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。
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。
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。
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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.
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).
Activate this skill when:
thinking: { type: "enabled", budget_tokens: N }).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
| Profile | Token Budget | Target Workload | Frontier Model Mapping |
|---|---|---|---|
speed | 1,024 tokens | AST queries, documentation lookup, typos, single unit tests | Gemini 4 Flash / Claude 3.5 Haiku |
balanced | 8,192 tokens | Feature implementation, component assembly, standard CRUD | Claude 3.7 Sonnet / Gemini 4 Pro |
deep | 32,768 tokens | Architecture synthesis, cross-domain migrations, security audits | Claude 5.1 Sonnet / Gemini 4 Pro |
exhaustive | 65,536 tokens | Cryptographic verification, SMT invariant proving, distributed sagas | OpenAI o3 / Gemini 4 Pro Deep Think |
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 {};
}
}
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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.
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).
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 };
}
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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。
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。
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。
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。
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。
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。