## 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)
