A2A agent protocol interoperability, Agent Cards, and MCP bridging / Protokol agen A2A, Agent Cards, dan integrasi MCP.
日本語の概要は準備中です。原文の説明を表示しています。
Methodological guide for the AI Agent to safely and systematically translate, refactor, and modernize giant legacy codebases (PHP, Python 2, old React) into modern stacks.
日本語の概要は準備中です。原文の説明を表示しています。
実行スコア 未検証
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
<a name="english"></a>
prd-architect (document reverse-engineered business rules into PRD.md), brainstorming, session-memory-manager.living-codebase-ast-graph (module dependencies & blast radius), scalability-clean-code (Clean Architecture boundaries), typescript-expert (strict type contracts).database-orm-expert (legacy SQL to Prisma/Drizzle schemas), js-backend-expert (Bun/Hono targets), go-programming-expert / rust-programming-expert.autonomous-tdd-debugger (shadow execution comparison suites), anti-slop (zero truncation, no stubbed mocks), zero-tech-debt-auditor.A systematic, risk-mitigated engineering methodology for migrating large, business-critical legacy codebases (PHP 5/7, Python 2, jQuery, old React Class Components, AngularJS) to modern 2026 enterprise stacks (Next.js 15, TypeScript 5.8+, Bun, Rust, Go) without business downtime or regression. Employs the Strangler Fig pattern, Shadow Execution Harnesses, and AST-driven module decomposition to prevent context exhaustion and hallucinated logic.
┌────────────────────────────────────────────────────────┐
│ 1. MAP & ISOLATE (AST Boundary Definition) │
│ Document I/O contracts in `migration_plan.md` │
└───────────────────────────┬────────────────────────────┘
│
▼
┌────────────────────────────────────────────────────────┐
│ 2. SHADOW HARNESS IMPLEMENTATION │
│ Run legacy and new code in parallel; log diffs │
└───────────────────────────┬────────────────────────────┘
│
▼
┌────────────────────────────────────────────────────────┐
│ 3. SIDE-BY-SIDE REFACTORING (Function-by-Function) │
│ Maintain old file while building typed modern clone │
└───────────────────────────┬────────────────────────────┘
│
▼
┌────────────────────────────────────────────────────────┐
│ 4. TRAFFIC CUTOVER & CLEANUP │
│ 100% parity verified ──► Deprecate legacy file │
└────────────────────────────────────────────────────────┘
// src/migration/shadow-harness.ts
export interface ShadowComparison<T> {
legacyOutput: T;
modernOutput: T;
isMatch: boolean;
discrepancies?: string[];
}
export async function executeWithShadowTesting<TInput, TOutput>(
input: TInput,
legacyFn: (data: TInput) => Promise<TOutput>,
modernFn: (data: TInput) => Promise<TOutput>,
onDiscrepancy?: (diff: ShadowComparison<TOutput>) => void
): Promise<TOutput> {
// 1. Run legacy function as ground truth for customer
const legacyPromise = legacyFn(input);
// 2. Execute modern implementation in shadow mode (non-blocking)
const modernPromise = modernFn(input).catch((err) => {
console.error('[Shadow-Harness] Modern function threw error:', err);
return null as unknown as TOutput;
});
const [legacyResult, modernResult] = await Promise.all([legacyPromise, modernPromise]);
// 3. Compare JSON representation for structural equality
const legacyJson = JSON.stringify(legacyResult);
const modernJson = JSON.stringify(modernResult);
if (legacyJson !== modernJson) {
const diffReport: ShadowComparison<TOutput> = {
legacyOutput: legacyResult,
modernOutput: modernResult,
isMatch: false,
};
if (onDiscrepancy) onDiscrepancy(diffReport);
console.warn('[Shadow-Harness] Discrepancy detected between legacy and modern outputs.');
}
// Always return the legacy result during the shadow probation phase
return legacyResult;
}
order-calc.legacy.php -> order-calc.service.ts).// ...rest of code).any, no phantom SDK imports).<a name="bahasa-indonesia"></a>
prd-architect (dokumentasi aturan bisnis ke PRD.md), brainstorming, session-memory-manager.living-codebase-ast-graph (dependensi modul & radius dampak), scalability-clean-code (batas Clean Architecture), typescript-expert (kontrak tipe ketat).database-orm-expert (SQL lama ke skema Prisma/Drizzle), js-backend-expert (target Bun/Hono), go-programming-expert / rust-programming-expert.autonomous-tdd-debugger (suite komparasi shadow execution), anti-slop (nol pemotongan kode, tanpa stub pura-pura), zero-tech-debt-auditor.Metodologi rekayasa sistematis dan aman bagi agen AI untuk memigrasikan basis kode warisan (legacy codebase) skala besar (PHP 5/7, Python 2, jQuery, React Class Component lawas) ke stack modern 2026 (Next.js 15, TypeScript 5.8+, Bun, Rust, Go) tanpa gangguan operasional (zero downtime) dan tanpa regresi logika bisnis. Menerapkan pola Strangler Fig, Shadow Execution, dan dekomposisi berbasis pohon sintaks (AST).
UserService.legacy.php -> user-service.ts) agar kode lama tetap menjadi acuan kebenaran.// TODO: sisa kode).any.// Membandingkan output kode lama vs baru secara real-time
const total = await executeWithShadowTesting(
cartData,
(cart) => hitungTotalLegacyPHP(cart),
(cart) => hitungTotalModernTS(cart),
(diff) => catatKeLogMonitoring(diff)
);
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A2A agent protocol interoperability, Agent Cards, and MCP bridging / Protokol agen A2A, Agent Cards, dan integrasi MCP.
日本語の概要は準備中です。原文の説明を表示しています。
Expert guide for automated and manual Web Accessibility (a11y) testing — axe-core, Pa11y, Playwright a11y, screen reader testing, and WCAG 2.2 Level AA/AAA compliance / Panduan ahli pengujian aksesibilitas web.
日本語の概要は準備中です。原文の説明を表示しています。
Web accessibility testing with axe-core, Playwright, and WCAG 2.2 / Pengujian aksesibilitas web axe-core, Playwright, dan WCAG 2.2.
日本語の概要は準備中です。原文の説明を表示しています。
Model cascading & complexity-scored routing (Grok, Gemini, Claude) / Routing kaskade model & scoring kompleksitas (Grok, Gemini, Claude).
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Affective computing, emotion AI, and multimodal sentiment analysis / Komputasi afektif, AI emosional, dan analisis sentimen multimodal.
日本語の概要は準備中です。原文の説明を表示しています。