据 AlphaMoat 统计,截至 2026-09,Model Router 累计下载 4,879 次、获 12 个星标,在全部 5,078 个 AI 技能中排名第 1,916 位,在「AI 智能体」分类(969 个)中排名第 360 位。

累计安装

577

累计下载

4,879

星标

全站排名

#1,916

全站前 38% · 「AI 智能体」前 38%

平台	累计安装	累计下载	星标	评论
ClawHub ↗	149	4,409	12	-
SkillHub ↗	577	4,879	11	—

各平台的计数口径不同,统计时间不一致。

这个技能是做什么的

读取模型配置或一份简单的模型清单，为每个进来的请求挑一个合适的模型：一般或低复杂度的请求走最便宜的可用模型，高复杂度的请求换更强的。判断依据包括模型声明能力与可选成本分、任务复杂度启发式，以及调用方给出的显式覆盖。复杂度启发式可能把法律、医疗、安全或准确性要求高的请求路由到能力不足的模型。此类任务应把路由结果视为提示，并进行人工复核或显式覆盖；只加载可信、已审核的模型配置。

资料发布于 2026-10-27

可以用来做什么

把简单问答压到低成本模型
给复杂请求指定更强的模型

什么时候用

模型调用成本偏高时
希望按任务难度自动换模型时

示例提示词

以下是根据技能文档生成的示例,不代表实际运行结果。

这个简单问题用最便宜的模型答

复杂任务自动换更强的模型处理

月度数据趋势

累计下载4,879

星标12

2026-09 起统计方法调整:同名技能按作者分别统计,并覆盖全部技能。图中虚线为调整前的月份,与之后的数据不直接比较,也不计算跨越调整的增长。

月份	累计下载	环比	星标
2026-09	4,879	—	12
2026-08	4,356	+3.1%	12
2026-07	4,225	+7.6%	12
2026-06	3,925	+3.6%	11
2026-05	3,790	—	11
2026-03	3,331	—	11

SKILL.md 原文

以下是作者发布的原始文件,按纯文本显示,本站未做修改或审核;其中的链接不可点击,命令请核实后再运行。抓取于 2026-10-23。

1.7 KB · ClawHub v0.1.1

---
name: model-router-premium
version: 0.1.1
description: Route model requests based on configured models, costs and task complexity. Use for routing general/low-complexity requests to the cheapest available model and for higher-complexity requests to stronger models.
---

# model-router

This skill provides a compact, runnable router that inspects an OpenClaw-style configuration (or a simple models JSON) and selects an appropriate model for an incoming request based on:

展开全部(共 39 行)

- declared model capabilities and an optional cost score
- task complexity (heuristic: short/simple vs long/complex)
- explicit overrides (user or caller hints)

Design principles
- Keep decision logic small and deterministic.
- Default to the cheapest model for general, not-complex tasks.
- Escalate to stronger models when the task appears complex or asks for high-fidelity results.
- Make model metadata explicit (capabilities, cost_score, tags) so the router is transparent and auditable.

What this skill includes
- scripts/router.py — a small CLI and library to pick a model given a task description and a models configuration file.
- examples/models.json — example model configurations (name, provider, cost_score, capabilities).

When to use
- Trigger when you need to programmatically choose which LLM to call for a user request.
- Use for batching or middleware routing in server apps.

Usage (quick)
- Prepare a models file (JSON) with model entries. See examples/models.json.
- Call: python3 scripts/router.py --models examples/models.json --task "Summarize this email" --mode auto
- The script prints the chosen model and the reasoning.

Files
- scripts/router.py — router CLI/library
- examples/models.json — sample model list