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벤치마크 — 성능 기준 & 회귀 감지

이 스킬을 사용하여 성능 기준선을 측정하고, PR 전후의 회귀를 감지하며, 스택 대안을 비교하세요.

벤치마크 — 성능 기준 & 회귀 감지란 무엇인가요?

이 스킬은 성능 기준선 설정, PR 전/후 회귀 감지, 스택 대안 비교를 위한 포괄적인 벤치마킹 도구 역할을 합니다. 브라우저 MCP를 통해 실제 브라우저 지표(Core Web Vitals), API 성능(p50/p95/p99 지연 시간), 빌드 성능(콜드 빌드, HMR, 테스트 스위트, TypeScript 검사 등)을 측정하는 네 가지 주요 모드를 지원합니다. `/benchmark baseline` 및 `/benchmark compare` 명령어를 통해 변경 전후를 비교하고, 회귀 여부를 명확히 표시합니다. 결과는 Git 추적되는 `.ecc/benchmarks/` 폴더에 JSON으로 저장되어 팀 공유가 가능하며, CI와 통합하거나 `/canary-watch`, `/browser-qa` 같은 다른 스킬과 함께 사용할 수 있습니다. Cursor, Claude Code 등 AI 에이전트 도구와 함께 사용하기에 적합합니다.

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

즐겨 사용하는 AI에게 물어보기

이 에이전트 스킬이 미리 로드된 새 채팅을 엽니다.

문서

벤치마크 — 성능 기준선 및 회귀 감지

사용 시점

  • PR 전후로 성능 영향을 측정할 때
  • 프로젝트의 성능 기준선을 설정할 때
  • 사용자가 "느리다"고 보고할 때
  • 출시 전 — 성능 목표를 충족하는지 확인할 때
  • 현재 스택을 대안과 비교할 때

작동 방식

모드 1: 페이지 성능

브라우저 MCP를 통해 실제 브라우저 지표를 측정합니다:

1. 각 대상 URL로 이동
2. Core Web Vitals 측정:
   - LCP (Largest Contentful Paint) — 목표 < 2.5초
   - CLS (Cumulative Layout Shift) — 목표 < 0.1
   - INP (Interaction to Next Paint) — 목표 < 200ms
   - FCP (First Contentful Paint) — 목표 < 1.8초
   - TTFB (Time to First Byte) — 목표 < 800ms
3. 리소스 크기 측정:
   - 전체 페이지 무게 (목표 < 1MB)
   - JS 번들 크기 (목표 < 200KB gzipped)
   - CSS 크기
   - 이미지 무게
   - 타사 스크립트 무게
4. 네트워크 요청 수 계산
5. 렌더링 차단 리소스 확인

모드 2: API 성능

API 엔드포인트를 벤치마킹합니다:

1. 각 엔드포인트를 100번 호출
2. 측정: p50, p95, p99 지연 시간
3. 추적: 응답 크기, 상태 코드
4. 부하 테스트: 10개의 동시 요청
5. SLA 목표와 비교

모드 3: 빌드 성능

개발 피드백 루프를 측정합니다:

1. 콜드 빌드 시간
2. 핫 리로드 시간 (HMR)
3. 테스트 스위트 실행 시간
4. TypeScript 검사 시간
5. 린트 시간
6. Docker 빌드 시간

모드 4: 변경 전후 비교

변경 전후를 실행하여 영향을 측정합니다:

/benchmark baseline    # 현재 지표 저장
# ... 변경 사항 적용 ...
/benchmark compare     # 기준선과 비교

출력:

| 지표 | 변경 전 | 변경 후 | 차이 | 판정 |
|--------|--------|-------|-------|---------|
| LCP | 1.2s | 1.4s | +200ms | 경고: 경고 |
| 번들 | 180KB | 175KB | -5KB | ✓ 개선 |
| 빌드 | 12s | 14s | +2s | 경고: 경고 |

출력

기준선을 .ecc/benchmarks/ 디렉토리에 JSON으로 저장합니다. Git으로 추적되어 팀이 기준선을 공유할 수 있습니다.

통합

  • CI: 모든 PR에 대해 /benchmark compare 실행
  • 배포 후 모니터링을 위해 /canary-watch와 함께 사용
  • 출시 전 전체 점검 목록을 위해 /browser-qa와 함께 사용

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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