القائمة
Route AI model requests to the optimal provider based on task, cost, latency, and capability requirements. Manage multi-provider LLM deployments. Use when working with model router.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Enforce staged execution discipline on large tasks: written stage plan, parallel sub-agent delegation, failable verification at each stage, and skeptical self-review before delivery. Use when tasks span multiple files, multiple sources, or multiple sessions. Also triggers on "do this thoroughly", "be systematic", "deep work mode", "be thorough".
Write production-quality code from specs — reads requirements, researches patterns, implements with tests, and iterates until verification passes. Use when implementing features, fixing bugs with known root causes, or building new modules.
Ship code to production through a controlled pipeline with verification gates and rollback plans. Use when deploying features, managing CI/CD, running database migrations, or performing post-incident hotfix recovery.
Investigate topics deeply with cross-referenced sources and produce evidence-backed findings. Use when evaluating technologies before adoption, analyzing competitors, or investigating bug root causes across docs and issues.
Read code changes with adversarial intent to find bugs, security holes, logic errors, and performance traps. Use when reviewing PRs, auditing refactoring for regressions, or running pre-deploy safety checks.
Detect and fix code style violations, enforce project conventions, and ensure consistent formatting across the codebase. Use when cleaning lint errors before PRs, migrating linters, or bulk-applying new rules.
| name | model-router |
| description | Route AI model requests to the optimal provider based on task, cost, latency, and capability requirements. Manage multi-provider LLM deployments. Use when working with model router. |
| domain | core |
| tags | ["ai","llm","routing","multi-provider","cost-optimization","model-selection"] |
Trigger phrases:
"model router"
"When managing multiple LLM providers (OpenAI, Anthropic, Google, etc"
"When optimizing for cost vs quality tradeoffs"
"When implementing fallback chains for reliability"
When managing multiple LLM providers (OpenAI, Anthropic, Google, etc.)
When optimizing for cost vs quality tradeoffs
When implementing fallback chains for reliability
When routing by task type (code to Claude, vision to Gemini, etc.)
Intelligent model routing that selects the best LLM for each task based on capability, cost, latency, and availability. Supports fallback chains, load balancing, and cost tracking.
| Rationalization | Reality |
|---|---|
| "One model is enough" | Different models excel at different tasks — routing optimizes quality |
| "I will use the cheapest model" | Cheap models fail on complex tasks — cost of failure exceeds savings |
| "Routing is over-engineering" | A 3-line fallback chain prevents 90% of LLM outages |
const routes: Record<string, string[]> = {
code: ['claude-sonnet-4-20250514', 'gpt-4o', 'gemini-2.5-pro'],
vision: ['gemini-2.5-pro', 'gpt-4o', 'claude-sonnet-4-20250514'],
fast: ['gemini-2.5-flash', 'gpt-4o-mini', 'claude-haiku'],
creative: ['gpt-4o', 'claude-opus-4-20250514', 'gemini-2.5-pro'],
};
async function routeRequest(task: string, prompt: string) {
const models = routes[task] || routes.fast;
for (const model of models) {
try {
return await callModel(model, prompt);
} catch (e) {
console.warn(`${model} failed, trying next...`);
}
}
throw new Error('All models failed');
}