Metrics| Platform | FPS | Resolution ||----------|-----|------------|| Quest 2 | 72-90 | 1832x1920 || Quest 3 | 90-120 | 2064x2208 || PCVR | 90 | 2160x2160+ || PSVR2 | 90-120 | 2000x2040 |### Frame Budget- VR requires consistent frame times- Single dropped frame = visible judder- 90 FPS = 11.11ms budget---## 4. Interaction Principles### Controller Interaction| Type | Use ||------|-----|| **Point + click** | UI, distant objects || **Grab** | Manipulation || **Gesture** | Magic, special actions || **Physical** | Throwing, swinging |### Hand Tracking- More immersive but less precise- Good for: social, casual- Challenging for: action, precision---## 5. Spatial Design### World Scale- 1 unit = 1 meter (critical)- Objects must feel right size- Test with real measurements### Depth Cues| Cue | Importance ||-----|------------|| Stereo | Primary depth || Motion parallax | Secondary || Shadows | Grounding || Occlusion | Layering |---## 6. Anti-Patterns| ❌ Don't | ✅ Do ||----------|-------|| Move camera without player | Player controls camera || Drop below 90 FPS | Maintain frame rate || Use tiny UI text | Large, readable text || Ignore arm length | Scale to player reach |---> **Remember:** Comfort is not optional. Sick players don't play.## AGI Framework Integration### Qdrant Memory IntegrationBefore executing complex tasks with this skill:```bashpython3 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_chunks` into your reasoning.- **No match?** Proceed normally, then store results:```bashpython3 execution/memory_manager.py store \ --content "Description of what was decided/solved" \ --type decision \ --tags vr-ar <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 `orchestrator` via intelligent routing.- **Context Sharing**: Always read previous agent outputs from memory before starting.### Local LLM SupportWhen available, use local Ollama models for embedding and lightweight inference:- Embeddings: `nomic-embed-text` via Qdrant memory system- Lightweight analysis: Local models reduce API costs for repetitive patterns

Frequently asked questions

Vr Ar