## Summary
This seems to be one of the most consistent benchmark cases we have in
terms of standard deviation:
```
➜ hyperfine "target/profiling/main pip compile scripts/requirements/airflow.in" --runs 200
Benchmark 1: target/profiling/main pip compile scripts/requirements/airflow.in
Time (mean ± σ): 292.6 ms ± 6.6 ms [User: 414.1 ms, System: 194.2 ms]
Range (min … max): 282.7 ms … 320.1 ms 200 runs
```
For smaller benchmarks, scispacy and dtlssocket seem to be a bit more
consistent than our current jupyter benchmark, but it hasn't given us
any problems so I'll leave it for now.
Remove a dev annoyance: This makes the development paths shorter, e.g.
`scripts/benchmarks/requirements/all-kinds.in` to
`scripts/requirements/all-kinds.in`. The requirements are also shared
between different tasks and not only used for benchmarking.
