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ai-model-selectorAI 模型选择器

Agent Skill

ai-model-selector 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

214

周安装

9

GitHub Stars

1

下载量

75
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:ai-model-selector(AI 模型选择器)
来源仓库:https://github.com/jaymay549/ai-model-selector
仓库路径:skills/ai-model-selector
安装命令:
npx skills add https://github.com/jaymay549/ai-model-selector --skill ai-model-selector
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/jaymay549/ai-model-selector --skill ai-model-selector

简介

AI model selector 根据任务类型推荐最优 AI 模型,平衡质量、速度与成本因素。

  • 适用于编码、推理、长文档处理等不同场景的快速决策支持。
  • 提供 GPT、Claude、Gemini 等主流模型的优先级排序与使用建议。
  • 输出为参考意见,实际表现受提示词与参数设置影响较大。
  • 预算敏感项目可优先考虑 DeepSeek 系列高性价比选项。

SKILL.md

AI Model Selector Skill

Comprehensive guide to selecting and implementing AI models. Updated January 2026.

Quick Decision Tree

What's your primary need?
│
├─► CODING/AGENTIC TASKS
│   ├─► Best quality → Claude Sonnet 4.5 or GPT-5.2-Codex
│   ├─► Complex reasoning → Claude Opus 4.5 (with effort param)
│   └─► Budget → DeepSeek-chat ($0.28/1M input)
│
├─► REASONING/MATH/SCIENCE
│   ├─► Maximum intelligence → GPT-5.2 Pro or Claude Opus 4.5
│   ├─► Good balance → GPT-5.2 (xhigh effort) or Gemini 2.5 Pro
│   └─► Budget → DeepSeek-reasoner (visible CoT)
│
├─► LONG DOCUMENTS (>200K tokens)
│   ├─► Up to 1M tokens → Claude Sonnet 4.5 (beta) or Gemini 2.5 Pro
│   ├─► Up to 400K → GPT-5.2
│   └─► Budget → DeepSeek-chat (128K)
│
├─► HIGH-VOLUME/LOW-LATENCY
│   ├─► Best speed → Claude Haiku 4.5
│   ├─► Cheapest → Gemini 2.5 Flash-Lite ($0.10/$0.40)
│   └─► Free tier → Gemini via AI Studio
│
├─► EMBEDDINGS/RAG
│   ├─► Best quality → Voyage 3.5 or voyage-3-large
│   ├─► Code-specific → voyage-code-3
│   ├─► Budget → text-embedding-3-small ($0.02/1M)
│   └─► Free → gemini-embedding-001
│
└─► MULTIMODAL (images/audio/video)
    ├─► Images → GPT-4o, Gemini 2.5 Pro/Flash, Claude 4.5
    ├─► Image generation → GPT Image 1, Imagen 4.0
    └─► Video generation → Veo 3.1

Model Quick Reference (January 2026)

Flagship Models

ModelContextMax OutputInput/Output $/1MBest For
GPT-5.2400K128K$1.75/$14Complex reasoning, coding
GPT-5.2 Pro400K128K$21/$168Hardest problems
Claude Opus 4.5200K64K$5/$25Deep reasoning, agents
Claude Sonnet 4.5200K (1M beta)64K$3/$15Coding, balanced
Gemini 2.5 Pro1M64K$1.25/$10Long context
Gemini 3 Pro1M64K$2/$12Latest Google (preview)

Budget Models

ModelContextInput/Output $/1MBest For
Claude Haiku 4.5200K$1/$5Fast, high-volume
Gemini 2.5 Flash1M$0.30/$2.50Large-scale processing
Gemini 2.5 Flash-Lite1M$0.10/$0.40Cheapest cloud option
DeepSeek-chat128K$0.28/$0.4210x cheaper than GPT
GPT-4o-mini128K$0.15/$0.60Simple tasks

Critical Gotchas

⚠️ GPT-5.x / O-series Don't Support These Parameters:

// WRONG - will error on GPT-5.2, o3, o4-mini
{
  temperature: 0.7,      // ❌ Not supported
  top_p: 0.9,            // ❌ Not supported
  max_tokens: 4096,      // ❌ Use max_completion_tokens
}

// CORRECT
{
  reasoning: { effort: "high" },  // none, low, medium, high, xhigh
  text: { verbosity: "medium" },  // low, medium, high
  max_completion_tokens: 4096
}

⚠️ Claude Opus 4.1 vs 4.5 Pricing

  • Opus 4.1: $15/$75 per 1M tokens (legacy pricing)
  • Opus 4.5: $5/$25 per 1M tokens (66% cheaper, better quality!)
  • Always use Opus 4.5 for new projects

⚠️ Long Context Premium Pricing (Claude Sonnet)

  • ≤200K tokens: $3/$15 per 1M
  • 200K tokens: $6/$22.50 per 1M (automatic)

Detailed Documentation

Use Case Guides

Cost Optimization

Batch API (50% off)

All major providers offer batch processing for non-urgent tasks:

  • OpenAI: 50% off all models
  • Anthropic: 50% off all models
  • DeepSeek: 33% off

Prompt Caching

  • Claude: 90% savings on cache reads
  • OpenAI: 90% savings on cached inputs
  • DeepSeek: Automatic caching, 90% off hits

Model Cascading

Route simple queries to cheap models, complex to expensive:

Simple question → Haiku 4.5 ($1/$5)
Complex task → Sonnet 4.5 ($3/$15)
Hardest problems → Opus 4.5 ($5/$25)

API Code Templates

OpenAI (GPT-5.2)

const response = await fetch("https://api.openai.com/v1/responses", {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${OPENAI_API_KEY}`,
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    model: "gpt-5.2",
    input: [{ role: "user", content: "Hello" }],
    reasoning: { effort: "medium" }
  })
});

Anthropic (Claude)

import Anthropic from '@anthropic-ai/sdk';
const anthropic = new Anthropic();

const response = await anthropic.messages.create({
  model: "claude-sonnet-4-5-20250929",
  max_tokens: 4096,
  messages: [{ role: "user", content: "Hello" }]
});

Google (Gemini)

import { GoogleGenerativeAI } from "@google/generative-ai";
const genAI = new GoogleGenerativeAI(process.env.GEMINI_API_KEY);
const model = genAI.getGenerativeModel({ model: "gemini-2.5-flash" });

const result = await model.generateContent("Hello");

DeepSeek (OpenAI-compatible)

import OpenAI from 'openai';
const client = new OpenAI({
  baseURL: 'https://api.deepseek.com',
  apiKey: process.env.DEEPSEEK_API_KEY
});

const response = await client.chat.completions.create({
  model: 'deepseek-chat',
  messages: [{ role: 'user', content: 'Hello' }]
});

Benchmark Reference (January 2026)

SWE-bench Verified (Coding)

  1. Claude Opus 4.5: 80.9%
  2. GPT-5.1-Codex-Max: 77.9%
  3. Claude Sonnet 4.5: 77.2%
  4. GPT-5.2-Codex: ~78% (est.)

AIME 2025 (Math)

  1. GPT-5.2 (xhigh): 100%
  2. o3: 90%+
  3. Claude Opus 4.5: High 80s%
  4. DeepSeek R1: 79.8%

GPQA Diamond (Science)

  1. GPT-5.2: ~92-93%
  2. Claude Opus 4.5: ~85%+

*Last updated: January 28, 2026* *Sources: Official documentation from OpenAI, Anthropic, Google, DeepSeek*

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

33.42%
按下载量换算25

Claude

31.05%
按下载量换算23

Cursor

19.15%
按下载量换算14

Gemini CLI

10.23%
按下载量换算8

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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