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Agent Skill · 研究检索

openrouter-models(OpenRouter models 搜索)

openrouter-models 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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。

openrouter-models 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 30,647。

总安装 2,843周安装 115GitHub Stars 110下载 892核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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:openrouter-models(OpenRouter models 搜索)
来源仓库:https://github.com/openrouterteam/skills
仓库路径:skills/openrouter-models
安装命令:
npx skills add https://github.com/openrouterteam/skills --skill openrouter-models
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/openrouterteam/skills --skill openrouter-models

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于查找与 OpenRouter 支持的模型列表相关信息。适合在选择推理引擎或测试多模型时获取参考。通过 GitHub 安装,建议核实模型可用性和版本更新。使用前应确认是否触发外部 API 调用。openrouter-models 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

需结合原始 README 了解模型切换和计费规则。

来源仓库内路径:skills/openrouter-models

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/openrouterteam/skills --skill openrouter-model…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

openrouter-models 怎么安装?

在终端执行 npx skills add https://github.com/openrouterteam/skills --skill openrouter-models。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

openrouter-models 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

openrouter-models 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

openrouter-models 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

openrouter-models 用的人多吗?

公开数据是累计安装 2,843 次,近一周安装 115 次,下载 892 次,GitHub 110 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

openrouter-models 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
openrouter-models

OpenRouter models 搜索 · 本页

2,843110接触敏感数据未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

OpenRouter Models

Discover, search, and compare the 300+ AI models available on OpenRouter. Query live data including pricing, context lengths, per-provider latency and uptime, throughput, supported modalities, and supported parameters.

Prerequisites

The OPENROUTER_API_KEY environment variable is optional for most scripts. It is only required for get-endpoints.ts (provider performance data). Get a key at https://openrouter.ai/keys

First-Time Setup

cd <skill-path>/scripts && npm install

Decision Tree

Pick the right script based on what the user is asking:

User wants to...ScriptExample
See all available modelslist-models.ts"What models does OpenRouter have?"
Find recently added modelslist-models.ts --sort newest"What are the newest models?"
Find cheapest modelslist-models.ts --sort price"What's the cheapest model?"
Find highest throughput modelslist-models.ts --sort throughput"Which models have the most output capacity?"
Find models in a categorylist-models.ts --category X"Best programming models?"
Search by namesearch-models.ts "query""Do they have Claude?"
Resolve an informal model nameresolve-model.ts "query""Use the nano banana 2.0 model"
Find image-capable modelssearch-models.ts --modality image"Which models accept images?"
Compare specific modelscompare-models.ts A B"Compare Claude vs GPT-4o"
Compare by throughputcompare-models.ts A B --sort throughput"Which has higher throughput, Claude or GPT-4o?"
Check provider performanceget-endpoints.ts "model-id""Which provider is fastest for Claude?"
Find fastest providerget-endpoints.ts "model-id" --sort throughput"Fastest provider for Claude Sonnet?"
Find lowest-latency providerget-endpoints.ts "model-id" --sort latency"Lowest latency provider for GPT-4o?"
Check model availabilityget-endpoints.ts "model-id""Is Claude Sonnet 4 up right now?"

Resolve Model

Resolve an informal or vague model name to an exact OpenRouter model ID using fuzzy matching:

cd <skill-path>/scripts && npx tsx resolve-model.ts "claude sonnet"
cd <skill-path>/scripts && npx tsx resolve-model.ts "gpt 4o mini"
cd <skill-path>/scripts && npx tsx resolve-model.ts "llama 3.1"

Results include a confidence level and score:

ConfidenceScoreAction
high (≥0.85)Use the model directly — the match is unambiguous
medium (≥0.55)Confirm with the user before proceeding
low (≥0.30)Suggest the matches and ask the user to clarify

Two-step workflow: First resolve the informal name with resolve-model.ts, then feed the resolved id into other scripts (compare-models.ts, get-endpoints.ts, etc.).

List Models

cd <skill-path>/scripts && npx tsx list-models.ts

Filter by Category

Server-side category filtering:

cd <skill-path>/scripts && npx tsx list-models.ts --category programming

Categories: programming, roleplay, marketing, marketing/seo, technology, science, translation, legal, finance, health, trivia, academia

Sort Results

cd <skill-path>/scripts && npx tsx list-models.ts --sort newest      # Recently added first
cd <skill-path>/scripts && npx tsx list-models.ts --sort price       # Cheapest first
cd <skill-path>/scripts && npx tsx list-models.ts --sort context     # Largest context first
cd <skill-path>/scripts && npx tsx list-models.ts --sort throughput  # Most output tokens first

Models with upcoming expiration_date values trigger a stderr warning.

Search Models

cd <skill-path>/scripts && npx tsx search-models.ts "claude"
cd <skill-path>/scripts && npx tsx search-models.ts --modality image
cd <skill-path>/scripts && npx tsx search-models.ts "gpt" --modality text

Modalities: text, image, audio, file

Compare Models

Compare two or more models side-by-side with pricing in per-million-tokens format. Uses exact ID matching — openai/gpt-4o matches only that model, not variants like gpt-4o-mini.

cd <skill-path>/scripts && npx tsx compare-models.ts "anthropic/claude-sonnet-4" "openai/gpt-4o"
cd <skill-path>/scripts && npx tsx compare-models.ts "anthropic/claude-sonnet-4" "openai/gpt-4o" "google/gemini-2.5-pro" --sort price

Sort options: price (cheapest first), context (largest first), speed/throughput (most output tokens first)

Provider Performance (Endpoints)

Get per-provider latency, uptime, and throughput for any model:

cd <skill-path>/scripts && npx tsx get-endpoints.ts "anthropic/claude-sonnet-4"
cd <skill-path>/scripts && npx tsx get-endpoints.ts "anthropic/claude-sonnet-4" --sort throughput
cd <skill-path>/scripts && npx tsx get-endpoints.ts "openai/gpt-4o" --sort latency

Sort options: throughput (fastest tokens/sec first), latency (lowest p50 ms first), uptime (most reliable first), price (cheapest first)

Returns for each provider:

  • Latency (p50/p75/p90/p99 in ms) — median to worst-case response times
  • Throughput (p50/p75/p90/p99 tokens/sec) — generation speed
  • Uptime — percentage over the last 30 minutes
  • Status — operational or degraded
  • Provider-specific pricing — some providers offer discounts
  • Supported parameters — varies by provider (some don't support all features)

API Response Shapes

GET /api/v1/models returns {data: Model[]}. For full field reference, see the Models reference.

Query parameters (all optional):

ParameterExampleEffect
category?category=programmingServer-side category filter
supported_parameters?supported_parameters=toolsOnly models supporting this parameter

Tips for working with the response:

  • To check if a model supports a feature, use model.supported_parameters (e.g. .includes("tools")), or filter server-side with ?supported_parameters=tools.
  • To check modalities, use model.architecture.input_modalities / model.architecture.output_modalities.
  • Pricing values are per-token in USD as strings — multiply by 1,000,000 for per-million-token pricing.
  • knowledge_cutoff and expiration_date are date strings or null.
  • links.details points to the per-provider endpoints API for that model. GET /api/v1/models/{author}/{slug}/endpoints returns {data: {id, name, endpoints: Endpoint[]}}.
  • Endpoint status: 0 = operational, non-zero = degraded.
  • Endpoint latency_last_30m / throughput_last_30m: percentile objects with p50, p75, p90, p99.

Script Output Formats

The scripts below reformat the raw API data. When calling the API directly (e.g. via fetch), refer to the OpenAPI spec for field names.

list-models.ts / search-models.ts

A subset of the raw API fields — the scripts run formatModel() which drops canonical_slug, hugging_face_id, default_parameters, knowledge_cutoff, and links. If you need those fields, call the API directly.

compare-models.ts

{
  "id": "anthropic/claude-sonnet-4",
  "name": "Anthropic: Claude Sonnet 4",
  "context_length": 1000000,
  "max_completion_tokens": 64000,
  "per_request_limits": null,
  "pricing_per_million_tokens": {
    "prompt": "$3.00",
    "completion": "$15.00",
    "cached_input": "$0.30"
  },
  "modalities": { "input": ["text", "image"], "output": ["text"] },
  "supported_parameters": ["max_tokens", "temperature", "..."],
  "is_moderated": false
}

get-endpoints.ts

{
  "model_id": "anthropic/claude-sonnet-4",
  "model_name": "Anthropic: Claude Sonnet 4",
  "total_providers": 5,
  "endpoints": [
    {
      "provider": "Anthropic",
      "tag": "anthropic",
      "status": "operational",
      "uptime_30m": "100.00%",
      "latency_30m_ms": { "p50": 800, "p75": 1200, "p90": 2000, "p99": 5000 },
      "throughput_30m_tokens_per_sec": { "p50": 45, "p75": 55, "p90": 65, "p99": 90 },
      "context_length": 1000000,
      "max_completion_tokens": 64000,
      "pricing_per_million_tokens": { "prompt": "$3.00", "completion": "$15.00", "cached_input": "$0.30" },
      "supports_implicit_caching": true,
      "supported_parameters": ["max_tokens", "temperature", "tools", "..."]
    }
  ]
}

Key Fields

FieldMeaning
pricing.prompt / pricing.completionCost per token in USD. Multiply by 1,000,000 for per-million-token pricing
context_lengthMax total tokens (input + output)
top_provider.max_completion_tokensMax output tokens from the best provider
top_provider.is_moderatedWhether content moderation is applied
per_request_limitsPer-request token limits (when non-null)
supported_parametersAPI parameters the model accepts (e.g., tools, structured_outputs, reasoning, web_search_options)
createdUnix timestamp — use for sorting by recency
expiration_dateNon-null means the model is being deprecated
latency_30m_ms.p50Median response latency over last 30 min
throughput_30m_tokens_per_sec.p50Median generation speed over last 30 min
uptime_30mProvider availability percentage over last 30 min

Presenting Results

  • When a user mentions a model by informal name, use resolve-model.ts first, then feed the resolved id into other scripts
  • Convert pricing to per-million-tokens format for readability
  • When comparing, use a markdown table with models as columns
  • For provider endpoints, highlight the fastest (lowest p50 latency) and most reliable (highest uptime) providers
  • Call out notable supported parameters: tools, structured_outputs, reasoning, web_search_options
  • Note cache pricing when available — it can cut input costs 90%+
  • Flag models with expiration_date as deprecated
  • When a model has multiple providers at different prices, mention the cheapest option

适合场景

01

调用多模型

02

代码和文本生成

03

Agent 推理流程

04

OpenRouter 模型接入

能力概览

能力 1

统一调用多种 LLM

能力 2

支持 Claude、Gemini、Kimi 等模型

能力 3

适合聊天、代码和推理任务

能力 4

可作为 Agent 模型调用入口

平台分布

Codex

34.77%
按下载量换算310

Claude

30.76%
按下载量换算274

Cursor

16.87%
按下载量换算150

Gemini CLI

8.37%
按下载量换算75

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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。目前只有一个公开来源,建议同时核对源仓库。

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