跳到正文
cccskills
免费在 GitHub 公开

frontier-ai-models-expert

Expert guide for late-2026 frontier AI models — Grok 4.7 / 4.6 (High Fast), Cursor Composer 2.5 (Fast), Claude Opus 5.5 / GPT-5.6 Sol / Gemini 4 Argon (Medium), and Claude Sonnet 5.5 / Fable 5.1 / Muse Wonder (High) / Panduan ahli model AI frontier akhir-2026.

中文简介正在准备中。当前显示原文说明。

集成工具VercelOpenAIGemini
查看安装方法

包含的文件(1)

  • SKILL.md20.0 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

frontier-ai-models-expert — vibes-plug Skill

English | Bahasa Indonesia


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

English

Orchestration & Integration

  • Model Booster & Inference Engines: gemini-agent-booster, test-time-compute-optimizer, thinking-budget-orchestrator
  • Multi-Draft & Synthesizer: speculative-multi-draft-synthesizer, living-codebase-ast-graph, ephemeral-wasm-sandbox-executor
  • Frameworks & SDKs: vercel-ai-sdk-expert, pydantic-ai-expert, ai-llm-integration-expert
  • Multimodal & Realtime Streaming: voice-ai-realtime-agent, affective-computing-emotion-ai, multimodal-spatial-video-cloner
  • Prefix Caching & FinOps: kv-cache-prefix-optimizer, context-window-engineer, llm-finops-router
  • Enterprise Safety & Governance: ai-safety-governance-expert, prompt-injection-firewall, anti-slop
  • Lifecycle & Governance: zero-to-prod-orchestrator, brainstorming, session-memory-manager

Description

Comprehensive guide for late-2026 frontier AI models across all primary IDE execution tiers. Calibrates routing, reasoning budgets, context ingestion, and code generation across four standardized tiers:

  1. High Fast Tier (Grok 4.7 High Fast, Grok 4.6 High Fast): Sub-second Time-to-First-Token (TTFT), real-time web compute, and lightning-fast subagent review loops.
  2. Fast Tier (Composer 2.5 Fast): Cursor native multi-file AST diff generation and surgical file system edits.
  3. Medium / Balanced Tier (Gemini 4 Argon Medium, GPT-5.6 Sol Medium, Claude Opus 5.5 Medium): 2M+ monolithic context ingestion with native caching, strict JSON schema contracts, and balanced architectural refactoring.
  4. High / Extended Thinking Tier (Claude Sonnet 5.5 High, Claude Fable 5.1 High, Muse Wonder High, Gemini 4 Pro / Project Astra): Deep System-2 thinking budgets (up to 64K tokens), zero-defect fullstack craftsmanship, generative UI layout synthesis, and multimodal spatial reasoning.

Trigger Conditions

Activate this skill when:

  • Selecting or configuring models in modern IDEs (Cursor, Antigravity, Claude Code, Windsurf).
  • Routing tasks across High Fast, Fast, Medium, and High model tiers.
  • Integrating Grok 4.7, Composer 2.5, Gemini 4 Argon, GPT-5.6 Sol, Claude Sonnet 5.5, or Muse Wonder into agent workflows.
  • Calibrating extended thinking budgets, KV-cache prefixing, or multi-file diff application pipelines.
  • Building speculative multi-draft code synthesis engines or multi-agent swarms.

Core Concepts & Tier Taxonomy

1. The 4-Tier Frontier Execution Matrix

TierModel IdentifierPrimary ProviderContext WindowThinking SupportOptimal Workload
High FastGrok 4.7 High FastxAI131,072Fast System-2Rapid test iterations, real-time web search grounding, sub-second code reviews
High FastGrok 4.6 High FastxAI131,072LightweightHigh-throughput batch AST scanning, linting auto-fixes, and syntax transforms
FastComposer 2.5 FastCursor / Native128,000Diff SpecializedMulti-file unified diff generation, cross-file renames, and atomic patch application
MediumGemini 4 Argon MediumGoogle DeepMind2,097,152Balanced (thinkingConfig)Monolithic repository ingestion, persistent context caching, balanced feature work
MediumGPT-5.6 Sol MediumOpenAI262,144Balanced (reasoning_effort)Strict JSON schema extraction, OpenAPI tool calling, and spec-driven development
MediumClaude Opus 5.5 MediumAnthropic200,000Balanced (up to 16K)Architectural system refactoring, cross-domain logic validation, clean code audit
HighClaude Sonnet 5.5 HighAnthropic200,000Extended (up to 64K)Flagship fullstack coding, complex type system engineering, formal verification
HighClaude Fable 5.1 HighAnthropic200,000Extended (up to 32K)Cost-optimized agentic workflows, long multi-turn swarms with heavy cache reuse
HighMuse Wonder HighMuse / Frontier131,072Exploratory ReasoningGenerative UI composition, visual layout brainstorming, dynamic motion physics
HighGemini 4 ProGoogle DeepMind2,097,152+Exhaustive (up to 64K)Monolithic codebase analysis, formal SMT invariant proving, spatial video cloner

2. Availability Fallback Matrix (Graceful Degradation Protocol)

When a late-2026 frontier model is pending regional provisioning, temporarily quota-throttled, or unavailable in the environment, the swarm enforces automatic, non-disruptive fallback:

Target Frontier ModelFallback TargetFallback Rationale & Preserved Guarantees
Gemini 4 Argon MediumGemini 3.8 FlashPreserves 1M+ context window, native context caching (cachedContent), and sub-100ms TTFT.
Gemini 4 Pro HighGemini 3.8 FlashPreserves monolithic context ingestion and dynamic thinking budget control.
Claude Opus 5.5 MediumClaude Opus 4.6Preserves deep architectural comprehension, cross-domain logic validation, and formal refactoring rigor.
Grok 4.7 High FastGrok 4.6 High FastPreserves sub-second TTFT and low-latency AST scanning loops.
Composer 2.5 FastGrok 4.6 High FastFallback for non-Cursor environments to generate unified diff patches.

Implementation Blueprints

1. Grok 4.7 High Fast: Streaming Low-TTFT Agentic Loop

import OpenAI from 'openai';

export interface GrokAgentOptions {
  apiKey: string;
  enableSearch?: boolean;
}

export class GrokFastAgent {
  private client: OpenAI;

  constructor(options: GrokAgentOptions) {
    this.client = new OpenAI({
      apiKey: options.apiKey,
      baseURL: 'https://api.x.ai/v1',
    });
  }

  async executeFastAudit(filePath: string, sourceCode: string): Promise<string> {
    const stream = await this.client.chat.completions.create({
      model: 'grok-4-7-high-fast',
      stream: true,
      temperature: 0.1,
      messages: [
        {
          role: 'system',
          content: 'You are Grok 4.7 High Fast. Provide instant, high-precision AST code critique. Identify vulnerabilities, performance bottlenecks, and type inconsistencies with zero conversational fluff.',
        },
        {
          role: 'user',
          content: `Audit target file [${filePath}]:\n\n\`\`\`typescript\n${sourceCode}\n\`\`\``,
        },
      ],
    });

    let result = '';
    for await (const chunk of stream) {
      const content = chunk.choices[0]?.delta?.content ?? '';
      result += content;
    }
    return result;
  }
}

2. Composer 2.5 Fast: Multi-File Unified Diff Engine

export interface FilePatch {
  filePath: string;
  originalContent: string;
  modifiedContent: string;
}

export interface UnifiedDiffResult {
  filePath: string;
  diffText: string;
  hasChanges: boolean;
}

export class ComposerDiffEngine {
  generateUnifiedDiff(patch: FilePatch): UnifiedDiffResult {
    const origLines = patch.originalContent.split('\n');
    const modLines = patch.modifiedContent.split('\n');

    if (patch.originalContent === patch.modifiedContent) {
      return { filePath: patch.filePath, diffText: '', hasChanges: false };
    }

    const diffLines: string[] = [
      `--- a/${patch.filePath}`,
      `+++ b/${patch.filePath}`,
    ];

    let i = 0;
    let j = 0;
    while (i < origLines.length || j < modLines.length) {
      if (i < origLines.length && j < modLines.length && origLines[i] === modLines[j]) {
        diffLines.push(` ${origLines[i]}`);
        i++;
        j++;
      } else if (j < modLines.length && (i >= origLines.length || !origLines.slice(i).includes(modLines[j]))) {
        diffLines.push(`+${modLines[j]}`);
        j++;
      } else if (i < origLines.length) {
        diffLines.push(`-${origLines[i]}`);
        i++;
      }
    }

    return {
      filePath: patch.filePath,
      diffText: diffLines.join('\n'),
      hasChanges: true,
    };
  }
}

3. Gemini 4 Argon Medium: Monolithic Context Caching

import { GoogleGenAI } from '@google/genai';

export interface RepositoryContext {
  files: Array<{ path: string; content: string }>;
}

export async function queryGeminiArgon(
  repo: RepositoryContext,
  query: string,
  apiKey: string
): Promise<string> {
  const ai = new GoogleGenAI({ apiKey });
  const serializedRepo = repo.files
    .map((f) => `// File: ${f.path}\n${f.content}\n`)
    .join('\n\n');

  // Utilize native context caching for 2M+ tokens with 75-90% read discount
  const cache = await ai.cachedContents.create({
    model: 'gemini-4-argon-medium',
    contents: [{ role: 'user', parts: [{ text: serializedRepo }] }],
    ttl: '7200s',
  });

  const response = await ai.models.generateContent({
    model: 'gemini-4-argon-medium',
    contents: [{ role: 'user', parts: [{ text: query }] }],
    config: {
      cachedContent: cache.name,
      thinkingConfig: {
        thinkingBudget: 8192,
      },
    },
  });

  return response.text ?? '';
}

4. Claude Sonnet 5.5 High: Extended Thinking & Ephemeral Prompt Caching

import Anthropic from '@anthropic-ai/sdk';

export interface ExtendedThinkingTask {
  systemContract: string;
  architectureSpec: string;
  prompt: string;
  thinkingBudgetTokens?: number;
}

export async function executeSonnetHighCraftsman(
  task: ExtendedThinkingTask,
  apiKey: string
): Promise<string> {
  const anthropic = new Anthropic({ apiKey });
  const budget = task.thinkingBudgetTokens ?? 32768;

  const response = await anthropic.messages.create({
    model: 'claude-sonnet-5.5-20261022',
    max_tokens: 64000,
    thinking: {
      type: 'enabled',
      budget_tokens: budget,
    },
    system: [
      {
        type: 'text',
        text: task.systemContract,
      },
      {
        type: 'text',
        text: task.architectureSpec,
        cache_control: { type: 'ephemeral' },
      },
    ],
    messages: [{ role: 'user', content: task.prompt }],
  });

  const textBlock = response.content.find((block) => block.type === 'text');
  return textBlock ? textBlock.text : '';
}

5. GPT-5.6 Sol Medium: Strict JSON Contracts & Tool Calls

import OpenAI from 'openai';
import { z } from 'zod';
import { zodResponseFormat } from 'openai/helpers/zod';

export const ArchitecturalDecisionSchema = z.object({
  componentName: z.string(),
  riskTier: z.enum(['LOW', 'MEDIUM', 'HIGH', 'CRITICAL']),
  dependencies: z.array(z.string()),
  interfaceSignature: z.string(),
  invariants: z.array(z.string()),
});

export type ArchitecturalDecision = z.infer<typeof ArchitecturalDecisionSchema>;

export async function extractStrictContract(
  specText: string,
  apiKey: string
): Promise<ArchitecturalDecision> {
  const openai = new OpenAI({ apiKey });

  const completion = await openai.beta.chat.completions.parse({
    model: 'gpt-5.6-sol-medium',
    messages: [
      {
        role: 'system',
        content: 'You are GPT-5.6 Sol. Extract strict architectural contracts adhering to zero-slop specifications.',
      },
      { role: 'user', content: specText },
    ],
    response_format: zodResponseFormat(ArchitecturalDecisionSchema, 'decision'),
  });

  const parsed = completion.choices[0]?.message.parsed;
  if (!parsed) {
    throw new Error('Failed to parse structured response from GPT-5.6 Sol');
  }
  return parsed;
}

6. Muse Wonder High: Exploratory Generative UI Synthesis

export interface GenerativeUIConcept {
  layoutPattern: 'bento' | 'split-rail' | 'spatial-card' | 'dock-centered';
  motionCurve: string;
  paletteTokens: Record<string, string>;
  interactionStates: string[];
}

export function buildMuseWonderUIBlueprint(intent: string): GenerativeUIConcept {
  // Generative UI concept mapping tailored for exploratory design
  return {
    layoutPattern: intent.includes('dashboard') ? 'bento' : 'spatial-card',
    motionCurve: 'cubic-bezier(0.16, 1, 0.3, 1)',
    paletteTokens: {
      surfacePrimary: '#0A0D14',
      surfaceSecondary: '#141824',
      borderSubtle: 'rgba(255, 255, 255, 0.08)',
      accentGlow: 'rgba(59, 130, 246, 0.25)',
      textTabular: 'tabular-nums font-mono text-sm',
    },
    interactionStates: ['idle', 'hover', 'active', 'focused', 'disabled'],
  };
}

Best Practices

  1. Use High Fast for Outer Tight Loops: Deploy Grok 4.7 or Grok 4.6 for quick AST scans, test assertions, and validation gates before initiating heavier tasks.
  2. Use Composer 2.5 Fast for Atomic File Mutations: Let Composer 2.5 handle surgical multi-file unified diff generation and patch applications to avoid AST disruption.
  3. Use Gemini 4 Argon for Repositories >100K Tokens: Ingest the entire codebase into Gemini 4 Argon's 2M+ context window with cachedContent to preserve full dependency topology without lossy RAG chunking.
  4. Use Claude Sonnet 5.5 High for Core Production Code: Direct all primary production feature development, complex type construction, and security-critical algorithms to Sonnet 5.5 with calibrated thinking budgets (8K–32K).
  5. Use GPT-5.6 Sol for External API Schemas: Leverage GPT-5.6 Sol when converting unstructured specifications into strict OpenAPI schemas or Zod contracts.
  6. Use Muse Wonder High for Design Explorations: Leverage Muse Wonder when brainstorming novel UX interactions, generative UI components, and dynamic micro-animations.

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

Bahasa Indonesia

Integrasi Orkestrasi

  • Booster Model & Engine Inferensi: gemini-agent-booster, test-time-compute-optimizer, thinking-budget-orchestrator
  • Sintesis Multi-Draf & Validator: speculative-multi-draft-synthesizer, living-codebase-ast-graph, ephemeral-wasm-sandbox-executor
  • Framework & SDK: vercel-ai-sdk-expert, pydantic-ai-expert, ai-llm-integration-expert
  • Streaming Multimodal & Realtime: voice-ai-realtime-agent, affective-computing-emotion-ai, multimodal-spatial-video-cloner
  • Caching Prefix & FinOps: kv-cache-prefix-optimizer, context-window-engineer, llm-finops-router
  • Keamanan & Tata Kelola Enterprise: ai-safety-governance-expert, prompt-injection-firewall, anti-slop
  • Siklus Hidup & Tata Kelola: zero-to-prod-orchestrator, brainstorming, session-memory-manager

Deskripsi

Panduan komprehensif integrasi model AI frontier akhir 2026 di seluruh tier eksekusi IDE modern. Mengalibrasi perutean (routing), anggaran penalaran (thinking budgets), ingesti konteks, dan pembentukan kode ke dalam empat tier standar:

  1. Tier High Fast (Grok 4.7 High Fast, Grok 4.6 High Fast): Time-to-First-Token (TTFT) sub-detik, komputasi web real-time, dan siklus review subagen secepat kilat.
  2. Tier Fast (Composer 2.5 Fast): Pembuat diff AST multi-file native Cursor dan mutasi sistem file yang presisi secara bedah.
  3. Tier Medium / Seimbang (Gemini 4 Argon Medium, GPT-5.6 Sol Medium, Claude Opus 5.5 Medium): Ingesti konteks monolitik 2M+ dengan caching native, kepatuhan skema JSON ketat, dan refaktor arsitektur seimbang.
  4. Tier High / Penalaran Mendalam (Claude Sonnet 5.5 High, Claude Fable 5.1 High, Muse Wonder High, Gemini 4 Pro / Project Astra): Anggaran penalaran System-2 mendalam (hingga 64K token), pengkodean fullstack tanpa cacat, eksplorasi desain UI generatif, dan penalaran spasial multimodal.

Kondisi Pemicu

Aktifkan skill ini ketika:

  • Memilih atau mengonfigurasi model pada IDE modern (Cursor, Antigravity, Claude Code, Windsurf).
  • Merutekan tugas di seluruh tier model High Fast, Fast, Medium, dan High.
  • Mengintegrasikan Grok 4.7, Composer 2.5, Gemini 4 Argon, GPT-5.6 Sol, Claude Sonnet 5.5, atau Muse Wonder ke alur agen.
  • Mengalibrasi budget penalaran, penguncian prefix cache KV, atau generator diff multi-file.
  • Membangun mesin sintesis draf spekulatif atau swarm multi-agen.

Matriks Taksonomi 4-Tier Model Frontier

TierNama ModelPenyedia UtamaWindow KonteksDukungan PenalaranBeban Kerja Optimal
High FastGrok 4.7 High FastxAI131.072System-2 CepatIterasi tes cepat, pencarian web real-time, audit kode sub-detik
High FastGrok 4.6 High FastxAI131.072RinganPemindaian AST throughput tinggi, auto-fix linter, transformasi sintaks
FastComposer 2.5 FastCursor / Native128.000Khusus DiffPembuatan diff multi-file, rename simbol lintas-file, aplikasi patch atomik
MediumGemini 4 Argon MediumGoogle DeepMind2.097.152Seimbang (thinkingConfig)Ingesti repositori monolitik, caching konteks persisten, fitur standar
MediumGPT-5.6 Sol MediumOpenAI262.144Seimbang (reasoning_effort)Ekstraksi skema JSON ketat, eksekusi tool OpenAPI, spec-driven dev
MediumClaude Opus 5.5 MediumAnthropic200.000Seimbang (hingga 16K)Refaktor arsitektur sistem, validasi logika lintas domain, audit clean code
HighClaude Sonnet 5.5 HighAnthropic200.000Diperluas (hingga 64K)Kode fullstack unggulan, rekayasa sistem tipe kompleks, verifikasi formal
HighClaude Fable 5.1 HighAnthropic200.000Diperluas (hingga 32K)Alur agen hemat biaya, swarm multi-putaran panjang dengan cache tinggi
HighMuse Wonder HighMuse / Frontier131.072Penalaran EksploratifKomposisi UI generatif, eksplorasi layout visual, fisika motion dinamis
HighGemini 4 ProGoogle DeepMind2.097.152+Lengkap (hingga 64K)Analisis repositori monolitik, pembuktian invarian SMT, kloning video spasial

2. Matriks Pengalihan Ketersediaan (Protokol Degradasi Anggun)

Ketika model frontier belum tersedia di region/lingkungan pengguna atau mengalami pembatasan kuota, swarm secara otomatis mengalihkan tugas tanpa gangguan eksekusi:

Model Frontier UtamaModel Pengalihan (Fallback)Alasan & Jaminan yang Dipertahankan
Gemini 4 Argon MediumGemini 3.8 FlashMempertahankan context window 1M+, caching konteks native (cachedContent), dan TTFT sub-100ms.
Gemini 4 Pro HighGemini 3.8 FlashMempertahankan ingesti konteks monolitik dan kontrol budget penalaran dinamis.
Claude Opus 5.5 MediumClaude Opus 4.6Mempertahankan pemahaman arsitektur mendalam, validasi logika lintas domain, dan ketelitian refaktor formal.
Grok 4.7 High FastGrok 4.6 High FastMempertahankan TTFT sub-detik dan loop pemindaian AST latensi rendah.
Composer 2.5 FastGrok 4.6 High FastPengalihan di luar lingkungan Cursor untuk menghasilkan patch unified diff.

Panduan Praktik Terbaik

  1. Gunakan High Fast untuk Loop Iterasi Cepat: Kerahkan Grok 4.7 atau Grok 4.6 untuk pemindaian AST awal, eksekusi unit test, dan validasi linter sebelum masuk ke tugas yang lebih berat.
  2. Gunakan Composer 2.5 Fast untuk Mutasi File Atomik: Serahkan pembuatan unified diff multi-file dan aplikasi patch ke Composer 2.5 agar modifikasi file berlangsung aman tanpa merusak struktur kode.
  3. Gunakan Gemini 4 Argon untuk Proyek >100K Token: Muat seluruh repositori ke context window 2M+ Gemini 4 Argon dengan cachedContent untuk mempertahankan seluruh relasi simbol tanpa terpotong oleh RAG.
  4. Gunakan Claude Sonnet 5.5 High untuk Kode Produksi Utama: Percayakan logika bisnis inti, arsitektur domain, dan sistem tipe TypeScript yang ketat pada Sonnet 5.5 dengan budget penalaran terkalibrasi.
  5. Gunakan GPT-5.6 Sol untuk Skema API Eksternal: Manfaatkan ketepatan ekstraksi skema GPT-5.6 Sol untuk kontrak OpenAPI dan skema Zod yang deterministik.
  6. Gunakan Muse Wonder High untuk Eksplorasi Desain UI: Manfaatkan penalaran divergen Muse Wonder saat merancang antarmuka generatif dan kurva animasi mikro yang memikat pengguna.

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

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.

中文简介正在准备中。当前显示原文说明。

roedyrustam/vibes-plug1122026年10月22日 更新

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.

中文简介正在准备中。当前显示原文说明。

roedyrustam/vibes-plug1122026年10月22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.

中文简介正在准备中。当前显示原文说明。

roedyrustam/vibes-plug1122026年10月22日 更新

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.

中文简介正在准备中。当前显示原文说明。

roedyrustam/vibes-plug1122026年10月22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.

中文简介正在准备中。当前显示原文说明。

roedyrustam/vibes-plug1122026年10月22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.

中文简介正在准备中。当前显示原文说明。

roedyrustam/vibes-plug1122026年10月22日 更新

查看 roedyrustam 的全部技能

报告此技能的问题