# Open Router

> Configure OpenRouter model routing with provider auth, model selection, fallback chains, and cost-aware defaults for stable multi-model workflows.

- Skill: `modbender/open-router` (Agent Skill, multi-file: 7 files)
- Install (CLI): `npx skillmds@latest add modbender/open-router`
- Raw SKILL.md: https://api.skillmd.com/api/skills/modbender/open-router/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: modbender (https://skillmd.com/u/modbender)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/modbender/open-router

---


## Setup

On first use, read `setup.md` to align activation boundaries, reliability goals, and routing preferences before making configuration changes.

## When to Use

Use this skill when the user wants to connect an OpenAI-compatible workflow to OpenRouter, choose models by task type, set safe fallbacks, and control cost drift over time.

## Architecture

Memory lives in `~/open-router/`. See `memory-template.md` for structure.

```
~/open-router/
├── memory.md            # Active routing profile and constraints
├── providers.md         # Confirmed provider and auth choices
├── routing-rules.md     # Task -> model and fallback policy
├── incidents.md         # Outages, rate limits, and recovery notes
└── budgets.md           # Spend guardrails and optimization actions
```

## Quick Reference

Use the smallest relevant file for the current task.

| Topic | File |
|-------|------|
| Setup and activation preferences | `setup.md` |
| Memory template | `memory-template.md` |
| Authentication and provider wiring | `auth-and-provider.md` |
| Routing patterns by workload | `routing-playbooks.md` |
| Reliability and fallback handling | `fallback-reliability.md` |
| Cost controls and spend reviews | `cost-guardrails.md` |

## Core Rules

### 1. Start from Workload Classes, Not Model Hype
- Classify requests first: coding, analysis, extraction, summarization, or long-context synthesis.
- Map each class to a primary model and a fallback before changing any defaults.

### 2. Keep Authentication Explicit and Verifiable
- Use `OPENROUTER_API_KEY` from the local environment, never pasted into logs or chat memory.
- Validate auth with a minimal request before applying routing changes.

### 3. Design Fallbacks for Failure Modes, Not Convenience
- Separate fallback reasons: rate limit, provider outage, latency spike, or output quality failure.
- Keep at least one fallback from a different provider family for resilience.

### 4. Enforce Cost Boundaries Before Throughput Tuning
- Set cost ceilings by task class and check expected token burn before broad rollout.
- Route low-stakes tasks to cheaper models and reserve premium models for high-impact tasks.

### 5. Change One Layer at a Time
- Modify either model selection, fallback policy, or budget limits in a single iteration.
- After each change, run a quick verification prompt set and record outcome.

### 6. Record Decisions for Repeatability
- Save the final routing policy, rationale, and known tradeoffs in memory.
- Reuse proven policies instead of repeatedly rebuilding from scratch.

## Common Traps

- Choosing one model for every task -> higher cost and unstable quality under varied workloads.
- Using same-family fallback chain only -> cascading failures during provider-specific incidents.
- Ignoring token limits for long inputs -> truncated responses and hidden quality loss.
- Changing routing and budgets simultaneously -> unclear root cause when quality drops.
- Running without verification prompts -> broken routing detected only after user-facing failures.

## External Endpoints

These endpoints are used only to discover model metadata and execute routed inference requests under explicit user task intent.

| Endpoint | Data Sent | Purpose |
|----------|-----------|---------|
| https://openrouter.ai/api/v1/models | none or auth header only | Discover current model catalog and metadata |
| https://openrouter.ai/api/v1/chat/completions | user prompt content and selected model id | Execute routed inference requests |

No other data is sent externally.

## Security & Privacy

**Data that leaves your machine:**
- Prompt text and selected model metadata sent to OpenRouter when inference is requested.

**Data that stays local:**
- Routing notes and preferences under `~/open-router/`.
- Local environment variable references and verification logs.

**This skill does NOT:**
- Request raw API keys in chat.
- Store plaintext secrets in skill memory files.
- Modify files outside `~/open-router/` for its own state.

## Trust

By using this skill, prompt content is sent to OpenRouter for model execution.
Only install if you trust this service with your data.

## Related Skills
Install with `clawhub install <slug>` if user confirms:
- `api` — API request design, payload shaping, and response validation patterns
- `auth` — credential handling and auth troubleshooting workflows
- `models` — model comparison and selection guidance
- `monitoring` — runtime health checks and incident tracking practices

## Feedback

- If useful: `clawhub star open-router`
- Stay updated: `clawhub sync`