Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/meta-quest/agentic-tools/hz-simpleperf-debugnpx skills add meta-quest/agentic-tools --skill hz-simpleperf-debuggit clone --depth 1 https://github.com/meta-quest/agentic-toolsWrote 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/meta-quest/agentic-tools/hz-simpleperf-debug)<a href="https://agentmods.dev/skills/meta-quest/agentic-tools/hz-simpleperf-debug"><img src="https://agentmods.dev/badge/skills/meta-quest/agentic-tools/hz-simpleperf-debug.svg" alt="Measured on agentmods" 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 | $0.00050 | $0.01729 |
| Opus 5 | $0.00025 | $0.00864 |
| Sonnet 5 | $0.00010 | $0.00346 |
| Haiku 4.5 | $0.00005 | $0.00173 |
Grade A, and why
hz-simpleperf-debug 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 4d 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 — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Simpleperf Debug Skill
When to Use
Use this skill when you need hardware-level CPU performance insights on Meta Quest devices:
- Classifying whether an app is CPU-bound, memory-bound, or I/O-bound
- Finding CPU hotspot functions consuming the most cycles
- Measuring kernel vs userspace CPU overhead per thread
- Identifying cache-thrashing or branch-prediction issues
- Supplementing Perfetto trace analysis with hardware PMU counter data
This skill complements hz-perfetto-debug. Perfetto shows what your app is doing over time. Simpleperf shows where the CPU is spending hardware cycles — cache misses, branch mispredictions, and instruction throughput that Perfetto can't see.
VR Performance Context
Quest devices run on mobile ARM SoCs with strict thermal and power budgets. CPU-bound apps hit frame drops when:
| Refresh Rate | CPU Frame Budget | Notes |
|---|---|---|
| 120 Hz | 8.3 ms | Tight — simpleperf critical for finding hotspots |
| 90 Hz | 11.1 ms | Default target for most apps |
| 72 Hz | 13.9 ms | Fallback for heavier apps |
Simpleperf's hardware counters reveal bottlenecks invisible to software tracing.
metavr Setup
Simpleperf profiling is powered by the metavr CLI. Invoke via npx — no install required:
npx -y metavr --version
Examples below use the bare metavr command for brevity. If metavr is not on PATH, invoke the same CLI via npx -y metavr <args> (the CLI is published under the npm package metavr). Connect your Quest via USB with developer mode enabled.
Quick Start Workflow
1. Classify the Workload
Before optimizing, determine the bottleneck type:
# Classify the foreground app's workload (10-second sample)
metavr perf simpleperf classify
# Target a specific app
metavr perf simpleperf classify --app com.example.myapp
# Custom duration
metavr perf simpleperf classify --duration 15
Returns a classification with evidence:
| Classification | Indicator | Optimization Strategy |
|---|---|---|
| CPU-bound | High IPC, low stall ratio | Optimize algorithms, reduce draw calls, batch work |
| Memory-bound | High stall ratio (stalled-cycles-backend / cpu-cycles) | Reduce cache misses, improve data locality, shrink working set |
| I/O-bound | High context switches per second | Reduce blocking I/O, use async, minimize thread contention |
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 4d ago First seen · 185 lines · 50 tokens per session scan A 3b1bb49cb1e5
hz-simpleperf-debug is a skill published in the GitHub repository meta-quest/agentic-tools (187 stars, last pushed 12d ago), licensed Apache-2.0. It adds 50 tokens to every session and 1,729 once invoked, about $0.0003 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 2026-08-30.
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