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benchmark (benchmark)

Use esta skill para medir linhas de base de desempenho, detectar regressões antes/depois de PRs e comparar alternativas de stack.

O que é benchmark (benchmark)?

Esta skill permite que desenvolvedores e agentes de IA executem benchmarks de desempenho de forma estruturada, abrangendo desde páginas web até APIs e processos de build. Suporta múltiplos modos de operação: Page Performance, que coleta métricas reais do navegador (como LCP, CLS, INP) via MCP do navegador; API Performance, que avalia latência (p50, p95, p99) e throughput de endpoints; e Build Performance, que mede tempos de compilação, recarga a quente (HMR) e suites de teste. O destaque é o modo Before/After Comparison, que salva uma linha de base e a compara com resultados posteriores, gerando uma tabela com deltas e vereditos visuais (WARNING ou BETTER). É útil para detectar regressões em pull requests, estabelecer metas de desempenho antes de lançamentos e validar otimizações. Integra-se perfeitamente com CI, permitindo execução automática em cada PR, e pode ser combinada com outras skills como canary-watch e browser-qa para uma verificação completa pré-lançamento. Os resultados são armazenados como JSON no diretório `.ecc/benchmarks/`, versionados pelo Git para compartilhamento entre a equipe.

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

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Documentação

Benchmark — Linha de Base de Desempenho e Detecção de Regressão

Quando Usar

  • Antes e depois de um PR para medir impacto no desempenho
  • Configurar linhas de base de desempenho para um projeto
  • Quando usuários reportam "está lento"
  • Antes de um lançamento — garantir que as metas de desempenho sejam cumpridas
  • Comparar sua stack com alternativas

Como Funciona

Modo 1: Desempenho de Página

Mede métricas reais do navegador via MCP do navegador:

1. Navegar para cada URL alvo
2. Medir Core Web Vitals:
   - LCP (Largest Contentful Paint) — alvo < 2,5s
   - CLS (Cumulative Layout Shift) — alvo < 0,1
   - INP (Interaction to Next Paint) — alvo < 200ms
   - FCP (First Contentful Paint) — alvo < 1,8s
   - TTFB (Time to First Byte) — alvo < 800ms
3. Medir tamanhos de recursos:
   - Peso total da página (alvo < 1MB)
   - Tamanho do bundle JS (alvo < 200KB gzipado)
   - Tamanho do CSS
   - Peso das imagens
   - Peso de scripts de terceiros
4. Contar requisições de rede
5. Verificar recursos que bloqueiam renderização

Modo 2: Desempenho de API

Realiza benchmarks de endpoints:

1. Acessar cada endpoint 100 vezes
2. Medir: latência p50, p95, p99
3. Rastrear: tamanho da resposta, códigos de status
4. Testar sob carga: 10 requisições concorrentes
5. Comparar com metas de SLA

Modo 3: Desempenho de Build

Mede o ciclo de feedback de desenvolvimento:

1. Tempo de build a frio
2. Tempo de recarga a quente (HMR)
3. Duração da suíte de testes
4. Tempo de verificação do TypeScript
5. Tempo de linting
6. Tempo de build Docker

Modo 4: Comparação Antes/Depois

Execute antes e depois de uma alteração para medir o impacto:

/benchmark baseline    # salva as métricas atuais
# ... faça as alterações ...
/benchmark compare     # compara com a linha de base

Saída:

| Métrica | Antes | Depois | Delta | Veridito |
|---------|-------|--------|-------|----------|
| LCP | 1,2s | 1,4s | +200ms | AVISO: WARN |
| Bundle | 180KB | 175KB | -5KB | ✓ MELHOR |
| Build | 12s | 14s | +2s | AVISO: WARN |

Saída

Armazena linhas de base em .ecc/benchmarks/ como JSON. Rastreado pelo Git para que a equipe compartilhe as linhas de base.

Integração

  • CI: execute /benchmark compare em cada PR
  • Combine com /canary-watch para monitoramento pós-implantação
  • Combine com /browser-qa para checklist completa pré-lançamento

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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