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benchmark

Utilisez cette compétence pour mesurer des références de performance, détecter des régressions avant/après des PR et comparer des alternatives de stack.

Qu'est-ce que benchmark ?

Cette compétence offre un cadre complet pour évaluer les performances de pages web, d'API, de builds et d'applications entières. Elle permet de mesurer des métriques clés comme les Core Web Vitals (LCP, CLS, INP, FCP, TTFB), la latence des API (p50, p95, p99), les temps de build et de rechargement à chaud (HMR). La compétence prend en charge les modes 'Avant/Après' pour quantifier l'impact exact des changements de code, stockant les résultats dans un fichier JSON versionné (`.ecc/benchmarks/`). Elle s'intègre facilement dans une pipeline CI pour détecter automatiquement les régressions sur chaque Pull Request. Utile pour tout développeur souhaitant valider des améliorations ou justifier des décisions techniques avec des données objectives.

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

Benchmark — Référence de performance et détection de régression

Quand l'utiliser

  • Avant et après une PR pour mesurer l'impact sur les performances
  • Mise en place de références de performance pour un projet
  • Lorsque les utilisateurs signalent "c'est lent"
  • Avant un lancement — assurez-vous d'atteindre les objectifs de performance
  • Comparer votre stack par rapport à des alternatives

Comment ça fonctionne

Mode 1 : Performance des pages

Mesure les métriques réelles du navigateur via le MCP navigateur :

1. Naviguer vers chaque URL cible
2. Mesurer les Core Web Vitals :
   - LCP (Largest Contentful Paint) — cible < 2,5 s
   - CLS (Cumulative Layout Shift) — cible < 0,1
   - INP (Interaction to Next Paint) — cible < 200 ms
   - FCP (First Contentful Paint) — cible < 1,8 s
   - TTFB (Time to First Byte) — cible < 800 ms
3. Mesurer la taille des ressources :
   - Poids total de la page (cible < 1 Mo)
   - Taille du bundle JS (cible < 200 Ko gzippé)
   - Taille du CSS
   - Poids des images
   - Poids des scripts tiers
4. Compter les requêtes réseau
5. Vérifier les ressources bloquant le rendu

Mode 2 : Performance des API

Évalue les endpoints d'API :

1. Frapper chaque endpoint 100 fois
2. Mesurer : latence p50, p95, p99
3. Suivre : taille de la réponse, codes de statut
4. Tester sous charge : 10 requêtes concurrentes
5. Comparer avec les objectifs SLA

Mode 3 : Performance du build

Mesure la boucle de feedback de développement :

1. Temps de build à froid
2. Temps de rechargement à chaud (HMR)
3. Durée de la suite de tests
4. Temps de vérification TypeScript
5. Temps de linting
6. Temps de build Docker

Mode 4 : Comparaison Avant/Après

Exécutez avant et après un changement pour mesurer l'impact :

/benchmark baseline    # enregistre les métriques actuelles
# ... apporter des modifications ...
/benchmark compare     # compare avec la baseline

Résultat :

| Métrique | Avant | Après | Écart | Verdict |
|----------|-------|-------|-------|---------|
| LCP | 1,2 s | 1,4 s | +200 ms | AVERTISSEMENT : ATTENTION |
| Bundle | 180 Ko | 175 Ko | -5 Ko | ✓ MEILLEUR |
| Build | 12 s | 14 s | +2 s | AVERTISSEMENT : ATTENTION |

Sortie

Stocke les baselines dans .ecc/benchmarks/ au format JSON. Suivi par Git pour que l'équipe partage les baselines.

Intégration

  • CI : exécutez /benchmark compare sur chaque PR
  • Associer à /canary-watch pour une surveillance post-déploiement
  • Associer à /browser-qa pour une checklist complète avant mise en production

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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