本文へ移動
cccskills
無料GitHub で公開

thinking-budget-orchestrator

Expert guide for dynamic thinking budget allocation, test-time reasoning scaling (Gemini 4 Pro, Claude 3.7/5.1 Extended Thinking, OpenAI o3/o4-mini), and task complexity heuristics / Panduan ahli alokasi budget penalaran dinamis dan penskalaan reasoning test-time.

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。

連携ツールOpenAIGemini
インストール方法を見る

含まれるファイル(1)

  • SKILL.md7.3 KB

SKILL.md(原文)

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。

thinking-budget-orchestrator — vibes-plug Skill

English | Bahasa Indonesia


<a name="english"></a>

English

Orchestration & Integration

Connects and orchestrates with test-time-compute-optimizer, adaptive-model-cascade, frontier-ai-models-expert, gemini-agent-booster, llm-finops-router, multi-agent-orchestration, and zero-to-prod-orchestrator to enforce optimal cognitive budget allocation across AI tasks.

Description

Production architecture for dynamically allocating and controlling reasoning thinking tokens (Extended Thinking / Test-Time Compute) across frontier LLMs (Gemini 4 Pro/Flash, Claude 3.7/5.1 Sonnet/Opus, OpenAI o3/o4-mini). Instead of static uniform token limits that either starve complex mathematical verifications or waste massive token budgets on simple queries, this skill evaluates structural task signals (AST blast radius, security boundaries, multi-agent topologies) and applies calibrated reasoning envelopes: speed (1K tokens), balanced (8K tokens), deep (32K tokens), and exhaustive (64K tokens).

Trigger Conditions

Activate this skill when:

  • Designing prompt chains or agents utilizing models with thinking budget controls (e.g. thinking: { type: "enabled", budget_tokens: N }).
  • Running multi-agent swarms where leaf worker agents require lightweight budgets while root critic agents require deep reasoning.
  • Optimizing API costs and P99 latency on enterprise agent pipelines.
  • Automating verification for high-consequence tasks (FinTech billing, cryptographic state machines, security audits).

Core Concepts & Reasoning Profiles

TASK INGESTION
      │
      ▼
┌───────────────────────────┐
│ HEURISTIC COMPLEXITY AST  │  Extract signals: blast radius, keywords, dependencies
└─────────────┬─────────────┘
              │
  ┌───────────┼───────────┬───────────┐
  ▼           ▼           ▼           ▼
SPEED     BALANCED      DEEP      EXHAUSTIVE
(1,024)    (8,192)    (32,768)     (65,536)
  │           │           │           │
Simple typo  Fullstack  Security    Formal math /
/ lint fixes  feature   audit/fuzz  SMT proving

1. Token Budget Allocation Matrix

ProfileToken BudgetTarget WorkloadFrontier Model Mapping
speed1,024 tokensAST queries, documentation lookup, typos, single unit testsGemini 4 Flash / Claude 3.5 Haiku
balanced8,192 tokensFeature implementation, component assembly, standard CRUDClaude 3.7 Sonnet / Gemini 4 Pro
deep32,768 tokensArchitecture synthesis, cross-domain migrations, security auditsClaude 5.1 Sonnet / Gemini 4 Pro
exhaustive65,536 tokensCryptographic verification, SMT invariant proving, distributed sagasOpenAI o3 / Gemini 4 Pro Deep Think

2. Native Multi-Provider Payload Generation (TypeScript)

export interface ThinkingConfig {
  profile: 'speed' | 'balanced' | 'deep' | 'exhaustive';
  budgetTokens: number;
}

export const BUDGET_TOKENS: Record<string, number> = {
  speed: 1024,
  balanced: 8192,
  deep: 32768,
  exhaustive: 65536
};

export function resolveThinkingBudget(task: string, userOverride?: string): ThinkingConfig {
  const profile = (userOverride || detectComplexity(task)) as ThinkingConfig['profile'];
  return {
    profile,
    budgetTokens: BUDGET_TOKENS[profile] || BUDGET_TOKENS.balanced
  };
}

function detectComplexity(task: string): string {
  const lower = task.toLowerCase();
  if (/(invariant|formal|pqc|smt|z3|crypto|zero-day|exploit)/.test(lower)) return 'exhaustive';
  if (/(security|audit|red-team|refactor|migration|payment|gateway|doku|stripe)/.test(lower)) return 'deep';
  if (/(typo|lint|format|doc|readme|comment)/.test(lower)) return 'speed';
  return 'balanced';
}

export function formatProviderThinkingPayload(provider: 'gemini' | 'claude' | 'openai', config: ThinkingConfig) {
  switch (provider) {
    case 'claude':
      return {
        thinking: {
          type: 'enabled',
          budget_tokens: config.budgetTokens
        }
      };
    case 'gemini':
      return {
        generationConfig: {
          thinkingConfig: {
            thinkingBudget: config.budgetTokens
          }
        }
      };
    case 'openai':
      return {
        reasoning_effort: config.budgetTokens >= 32768 ? 'high' : config.budgetTokens >= 8192 ? 'medium' : 'low'
      };
    default:
      return {};
  }
}

<a name="bahasa-indonesia"></a>

Bahasa Indonesia

Integrasi Orkestrasi

Terhubung dan mengorkestrasi skill test-time-compute-optimizer, adaptive-model-cascade, frontier-ai-models-expert, gemini-agent-booster, llm-finops-router, multi-agent-orchestration, dan zero-to-prod-orchestrator untuk menegakkan alokasi anggaran penalaran optimal di seluruh aktivitas agen AI.

Deskripsi

Arsitektur produksi untuk mengalokasikan dan mengendalikan reasoning thinking tokens (Extended Thinking / Test-Time Compute) secara dinamis di seluruh LLM frontier (Gemini 4 Pro/Flash, Claude 3.7/5.1, OpenAI o3/o4-mini). Daripada menggunakan batas token statis seragam yang membuang biaya komputasi atau membatasi penalaran mendalam pada sistem kritis, skill ini mengevaluasi sinyal struktural tugas dan menerapkan profil reasoning terkalibrasi: speed (1K token), balanced (8K token), deep (32K token), dan exhaustive (64K token).

Kondisi Pemicu

  • Merancang alur agen yang memanfaatkan kontrol thinking budget native API.
  • Menjalankan swarm multi-agen di mana subagen pelaksana membutuhkan budget ringan sedangkan agen auditor membutuhkan budget mendalam.
  • Mengoptimalkan efisiensi biaya token dan latensi P99 pada pipeline AI enterprise.
  • Memvalidasi logika berisiko tinggi (state machine finansial, billing SaaS, kriptografi).

Implementasi Pola Kunci Alokasi Budget Penalaran (TypeScript)

export interface KonfigurasiPenalaran {
  profil: 'speed' | 'balanced' | 'deep' | 'exhaustive';
  budgetTokens: number;
}

export function tentukanBudgetPenalaran(deskripsiTugas: string): KonfigurasiPenalaran {
  const teks = deskripsiTugas.toLowerCase();
  
  // 1. Tugas formal matematika/kriptografi: Berikan budget maksimal
  if (/(invarian|formal|pqc|smt|z3|kriptografi)/.test(teks)) {
    return { profil: 'exhaustive', budgetTokens: 65536 };
  }
  // 2. Audit keamanan, payment, dan migrasi arsitektur: Budget mendalam
  if (/(keamanan|audit|red-team|refaktor|migrasi|payment|doku|stripe)/.test(teks)) {
    return { profil: 'deep', budgetTokens: 32768 };
  }
  // 3. Perbaikan typo, formatting, dan dokumentasi: Budget super cepat
  if (/(typo|lint|format|dokumentasi|readme)/.test(teks)) {
    return { profil: 'speed', budgetTokens: 1024 };
  }
  // 4. Default pengembangan fitur fullstack: Budget seimbang
  return { profil: 'balanced', budgetTokens: 8192 };
}

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

コメント

ログインなしで書き込めます。使ってみた感想、質問、動かなかった環境などを気軽にどうぞ。

まだコメントはありません。最初のコメントを書いてみませんか。

送信するとすぐに公開されます。メールアドレスや電話番号などの個人情報は書かないでください。URL は 2 つまでです。

0 / 1000 文字

このフォームは reCAPTCHA で保護されており、Google のプライバシーポリシーと利用規約が適用されます。

同じリポジトリのスキル

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。

roedyrustam/vibes-plug772026年10月18日 更新

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。

roedyrustam/vibes-plug772026年10月18日 更新

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.

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。

roedyrustam/vibes-plug772026年10月18日 更新

Expert guide for AI-assisted coding workflows — agentic code generation, multi-agent code swarms, self-healing CI/CD, automated PR review, spec-to-code pipelines, codebase knowledge graphs, and human-in-the-loop approval gates / Panduan ahli untuk workflow pengkodean berbasis AI — generasi kode agentic, code swarm multi-agen, CI/CD self-healing, review PR otomatis, pipeline spec-to-code, knowledge graph codebase, dan gate persetujuan human-in-the-loop.

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。

roedyrustam/vibes-plug772026年10月18日 更新

Expert guide for autonomous Machine-to-Machine (M2M) agent settlement, dual-rail fiat & stablecoin micro-clearing, dynamic budget envelopes, and cryptographic agent invoicing / Panduan ahli settlement otonom antar-agen (M2M), kliring mikro dual-rail fiat & stablecoin, pagu anggaran dinamis, dan penagihan agen kriptografis.

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。

roedyrustam/vibes-plug772026年10月18日 更新

Expert guide for long-term episodic memory integration (Mem0 v2, Letta/MemGPT, Zep v2), memory tier architecture, pgvector HNSW storage, and unified context management for autonomous AI agents / Panduan ahli untuk integrasi memori episodik jangka panjang (Mem0 v2, Letta/MemGPT, Zep v2), arsitektur tier memori, penyimpanan pgvector HNSW, dan manajemen konteks terpadu untuk agen AI otonom.

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。

roedyrustam/vibes-plug772026年10月18日 更新

roedyrustam のスキルをすべて見る

このスキルの問題を報告する