audio-perf-loop

Orchestrate multi-wave investigations to diagnose audio clipping and asset-load stutter in RB3.

4|Updated Mar 19, 2026
One-click install
npx skills add https://github.com/freeqaz/rb3 --skill audio-perf-loop
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: audio-perf-loop
Source: https://github.com/freeqaz/rb3/tree/main/.claude/skills/audio-perf-loop
Command: npx skills add https://github.com/freeqaz/rb3 --skill audio-perf-loop

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill orchestrates multi-wave investigations to diagnose audio clipping and asset-load stutter in Rock Band 3 RB3, enabling structured measurement and reproducible fixes.

Core Features & Use Cases

  • Orchestrated waves coordinate parallel hypotheses and subagents, writing findings to on-disk wave docs for traceability.
  • Cross-target analysis supports native-first evaluation with optional web checks, ensuring consistent metrics across platforms.
  • Handoff discipline uses a canonical STATE.md and per-wave docs to drive convergent fixes and re-measurement.

Quick Start

Start the native-first audio-perf loop and iterate waves until convergence.

Frequently Asked Questions about audio-perf-loop

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I debug audio clipping and asset-load stutter in RB3?

Debug audio clipping and asset-load stutter in RB3 by running an orchestrated multi-wave investigation that coordinates subagents, takes explicit measurements, and logs findings to on-disk wave docs for reproducible fixes.

What is a multi-wave audio fidelity investigation workflow?

A multi-wave audio fidelity investigation is a structured debugging process that applies parallel hypotheses across native and web targets, using STATE.md handoffs to drive convergent fixes and re-measurement until issues resolve.

How do I start an audio-perf loop for native-first evaluation?

Start the native-first audio-perf loop by initiating the orchestrated workflow, then iterate through waves of subagent coordination and structured measurement until performance metrics converge.

Does this audio debugging workflow support both native and web targets?

Yes, the workflow supports cross-target analysis with a native-first evaluation approach and optional web checks, ensuring consistent audio fidelity and load performance metrics across both platforms.

Why do I need STATE.md handoffs for audio performance debugging?

You need STATE.md handoffs to maintain handoff discipline across waves, ensuring convergent fixes are tracked and re-measured accurately across native and web targets for reproducible results.

What's the best way to coordinate subagents for load-perf studies?

The best way to coordinate subagents for load-perf studies is using an orchestrated multi-wave loop that assigns parallel hypotheses, logs findings to per-wave docs, and references tooling for reproducible measurements.