emiliodominguez/flow-skills

flow-benchmark

Performance measurement with a reproducible baseline and stated uncertainty.

查看源码

使用方法

  1. 复制安装命令,在自己的智能体环境安装前先检查源码包。
  2. 从原始文档核对所需工具、凭据和支持的宿主。
  3. 说明输入材料与期望交付物,并对照来源检查输出。
npx skills add https://github.com/emiliodominguez/flow-skills --skill flow-benchmark

开始前需了解

  • 安装不包含模型订阅、API 额度或访问文件的权限。
  • 仓库星数和安装量反映活跃情况,不等于独立质量或安全测试。
仓库原始内容

按源仓库内容呈现,保留标题、案例、代码、表格、链接以及原文引用的演示图片。

Benchmark

Define what "better" means before measuring. Keep raw results and compare like with like; never report a single attractive timing.

Process

  1. State metric, workload, target and the decision it informs. Define the timing boundary and

cache/process state: cold startup includes initialization; steady-state throughput may warm up. Keep cold and warm experiments separate.

  1. Record baseline revision, runtime/build settings, hardware/environment, fixtures,

concurrency, sample unit and tool/version. Time setup only when it is part of the metric; never warm away the behavior under investigation.

  1. Run baseline and candidate under controlled conditions, changing one variable per

experiment. Interleave A/B samples when drift matters; isolate data/state between samples.

  1. Collect enough observations for the claim. Report median, tails such as p95/p99 only when

sample size supports them, variance, outliers and known noise. No false precision, no unexplained outlier removal.

  1. Check tradeoffs in memory, CPU, responsiveness, bundle size and complexity. Profile when the

result needs an explanation. A lower score is not automatically a better user experience.

  1. Save the harness or command, raw results and comparison so others can rerun it. State

whether the effect is large relative to noise.

Anti-patterns

  • Warming a cold-start benchmark or excluding the initialization under investigation.
  • Comparing development and production builds as if only the implementation changed.
  • Reporting a best run or p99 from a handful of samples.
  • Claiming causation from timing alone.

Done when

  • Baseline and candidate share a relevant reproducible workload and timing boundary.
  • Raw results, uncertainty and meaningful regressions are visible.
  • The evidence supports an optimization decision or names what better measurement is needed.

Use flow-work or flow-refactor only for an optimization the evidence justifies.

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