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基准测试

使用此技能测量性能基线、检测 PR 前后的性能回退,并比较技术栈的替代方案。

基准测试 是什么?

该技能提供了一套全面的基准测试工具,用于在软件开发中测量性能基线、检测回归并比较替代方案。它支持四种模式:页面性能(通过 browser MCP 测量核心网页指标和资源大小)、API 性能(测量延迟百分位数和负载下的请求)、构建性能(测量冷启动、热重载和测试持续时间)以及前后对比(通过命令行界面保存基线并比较变化)。基线存储在 `.ecc/benchmarks/` 目录中的 JSON 文件中,支持 Git 跟踪以促进团队协作。它与 CI 流水线集成,可以在每次 PR 上自动进行比较,并与 /canary-watch(用于部署后监控)和 /browser-qa(用于发布前检查清单)等其他技能配合使用。

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

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打开一个已预加载此 Agent Skill 的新对话。

文档

基准测试 — 性能基线 & 回归检测

何时使用

  • PR 前后测量性能影响
  • 为项目设置性能基线
  • 当用户反馈“感觉变慢了”时
  • 发布前确保满足性能目标
  • 将你的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 browser MCP 测量真实的浏览器指标:

1. 导航到每个目标 URL
2. 测量核心网页指标:
   - LCP(最大内容绘制)— 目标 < 2.5s
   - CLS(累积布局偏移)— 目标 < 0.1
   - INP(下次绘制交互)— 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:
   - 总页面权重(目标 < 1MB)
   - JS 包大小(目标 < 200KB gzipped)
   - CSS 大小
   - 图片权重
   - 第三方脚本权重
4. 统计网络请求数量
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. 每个端点执行 100 次请求
2. 测量:p50、p95、p99 延迟
3. 跟踪:响应大小、状态码
4. 在负载下测试: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. 冷构建时间
2. 热重载时间(HMR)
3. 测试套件持续时间
4. TypeScript 检查时间
5. Lint 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
# ... 进行变更 ...
/benchmark compare     # 与基线进行比较

输出:

| 指标 | 前 | 后 | 差值 | 判定 |
|--------|--------|-------|-------|---------|
| LCP | 1.2s | 1.4s | +200ms | 警告 |
| Bundle | 180KB | 175KB | -5KB | ✓ 更好 |
| Build | 12s | 14s | +2s | 警告 |

输出

将基线存储在 .ecc/benchmarks/ 目录下的 JSON 文件中。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 配合使用进行部署后监控
  • 与 /browser-qa 配合使用完成发布前完整检查清单

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/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/ECC

Multi-platform content distribution across X, LinkedIn, Threads, and Bluesky. Adapts content per platform using content-engine patterns. Never posts identical content cross-platform. Use when the user wants to distribute content across social platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

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.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

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