Imported from terminalskills/skills (skills/routerbase-gateway/SKILL.md). Install upstream with npx skills add terminalskills/skills --skill routerbase-gateway. Copyright stays with the author (Apache-2.0).

RouterBase Gateway

Overview

Use routerbase as an OpenAI-compatible gateway for chat, model routing, and media generation. This skill helps an AI agent update existing OpenAI-style integrations, keep credentials server-side, choose RouterBase model IDs, and add practical fallback handling.

Instructions

When a user asks to integrate or debug RouterBase, follow these steps:

1. Identify the workflow

Classify the request as one of:

• API migration: changing an existing OpenAI-compatible app to RouterBase.
• New integration: adding RouterBase to a backend, script, CLI, or agent.
• Model routing: choosing primary and fallback models for cost, latency, quality, or modality.
• Media generation: calling image, video, speech, or audio generation endpoints.
• Debugging: fixing auth, endpoint, model ID, streaming, or response-shape issues.

2. Keep credentials safe

• Use ROUTERBASE_API_KEY in server-side environment variables.
• Never paste, print, log, or commit a real API key.
• Do not place RouterBase keys in browser code, mobile apps, public repos, screenshots, or client-side configuration.
• Mask secrets in reports, for example sk-rb-...abcd.

3. Use the OpenAI-compatible base URL

For most chat and SDK migrations, preserve the OpenAI request shape and change only the API key, base URL, and model.

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["ROUTERBASE_API_KEY"],
    base_url="https://routerbase.com/v1",
)

response = client.chat.completions.create(
    model="google/gemini-2.5-flash",
    messages=[{"role": "user", "content": "Summarize model routing in one sentence."}],
)

print(response.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.ROUTERBASE_API_KEY,
  baseURL: "https://routerbase.com/v1",
});

const response = await client.chat.completions.create({
  model: "google/gemini-2.5-flash",
  messages: [{ role: "user", content: "Write a short RouterBase smoke test." }],
});

console.log(response.choices[0].message.content);

4. Verify model IDs before production

RouterBase model availability and pricing can change as upstream providers change. When credentials are available, check the live catalog before recommending final production settings.

curl "https://routerbase.com/api/v1/models?task=chat" \
  -H "Authorization: Bearer $ROUTERBASE_API_KEY"

For routing plans, document:

• Primary model.
• Fallback model.
• Why each model fits.
• Feature assumptions to test, such as streaming, tool calling, JSON mode, vision, or context length.
• A minimal smoke test request.

5. Add conservative fallback handling

Fallback only on transient failures such as timeouts, network errors, 429, or 5xx. Do not retry blindly on invalid credentials, invalid model IDs, validation errors, or policy errors.

const modelPlan = [
  "anthropic/claude-sonnet-4-5",
  "google/gemini-2.5-flash",
];

for (const model of modelPlan) {
  try {
    return await client.chat.completions.create({ model, messages });
  } catch (error) {
    if (!isRetryableRouterBaseError(error)) throw error;
  }
}

6. Handle media endpoints by modality

Choose the endpoint based on the requested media type:

• Image: POST https://routerbase.com/v1/images/generations
• Video: POST https://routerbase.com/v1/videos/generations
• Speech: POST https://routerbase.com/v1/audio/speech
• Audio generation: POST https://routerbase.com/v1/audio/generations

Image requests are typically synchronous. Video and audio generation may be asynchronous, so persist the task ID, poll with backoff, or use a callback URL when the app has a durable webhook receiver.

Examples

Example 1: Migrate an OpenAI SDK call

User request: "Move our existing OpenAI chat integration to RouterBase."

Output:

Changes made:
- Set base URL to https://routerbase.com/v1
- Read API key from ROUTERBASE_API_KEY
- Kept the existing chat.completions request shape
- Swapped the model to google/gemini-2.5-flash for the smoke test

Validation:
- Run the backend-only smoke test with ROUTERBASE_API_KEY set
- Confirm the selected production model against the live RouterBase catalog

Example 2: Design fallback routing

User request: "Pick a low-latency primary model and a stronger fallback for support chat."

Output:

Routing plan:
- Primary: google/gemini-2.5-flash for fast support replies
- Fallback: anthropic/claude-sonnet-4-5 for harder cases or transient failures
- Retry only on network errors, 429, and 5xx
- Do not retry invalid model IDs or authentication failures

Smoke test:
- Send 20 representative support questions
- Track latency, refusal rate, JSON validity, and escalation quality

Example 3: Create an image generation request

User request: "Generate a product-style image through RouterBase."

Output:

curl -X POST https://routerbase.com/v1/images/generations \
  -H "Authorization: Bearer $ROUTERBASE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "google/imagen-4",
    "prompt": "A clean product illustration of an AI model routing dashboard",
    "aspect_ratio": "1:1",
    "resolution": "1K"
  }'

Guidelines

• Prefer small smoke tests before changing production traffic.
• Preserve OpenAI-compatible request shapes unless the selected model requires model-specific fields.
• For tool calling, keep standard tools payloads and test the exact schema.
• For JSON outputs, use response_format when supported and validate the result in application code.
• For async media jobs, store the task ID, status, request hash, result URL, and error details.
• Tell the user when model IDs, pricing, or feature support need a live catalog check.
• Keep logs free of API keys, private prompts, customer data, and temporary media URLs.