benchmarklisted

# Benchmark Two tools, two questions. `hyperfine` answers "how long does this command take" with warmup, repeated runs, and a comparison; `oha` answers "how does this endpoint behave under load" with latency percentiles and a live TUI. Diagnosing why something is slow belongs to [systematic-debugging](../systematic-debugging/SKILL.md); this skill produces the numbers. ## Commands ```bash hyperfine --warmup 3 --runs 10 'old-cmd' 'new-cmd' # A/B with mean ± σ and a relative speed line hyperfine --warmup 3 --prepare 'uv sync --frozen' 'uv run pytest -q' # prepare the locked environment before each run hyperfine --parameter-list n 10,100,1000 'tool --items {n}' # scaling curve hyperfine --export-markdown bench.md --export-json bench.json 'cmd' # tables for the PR, raw data for later oha -z 30s -c 50 --latency-correction http://localhost:8080/health # 30 s, 50 connections, record the load model and correction setting oha -n 2000 -c 20 -m POST -H 'Content-Type: application/json' -d '{"q":1}' http://localhost:8080/api oha --no-tui -z 10s -c 10 --output-format json -o oha.json http://localhost:8080/ # scriptable output for CI or a report ``` ## Workflow 1. **Fix the question**: one command or endpoint, one metric (mean latency, p99, requests per second), one hypothesis. 1. **Control the machine**: close heavy processes, run on AC power, and pin versions; record CPU, OS, and tool versions in the report. 1. **Warm up and repea