Ultimate production readiness skill that orchestrates all relevant skills (frontend, backend, security, performance, SEO, testing, DevOps) to harden applications before deployment / Skill kesiapan produksi utama yang mengorkestrasi semua skill relevan (frontend, backend, keamanan, performa, SEO, testing, DevOps) untuk mengeraskan aplikasi sebelum deployment.
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The Production-Ready Hardener is a master orchestrator skill that combines and delegates to all relevant vibes-plug skills to ensure your application is rock-solid, secure, performant, and production-grade before deployment. It acts as a comprehensive pre-production checklist that leaves no stone unturned — from frontend polish to backend resilience, from data security to observability.
This skill does NOT replace individual skills — it coordinates them into a structured, phased audit-and-hardening workflow.
When this skill is triggered, execute the following 7-phase hardening process in order. Each phase produces findings and recommendations. At the end, compile a Production Readiness Report.
An automated Python scanner script is included at production_readiness_scanner.py to perform all check phases automatically. It automatically detects technology stacks (Vite, React 19, Supabase, Vitest, Playwright, ESLint) and parses local developer logs (tsc_errors.txt, eslint_output.txt, etc.) to calculate a live readiness score and compile an interactive, clickable markdown report (PRODUCTION_READINESS_REPORT.md).
Usage:
# Run basic static checklist checks on a project
python scripts/production_readiness_scanner.py /path/to/project
# Run active compilation, linting, and test execution diagnostics
python scripts/production_readiness_scanner.py /path/to/project --run-tsc --run-lint --run-tests --run-build
Production Readiness Report Format
After completing all 7 phases, compile a Production Readiness Report with:
# Production Readiness Report
## Executive Summary
Overall readiness score (0-100) with letter grade (A/B/C/D/F).
## Phase Scores
| Phase | Score | Critical Issues | Warnings |
|-------|-------|-----------------|----------|
| 1. Architecture & Code Quality | XX/100 | N | N |
| 2. Frontend Hardening | XX/100 | N | N |
| 3. Backend Hardening | XX/100 | N | N |
| 4. Security Hardening | XX/100 | N | N |
| 5. Testing & QA | XX/100 | N | N |
| 6. Performance & SEO | XX/100 | N | N |
| 7. DevOps & Deployment | XX/100 | N | N |
## 🔴 Critical Issues (Must Fix Before Production)
List of blockers that MUST be resolved.
## 🟡 Warnings (Should Fix)
List of important improvements.
## 🔵 Recommendations (Nice to Have)
List of enhancements for future iterations.
## Remediation Plan
Step-by-step action items ordered by priority.
Scoring Methodology
Each phase is scored 0-100 based on checklist completion:
Performa & SEO: performance-web-vitals, seo, app-analyzer-optimizer
Siklus Hidup & Tata Kelola: brainstorming, zero-to-prod-orchestrator, prd-architect, session-memory-manager, anti-slop
Deskripsi
Production-Ready Hardener adalah skill orkestrator utama yang menggabungkan dan mendelegasikan ke semua skill vibes-plug yang relevan untuk memastikan aplikasi Anda kokoh, aman, berperforma tinggi, dan siap produksi sebelum deployment. Skill ini berfungsi sebagai checklist pra-produksi komprehensif yang tidak meninggalkan celah — dari polish frontend hingga ketahanan backend, dari keamanan data hingga observability.
Skill ini TIDAK menggantikan skill individual — ia mengoordinasikan mereka ke dalam alur kerja audit-dan-pengerasan yang terstruktur dan bertahap.
Docker, CI/CD pipeline, OpenTelemetry, PostHog, Vercel/Cloudflare, state checkpoint & CHANGELOG
Protokol Eksekusi
Ketika skill ini dipicu, jalankan 7 fase pengerasan berurutan. Setiap fase menghasilkan temuan dan rekomendasi. Di akhir, compile sebuah Laporan Kesiapan Produksi.
FASE 1: Audit Arsitektur & Kualitas Kode
Struktur proyek mengikuti clean architecture
Prinsip SOLID diterapkan konsisten
Pelanggaran DRY diidentifikasi dan di-refactor
Kontrak API menggunakan desain spec-first (OpenAPI/GraphQL)
Error handling terstruktur dan konsisten (RFC 9457)
TypeScript strict mode aktif (strict: true), tanpa tipe any (typescript-expert)
Validasi input di semua batas API (Zod, Pydantic)
Tidak ada hardcoded value — semua konfigurasi via environment variable
Audit Anti-Slop Berdaulat Lolos — validasi wajib via node scripts/check-anti-slop.js --strict untuk menjamin 0 placeholder, 0 stub, 0 komentar sintaksis, dan 0 sampah debug (anti-slop)
Audit dependensi — semua paket up-to-date, tanpa CVE (dependency-upgrade-migrator)
FASE 2: Pengerasan Frontend
Server Components secara default
Suspense boundary dengan loading state bermakna
Error boundary di setiap segmen halaman
Validasi formulir ketat dengan React Hook Form + Zod (form-validation-expert)
Script scanner Python otomatis tersedia di production_readiness_scanner.py untuk menjalankan seluruh fase pemeriksaan secara otomatis. Scanner ini mendeteksi stack teknologi (Vite, React 19, Supabase, Vitest, Playwright, ESLint) serta mem-parsing file log error lokal (tsc_errors.txt, eslint_output.txt, dll) untuk mengkalkulasi skor kesiapan rilis dan menyusun laporan markdown interaktif (PRODUCTION_READINESS_REPORT.md).
Cara Penggunaan:
# Jalankan pemeriksaan checklist statis dasar pada proyek
python scripts/production_readiness_scanner.py /path/to/project
# Jalankan diagnostik aktif termasuk typecheck tsc, linter eslint, unit test, dan bundle build
python scripts/production_readiness_scanner.py /path/to/project --run-tsc --run-lint --run-tests --run-build
Metodologi Penilaian
Setiap fase dinilai 0-100 berdasarkan penyelesaian checklist:
Expert guide for intelligent model cascading and routing — complexity-scored task routing from Fast (Composer 2.5, Grok 4.7/4.6) to Medium (Gemini 4 Argon, GPT-5.6 Sol, Claude Opus 5.5) and High (Claude Sonnet 5.5, Claude Fable 5.1, Muse Wonder) / Panduan ahli untuk kaskade dan routing model cerdas.
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Expert guide for intelligent model cascading and routing — complexity-scored task routing from Flash/Haiku to Sonnet/Opus/Astra, dynamic escalation with quality gates, 40-60% token cost reduction while maintaining output quality / Panduan ahli untuk kaskade dan routing model cerdas — routing tugas berbasis skor kompleksitas dari Flash/Haiku ke Sonnet/Opus/Astra, eskalasi dinamis dengan gerbang kualitas, pengurangan biaya token 40-60% dengan kualitas output terjaga.
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Master guide for Affective Computing, emotional AI, and real-time sentiment analysis through native multimodal tokens (voice intonation and facial micro-expressions) / Panduan utama komputasi afektif, AI emosional, dan analisis sentimen real-time melalui token multimodal native.
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Expert guide for Affective Computing, emotional AI, and real-time sentiment analysis through native multimodal tokens (voice intonation and facial micro-expressions) / Panduan ahli komputasi afektif, AI emosional, dan analisis sentimen real-time melalui token multimodal native.
The English summary is being prepared. Showing the original description.