Agent Skills › decolua/9router › 9router-chat

9router-chat

GitHub

通过9Router网关调用LLM，支持OpenAI和Anthropic格式。用于聊天、代码生成及文本摘要，具备流式传输与多提供商自动降级能力。

skills/9router-chat/SKILL.md decolua/9router

触发场景

用户希望进行对话或提问需要生成代码片段要求总结文本内容执行自定义Prompt

安装

npx skills add decolua/9router --skill 9router-chat -g -y

更多选项

不安装直接使用

npx skills use decolua/9router@9router-chat

指定 Agent (Claude Code)

npx skills add decolua/9router --skill 9router-chat -a claude-code -g -y

安装 repo 全部 skill

npx skills add decolua/9router --all -g -y

预览 repo 内 skill

npx skills add decolua/9router --list

SKILL.md

Frontmatter

{
    "name": "9router-chat",
    "description": "Chat \/ code generation via 9Router using OpenAI \/v1\/chat\/completions or Anthropic \/v1\/messages format with streaming + auto-fallback combos. Use when the user wants to ask an LLM, generate code, summarize text, or run prompts through 9Router."
}

9Router — Chat

Requires NINEROUTER_URL (and NINEROUTER_KEY if auth enabled). See https://raw.githubusercontent.com/decolua/9router/refs/heads/master/skills/9router/SKILL.md for setup.

Endpoints

POST $NINEROUTER_URL/v1/chat/completions — OpenAI format
POST $NINEROUTER_URL/v1/messages — Anthropic format

Discover

curl $NINEROUTER_URL/v1/models | jq '.data[].id'
# Per-model metadata (contextWindow, params)
curl "$NINEROUTER_URL/v1/models/info?id=openai/gpt-4o"

Combos (e.g. vip, mycodex) auto-fallback through multiple providers.

OpenAI format

curl -X POST $NINEROUTER_URL/v1/chat/completions \
  -H "Authorization: Bearer $NINEROUTER_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"openai/gpt-5","messages":[{"role":"user","content":"Hi"}],"stream":false}'

JS (OpenAI SDK):

import OpenAI from "openai";
const client = new OpenAI({ baseURL: `${process.env.NINEROUTER_URL}/v1`, apiKey: process.env.NINEROUTER_KEY });
const res = await client.chat.completions.create({
  model: "openai/gpt-5",
  messages: [{ role: "user", content: "Hi" }],
  stream: true,
});
for await (const chunk of res) process.stdout.write(chunk.choices[0]?.delta?.content || "");

Anthropic format

curl -X POST $NINEROUTER_URL/v1/messages \
  -H "Authorization: Bearer $NINEROUTER_KEY" \
  -H "anthropic-version: 2023-06-01" \
  -H "Content-Type: application/json" \
  -d '{"model":"cc/claude-opus-4-7","max_tokens":1024,"messages":[{"role":"user","content":"Hi"}]}'

Response shape

OpenAI (/v1/chat/completions):

{ "id": "chatcmpl-...", "object": "chat.completion", "model": "openai/gpt-5",
  "choices": [{ "index": 0, "message": { "role": "assistant", "content": "Hello!" }, "finish_reason": "stop" }],
  "usage": { "prompt_tokens": 8, "completion_tokens": 2, "total_tokens": 10 } }

Streaming (stream:true) emits SSE: data: {choices:[{delta:{content:"..."}}]}\n\n ... data: [DONE]\n\n.

Anthropic (/v1/messages):

{ "id": "msg_...", "type": "message", "role": "assistant", "model": "cc/claude-opus-4-7",
  "content": [{ "type": "text", "text": "Hello!" }],
  "stop_reason": "end_turn", "usage": { "input_tokens": 8, "output_tokens": 2 } }