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## Why Macrobenchmarks benefit from having a way to exercise remote-executor latency without depending on Docker. This is a very minimal first cut, if we find that simulating network conditions is useful we can always expand this scope or switch to a more robust network shaping approach. ## What - add a package-local exec-server binary for Cargo and Bazel test fixtures - add a host-local WebSocket exec-server fixture and fixed-delay interposer - let TestAppServer route its auto environment through that delayed WebSocket transport - cover the delayed thread/start path through the public app-server API ## Stack 1. [#31425 test: add TestAppServer builder](https://github.com/openai/codex/pull/31425) 2. [#31427 test: add delayed exec-server transport](https://github.com/openai/codex/pull/31427) 3. [#31295 bench: add cold skill load macrobenchmark](https://github.com/openai/codex/pull/31295) 4. [#31428 bench: add e2e benchmark entrypoints](https://github.com/openai/codex/pull/31428) 5. [#31429 ci: smoke Bazel e2e benchmarks](https://github.com/openai/codex/pull/31429)