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基準測試

使用此技能來測量效能基線、在 PR 前後偵測回歸問題,並比較不同技術方案的優劣。

基準測試 是什麼?

此基準測試技能提供一個結構化的框架,用於評估網頁、API、建置流程以及變更前後的效能表現。它支援多種執行「模式」:網頁效能(測量核心網頁指標如 LCP、CLS、FCP)、API 效能(百分位數延遲、負載測試)、建置效能(冷啟動、熱重載、測試時長),以及變更前後比較。對於任何使用 Claude Code、Cursor 或類似工具的開發者來說,這項技能至關重要,因為它能自動量化效能影響,將結果儲存在 `.ecc/benchmarks/` 的 JSON 檔案中,並與 CI 流程整合,藉由比較 PR 與已存基線的結果來防止回歸。其輸出包含詳細的對比表格,並會對超出目標閾值的項目標記警告,是維護生產級應用程式效能的不可或缺工具。

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

在你喜歡的 AI 中提問

開啟一個已預先載入此 Agent Skill 的新對話。

說明文件

基準測試 — 效能基線與回歸偵測

使用時機

  • 在 PR 前後測量效能影響
  • 為專案建立效能基線
  • 當使用者回報「感覺變慢了」
  • 在發布前,確保達到效能目標
  • 比較自家技術方案與其他替代方案的優劣

運作方式

模式 1:網頁效能

透過瀏覽器 MCP 測量真實瀏覽器指標:

1. 導覽至每個目標 URL
2. 測量核心網頁指標:
   - LCP (最大內容繪製) — 目標 < 2.5 秒
   - CLS (累計版面配置位移) — 目標 < 0.1
   - INP (與下一次繪製的互動) — 目標 < 200 毫秒
   - FCP (首次內容繪製) — 目標 < 1.8 秒
   - TTFB (第一個位元組的時間) — 目標 < 800 毫秒
3. 測量資源大小:
   - 總頁面重量 (目標 < 1 MB)
   - JS 套件大小 (目標 < 200 KB gzipped)
   - CSS 大小
   - 圖片重量
   - 第三方腳本重量
4. 計算網路請求數量
5. 檢查是否含有渲染阻塞資源

模式 2:API 效能

對 API 端點進行基準測試:

1. 對每個端點發送 100 次請求
2. 測量:p50、p95、p99 延遲
3. 追蹤:回應大小、狀態碼
4. 在負載下測試:10 個並發請求
5. 與 SLA 目標進行比較

模式 3:建置效能

測量開發反饋循環:

1. 冷建置時間
2. 熱重載時間 (HMR)
3. 測試套件執行時間
4. TypeScript 檢查時間
5. Lint 檢查時間
6. Docker 建置時間

模式 4:變更前後比較

在變更前後執行以測量影響:

/benchmark baseline    # 儲存當前指標
# ... 進行變更 ...
/benchmark compare     # 與基線進行比較

輸出:

| 指標 | 變更前 | 變更後 | 差異 | 結果 |
|--------|--------|-------|-------|---------|
| LCP | 1.2s | 1.4s | +200ms | 警告:需注意 |
| Bundle | 180KB | 175KB | -5KB | ✓ 改善 |
| Build | 12s | 14s | +2s | 警告:需注意 |

輸出

將基線儲存在 .ecc/benchmarks/ 目錄下的 JSON 檔案中。這些檔案會被 Git 追蹤,以便團隊共享基線。

整合

  • CI:在每個 PR 上執行 /benchmark compare
  • 搭配 /canary-watch 使用:進行部署後的監控
  • 搭配 /browser-qa 使用:完成完整的發布前檢查清單

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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