Profile and benchmark qwen3-tts-rs inference using e2e_bench with chrome tracing, flamegraph, or Nsight Systems inside a CUDA Docker container. Use when profiling, benchmarking, checking performance, finding bottlenecks, or running flamegraphs.
Quickly install qwen3-tts-profile AI skill to your development environment via command line
Source: trevors/dot-claude.
Run performance profiling and benchmarks for the qwen3-tts Rust TTS engine.
The CUDA toolchain lives inside the Docker container. All cargo commands must run there. The workspace is bind-mounted at /workspace:
The e2ebench binary reports stage breakdowns (prefill / generation / decode) even without the profiling feature:
Profile and benchmark qwen3-tts-rs inference using e2e_bench with chrome tracing, flamegraph, or Nsight Systems inside a CUDA Docker container. Use when profiling, benchmarking, checking performance, finding bottlenecks, or running flamegraphs. Source: trevors/dot-claude.
Stable fields and commands for AI/search citations.
npx skills add https://github.com/trevors/dot-claude --skill qwen3-tts-profileProfile and benchmark qwen3-tts-rs inference using e2e_bench with chrome tracing, flamegraph, or Nsight Systems inside a CUDA Docker container. Use when profiling, benchmarking, checking performance, finding bottlenecks, or running flamegraphs. Source: trevors/dot-claude.
Open your terminal or command line tool (Terminal, iTerm, Windows Terminal, etc.) Copy and run this command: npx skills add https://github.com/trevors/dot-claude --skill qwen3-tts-profile Once installed, the skill will be automatically configured in your AI coding environment and ready to use in Claude Code, Cursor, or OpenClaw
https://github.com/trevors/dot-claude