kv-cache-prefix-optimizer — vibes-plug Skill

English | Bahasa Indonesia

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English

Orchestration & Integration

Connects and orchestrates with brainstorming, zero-to-prod-orchestrator, frontier-ai-models-expert, llm-finops-router, gemini-agent-booster, and vercel-ai-sdk-expert to structure multi-turn prompts for maximum KV-cache reuse, slashing token billing by up to 80% and reducing Time-to-First-Token (TTFT) by up to 10x.

Description

Production guide for engineering deterministic prompt prefixes, stabilizing KV-cache boundaries, and optimizing context reuse across frontier models (Google Gemini 4 Pro / 3.x context caching, Anthropic Claude 5.5 prompt caching, and OpenAI prefix caching). Delivers architectures that guarantee 90%+ cache hit rates through canonical tool definition sorting, stable JSON serialization, and dynamic context partitioning.

Trigger Conditions

Activate this skill when:

• Operating large-context agents (>100k tokens) where recurring prompt costs dominate operational budgets.
• Experiencing high Time-to-First-Token (TTFT) latencies in interactive chat or agentic coding loops.
• Configuring model caching primitives (cache_control: { type: 'ephemeral' } or Gemini explicit cache tokens).
• Architecting system instructions, tool schemas, and workspace context maps for multi-turn sessions.

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Core Concepts & Patterns

1. The Anatomy of a High-Cache Prompt

Modern frontier models utilize prefix-matching algorithms. Even a single changed byte (such as a timestamp, random ID, or altered tool order) invalidates all subsequent cached tokens:

[STATIC PREFIX: CACHE PINNED (90%+ HIT RATE)]
  ├── System Directives & Behavioral Guardrails (Immutable)
  ├── 140-Skill Ecosystem Registry (Canonical Alphabetical Order)
  ├── Tool Definitions (Strictly Sorted Keys & Parameters)
  └── Workspace Schema & Architecture Map (Immutable per Session)
------------------------ [CACHE BOUNDARY PIN] ------------------------
[DYNAMIC SUFFIX: VOLATILE PER TURN]
  ├── Ephemeral Turn Timestamp & User Message
  └── Real-Time Tool Execution Results

2. Deterministic Prompt & Cache Pinning (TypeScript Implementation)

export interface ToolDefinition {
  name: string;
  description: string;
  parameters: Record<string, unknown>;
}

export function serializeCanonicalJson(obj: unknown): string {
  if (obj === null || typeof obj !== 'object') {
    return JSON.stringify(obj);
  }
  if (Array.isArray(obj)) {
    return `[${obj.map((item) => serializeCanonicalJson(item)).join(',')}]`;
  }
  const sortedKeys = Object.keys(obj as Record<string, unknown>).sort();
  const entries = sortedKeys.map(
    (key) => `${JSON.stringify(key)}:${serializeCanonicalJson((obj as Record<string, unknown>)[key])}`
  );
  return `{${entries.join(',')}}`;
}

export function sortToolsCanonically(tools: ToolDefinition[]): ToolDefinition[] {
  return [...tools].sort((a, b) => a.name.localeCompare(b.name));
}

export interface PromptPayload {
  systemPrompt: string;
  cachedContextBlock: {
    type: 'text';
    text: string;
    cache_control: { type: 'ephemeral' };
  };
  messages: Array<{ role: 'user' | 'assistant'; content: string }>;
}

export function constructCacheOptimizedPayload(
  systemDirectives: string,
  staticWorkspaceContext: string,
  tools: ToolDefinition[],
  userTurns: Array<{ role: 'user' | 'assistant'; content: string }>
): PromptPayload {
  const sortedTools = sortToolsCanonically(tools);
  const canonicalToolSpecs = serializeCanonicalJson(sortedTools);

  const staticPinnedBlock = [
    '=== IMMUTABLE SYSTEM CONTEXT ===',
    systemDirectives,
    '=== CANONICAL TOOL REGISTRY ===',
    canonicalToolSpecs,
    '=== PROJECT ARCHITECTURAL MAP ===',
    staticWorkspaceContext
  ].join('\n\n');

  return {
    systemPrompt: 'You are an advanced sovereign AI coding agent operating under strict anti-slop guidelines.',
    cachedContextBlock: {
      type: 'text',
      text: staticPinnedBlock,
      cache_control: { type: 'ephemeral' }
    },
    messages: userTurns
  };
}

3. Cache Performance & Telemetry Targets

Metric	Unoptimized Baseline	KV-Cache Optimized Target	Improvement Factor
Cache Hit Ratio	0% - 20%	90% - 98%	4.5x - 5x higher
Time-to-First-Token (TTFT)	3,200ms - 5,500ms	250ms - 450ms	~10x faster response
Prompt Token Cost	100% full rate	20% - 25% discounted rate	75% - 80% cost reduction

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Best Practices

1. Never Insert Timestamps in System Prompts: Place dates or timestamps exclusively in the final volatile user message, never in the static system prompt.
2. Sort Tool Definitions Alphabetically: Always sort tool names and their JSON schema parameter keys canonically before sending requests.
3. Partition Dynamic Context to the Tail: Ensure all large documentation and code context maps appear before volatile conversation history.
4. Monitor Cache Read Discrepancies: Log usage.prompt_tokens_details.cached_tokens in application telemetry to catch accidental prefix cache busts immediately.

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Common Pitfalls to Avoid

Anti-Pattern	Operational Consequence	Engineering Remedy
Dynamic session IDs embedded in the system prompt	100% cache invalidation on every user session	Relocate session IDs to application state headers
Randomized order of tools from Object.values(tools)	Non-deterministic JSON payload busts KV cache	Sort tools alphabetically by name and schema keys
Modifying instruction wording midway through conversation	Re-reads entire 100k+ token context at full price	Keep static prompt templates strictly versioned and immutable

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Integration with Other Skills (MANDATORY)

• frontier-ai-models-expert — Align with caching specs across Gemini 4 Pro, Claude 5.5, and GPT Astra 6.
• llm-finops-router — Calculate financial savings and route cache-heavy workloads to optimal endpoints.
• gemini-agent-booster — Exploit Google Cloud native 2M context caching mechanisms.
• vercel-ai-sdk-expert — Implement provider options for Anthropic and Google cache controls.

Referenced By Orchestrators (MANDATORY)

• brainstorming — Added to "Integrasi AI & LLM" and "Frontier AI & Simulation" matrix rows.
• zero-to-prod-orchestrator — Integrated in Phase 4 (Backend APIs, Microservices & AI Agents).

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Bahasa Indonesia

Integrasi Orkestrasi

Terhubung dan mengorkestrasi dengan brainstorming, zero-to-prod-orchestrator, frontier-ai-models-expert, llm-finops-router, gemini-agent-booster, dan vercel-ai-sdk-expert untuk menyusun prompt multi-turn dengan efisiensi KV-cache maksimal, menghemat biaya token hingga 80% dan memangkas waktu respons awal (TTFT) hingga 10x lebih cepat.

Deskripsi

Panduan produksi untuk merekayasa awalan (prefix) prompt yang deterministik, mengunci batas KV-cache, dan memaksimalkan penggunaan ulang konteks pada model frontier (Google Gemini 4 Pro / 3.x context caching, Anthropic Claude 5.5 prompt caching, dan OpenAI prefix caching). Memberikan arsitektur yang menjamin tingkat keberhasilan cache (cache hit rate) di atas 90% melalui pengurutan definisi tool kanonikal, serialisasi JSON stabil, dan partisi konteks dinamis.

Kondisi Pemicu

Aktifkan skill ini ketika:

• Mengelola agen berkonteks besar (>100k token) dengan biaya input token berulang yang signifikan.
• Mengalami latensi tinggi pada Time-to-First-Token (TTFT) saat percakapan atau coding loop interaktif.
• Mengonfigurasi parameter caching model (cache_control: { type: 'ephemeral' } atau Gemini explicit caching).
• Merancang instruksi sistem, skema tool, dan peta repositori untuk sesi multi-turn.

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Konsep Inti & Pola Praktik

1. Anatomi Prompt Ber-Cache Tinggi

Model frontier mencocokkan token awalan secara persis (prefix matching). Perubahan satu karakter saja di awal prompt akan merusak seluruh cache setelahnya:

• Blok Statis (Terkunci): Instruksi sistem, daftar 140 skill, skema tool terurut, dan peta arsitektur proyek.
• Titik Kunci Cache (Cache Pin): Menandai akhir blok statis dengan header kontrol cache.
• Blok Volatil (Dinamis): Riwayat percakapan terkini dan hasil eksekusi tool giliran terakhir.

2. Serialisasi JSON Kanonikal

Kunci objek JSON wajib diurutkan secara alfabetis sebelum dikirimkan ke model agar representasi string biner tidak berubah antar permintaan.

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Praktik Terbaik

1. Jangan Taruh Timestamp di System Prompt: Masukkan waktu atau tanggal hanya pada pesan user paling akhir, bukan pada blok instruksi utama.
2. Urutkan Definisi Tool Secara Alfabetis: Selalu lakukan sorting pada daftar nama fungsi dan parameter tool.
3. Posisikan Data Dinamis di Bagian Akhir: Taruh seluruh dokumen referensi dan kode sebelum giliran pesan pengguna.
4. Pantau Metrik Cache: Pantau token yang terbaca dari cache melalui telemetri guna mendeteksi jika terjadi cache bust yang tidak disengaja.

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Jebakan Umum yang Harus Dihindari

Praktik Buruk	Dampak Buruk	Solusi Rekayasa
Menyisipkan ID sesi acak ke dalam system prompt	Cache batal 100% pada setiap sesi baru	Pindahkan ID sesi ke header aplikasi di luar prompt
Urutan tool berubah-ubah dari Object.values(tools)	Representasi string berubah dan membatalkan KV cache	Urutkan tool secara alfabetis berdasarkan namanya
Mengubah redaksi instruksi di tengah percakapan	Membaca ulang 100k+ token dengan harga penuh	Kunci template instruksi dalam keadaan statis dan terversi

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Integrasi dengan Skill Lain (WAJIB)

• frontier-ai-models-expert — Sinkronisasi spesifikasi caching pada Gemini 4 Pro, Claude 5.5, dan GPT Astra 6.
• llm-finops-router — Menghitung efisiensi anggaran dan mengarahkan beban kerja ber-cache tinggi.
• gemini-agent-booster — Memanfaatkan mekanisme context caching native Google Cloud 2M.
• vercel-ai-sdk-expert — Mengimplementasikan opsi provider untuk kontrol caching Anthropic dan Google.

Direferensikan oleh Orchestrator (WAJIB)

• brainstorming — Tambahkan ke baris "Integrasi AI & LLM" dan "Frontier AI & Simulation" pada Matriks Orkestrasi.
• zero-to-prod-orchestrator — Tambahkan ke Fase 4 (Backend APIs, Microservices & AI Agents).