Borrowing it
Nothing to install: this file belongs to dungnotnull/ar-vr-graphics-hardware-tuning-agent-skill. 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/dungnotnull/ar-vr-graphics-hardware-tuning-agent-skill/main/CLAUDE.mdgit clone --depth 1 https://github.com/dungnotnull/ar-vr-graphics-hardware-tuning-agent-skillWrote 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/instructions/dungnotnull/ar-vr-graphics-hardware-tuning-agent-skill/claude-md)<a href="https://agentmods.dev/instructions/dungnotnull/ar-vr-graphics-hardware-tuning-agent-skill/claude-md"><img src="https://agentmods.dev/badge/instructions/dungnotnull/ar-vr-graphics-hardware-tuning-agent-skill/claude-md/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/instructions/dungnotnull/ar-vr-graphics-hardware-tuning-agent-skill/claude-md"><img src="https://agentmods.dev/badge/instructions/dungnotnull/ar-vr-graphics-hardware-tuning-agent-skill/claude-md.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 | $0.01295 | $0.01295 |
| Opus 5 | $0.00647 | $0.00647 |
| Sonnet 5 | $0.00259 | $0.00259 |
| Haiku 4.5 | $0.00129 | $0.00129 |
Grade A, and why
ar-vr-graphics-hardware-tuning-agent-skill CLAUDE.md 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md — Skill 249: ar-vr-graphics-hardware-tuning
Skill Identity
- Skill Name:
ar-vr-graphics-hardware-tuning - Tagline: Graphics & Hardware Configuration Advisor for VR/AR Games — XR Graphics & Hardware Performance Tuning analysis & decision-support harness.
- Current Phase: Phase 0 — Architecture & Research
- Folder:
D:\972026\249-ar-vr-graphics-hardware-tuning\
Problem This Skill Solves
This skill provides a structured, evidence-backed analytical workflow for XR Graphics & Hardware Performance Tuning. It gathers authoritative real-time and reference data, applies recognized domain methods, cross-references academic research, and delivers actionable outputs that are fully evidenced, risk/limitation-disclosed, and traceable to authoritative sources — continuously self-improving through an automated knowledge crawl pipeline.
Harness Flow Summary
/ar-vr-graphics-hardware-tuning invoked
│
├─ Step 1: sub-gather-requirements → Clarify the object of analysis, constraints, timeframe, available inputs, target audience, and language before any data fetching.
├─ Step 2: sub-evidence-collector → Fetch authoritative real-time and reference data for the object: current status/parameters, authoritative documents/standards, and recent developments from domain and academic sources.
├─ Step 3: sub-core-analysis → Optimize graphics and hardware configuration for VR/AR games to hit the framerate/latency budget for comfort, balancing reprojection, resolution, and foveation.
├─ Step 4: sub-knowledge-updater → Query SECOND-KNOWLEDGE-BRAIN.md for authoritative academic and professional evidence; surface citations with tier labels and flag gaps for the crawl pipeline.
├─ Step 5: sub-advisor → Synthesize all prior analysis into a risk-disclosed conclusion with a full evidence chain and recommended actions.
└─ Step 6: main (quality gate) → verify evidence hierarchy, disclosure, output polish
Sub-Skills
| skills/sub-gather-requirements.md | Clarify the object of analysis, constraints, timeframe, available inputs, target audience, and language before any data fetching. |
| skills/sub-evidence-collector.md | Fetch authoritative real-time and reference data for the object: current status/parameters, authoritative documents/standards, and recent developments from domain and academic sources. |
| skills/sub-core-analysis.md | Optimize graphics and hardware configuration for VR/AR games to hit the framerate/latency budget for comfort, balancing reprojection, resolution, and foveation. |
| skills/sub-knowledge-updater.md | Query SECOND-KNOWLEDGE-BRAIN.md for authoritative academic and professional evidence; surface citations with tier labels and flag gaps for the crawl pipeline. |
| skills/sub-advisor.md | Synthesize all prior analysis into a risk-disclosed conclusion with a full evidence chain and recommended actions. |
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.
- 9d ago First seen · 122 lines · 1,295 tokens per session scan A 57b557ad22ca
ar-vr-graphics-hardware-tuning-agent-skill CLAUDE.md is an instructions file published in the GitHub repository dungnotnull/ar-vr-graphics-hardware-tuning-agent-skill (5 stars, last pushed 1mo ago), licensed MIT. It adds 1,295 tokens to every session, about $0.0065 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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