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基准测试

使用此技能来测量性能基线、检测 PR 前后的回归,并比较不同技术栈的替代方案。

基准测试 是什么?

基准测试(Benchmark)技能旨在帮助开发者在项目开发周期中系统性地测量和追踪性能指标。它支持四种主要模式:页面性能(通过浏览器 MCP 测量核心 Web 指标,如 LCP、CLS、FCP 等)、API 性能(对端点进行 100 次调用以获取 p50、p95、p99 延迟)、构建性能(衡量冷启动、热重载、测试套件等时间),以及前后对比模式(/benchmark baseline 和 /benchmark compare 命令行)。此技能能够将基线存储为 JSON 文件(位于 .ecc/benchmarks/),并支持 Git 跟踪,方便团队共享历史数据。它适合在 CI/CD 流水线中使用,可与 /canary-watch(部署后监控)和 /browser-qa(发布前检查)等技能配合,形成完整的性能保障体系。输出格式为清晰的表格,包含指标、前后值、变化量和判定结果(更好/警告)。

兼容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/everything-claude-code/tree/main/skills/benchmark

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文档

基准测试——性能基线及回归检测

何时使用

  • 在 PR 前后测量性能影响
  • 为项目设定性能基线
  • 用户反馈“感觉变慢了”时
  • 上线前——确保满足性能目标
  • 将当前技术栈与替代方案进行比较

工作原理

模式 1:页面性能

通过浏览器 MCP 测量真实的浏览器指标:

1. 导航到每个目标 URL
2. 测量核心 Web 指标:
   - LCP(最大内容绘制)——目标 < 2.5s
   - CLS(累积布局偏移)——目标 < 0.1
   - INP(与下一次绘制的交互)——目标 < 200ms
   - FCP(首次内容绘制)——目标 < 1.8s
   - TTFB(首字节时间)——目标 < 800ms
3. 测量资源大小:
   - 页面总大小(目标 < 1MB)
   - JS 包大小(目标 < 200KB gzipped)
   - CSS 大小
   - 图片大小
   - 第三方脚本大小
4. 统计网络请求数
5. 检查渲染阻塞资源

模式 2:API 性能

对 API 端点进行基准测试:

1. 每个端点调用 100 次
2. 测量:p50、p95、p99 延迟
3. 跟踪:响应大小、状态码
4. 负载测试:10 个并发请求
5. 与 SLA 目标进行比较

模式 3:构建性能

测量开发反馈循环:

1. 冷启动构建时间
2. 热重载时间(HMR)
3. 测试套件运行时长
4. TypeScript 检查时间
5. 代码检查时间
6. Docker 构建时间

模式 4:前后对比

在变更前后运行以测量影响:

/benchmark baseline    # 保存当前指标
# ... 进行更改 ...
/benchmark compare     # 与基线对比

输出:

| 指标 | 之前 | 之后 | 变化 | 判定 |
|--------|--------|-------|-------|---------|
| LCP | 1.2s | 1.4s | +200ms | 警告:注意 |
| 包大小 | 180KB | 175KB | -5KB | ✓ 更优 |
| 构建 | 12s | 14s | +2s | 警告:注意 |

输出

将基线存储为 JSON 文件,位于 .ecc/benchmarks/ 目录下。这些文件由 Git 跟踪,以便团队共享基线。

集成

  • CI:在每个 PR 上运行 /benchmark compare
  • 与 /canary-watch 配合,用于部署后监控
  • 与 /browser-qa 配合,用于完整的发布前检查清单

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/claude-api

Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.

affaan-m/everything-claude-code

End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.

affaan-m/everything-claude-code

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/everything-claude-code-conventions

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/frontend-design

Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.

affaan-m/gget

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

affaan-m/literature-review

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

affaan-m/motion-ui

Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.

affaan-m/project-guidelines-example

Example project-specific skill template based on a real production application.

affaan-m/pubmed-database

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.

affaan-m/scholar-evaluation

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

affaan-m/uspto-database

USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

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