Borrowing it
Nothing to install: this file belongs to diillson/chatcli. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/diillson/chatcli/main/.agent/skills/game-development/vr-ar/SKILL.mdgit clone --depth 1 https://github.com/diillson/chatcliWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/diillson/chatcli/vr-ar)<a href="https://agentmods.dev/skills/diillson/chatcli/vr-ar"><img src="https://agentmods.dev/badge/skills/diillson/chatcli/vr-ar/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/diillson/chatcli/vr-ar"><img src="https://agentmods.dev/badge/skills/diillson/chatcli/vr-ar.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00016 | $0.00627 |
| Opus 5.5 | $0.00006 | $0.00251 |
| Sonnet 5.5 | $0.00003 | $0.00125 |
| Haiku 4.5 | $0.00002 | $0.00063 |
Grade A, and why
vr-ar scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to vr-ar — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 124 lines · 16 tokens per session scan A 59ddbdecc4fa
vr-ar is a skill published in the GitHub repository diillson/chatcli (93 stars, last pushed today), licensed Apache-2.0. It adds 16 tokens to every session and 627 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to vr-ar, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
agent-state-bar
A design guide for an agent status bar: a short, automatically updated summary of progress, remaining tasks, tool counts, errors, and system state placed near the latest model input.
reward-design
A guide to defining the scores that an AI agent receives during trial-and-error training. It covers checks based on rules, human preferences, or another model, and distinguishes rewarding the final result from rewarding the steps taken.
ai-style
A Chinese-language writing guide for producing or revising reader-facing text such as product announcements, public-account articles, emails, and README files. It focuses on reducing patterns that make writing sound machine-generated.
agent-evolution
A guide to making an AI agent improve from its past runs. It covers how to judge results, extract reusable lessons, and decide whether to store them as knowledge, instructions, code, or model training.
bad-case-to-dpo
A guide to turning failed production agent runs into training and evaluation data. It explains how to find the first mistake, create preference examples for DPO (Direct Preference Optimization), and check that a fix does not damage other tasks.
knowledge-org
A guide to organising large bodies of knowledge so an AI system can navigate relationships, summaries, and layers instead of searching only flat text chunks. It covers tree indexes, knowledge graphs, file-like structures, incremental updates, and long-term memory layouts.