freeride-opencode

name	freeride-opencode
description	Configure and optimize OpenCode Zen free models with smart fallbacks for subtasks, heartbeat, and cron jobs. Use when setting up cost-effective AI model routing with automatic failover between free models.
version	1.0.0

Freeride OpenCode

Configure OpenCode Zen free models with intelligent fallbacks to optimize costs while maintaining reliability.

Quick Start

Apply optimal free model configuration:

{
  "agents": {
    "defaults": {
      "model": {
        "primary": "opencode/minimax-m2.1-free",
        "fallbacks": [
          "opencode/kimi-k2.5-free",
          "opencode/glm-4.7-free",
          "opencode/gpt-5-nano"
        ]
      },
      "heartbeat": {
        "model": "opencode/gpt-5-nano"
      },
      "subagents": {
        "model": "opencode/kimi-k2.5-free"
      }
    }
  }
}

Model Selection Guide

See models.md for detailed model comparisons, capabilities, and provider information.

Task Type	Recommended Model	Rationale
Primary/General	MiniMax M2.1 Free	Best free model capability
Subtasks	Kimi K2.5 Free	Balanced cost/performance
Heartbeat	GPT 5 Nano	Fastest and cheapest
Cron Jobs	Depends on complexity

Free Models Available

Model	ID	Best For
MiniMax M2.1 Free	opencode/minimax-m2.1-free	Complex reasoning, coding
Kimi K2.5 Free	opencode/kimi-k2.5-free	General purpose, balance
GLM 4.7 Free	opencode/glm-4.7-free	Multilingual, fallback
GPT 5 Nano	opencode/gpt-5-nano	Simple tasks, high frequency

Fallback Strategy

Configure fallbacks in priority order:

"fallbacks": [
  "opencode/kimi-k2.5-free",    // Second best capability
  "opencode/glm-4.7-free",       // Alternative provider
  "opencode/gpt-5-nano"          // Fallback of last resort
]

Fallback triggers:

• Rate limits exceeded
• Auth failures
• Timeouts
• Provider unavailability

Per-Task Configuration

Heartbeat (Every 30 min)

"heartbeat": {
  "every": "30m",
  "model": "opencode/gpt-5-nano"
}

Use the cheapest model for frequent, lightweight checks.

Subtasks/Subagents

"subagents": {
  "model": "opencode/kimi-k2.5-free"
}

Good balance for secondary tasks that need reasonable capability.

Complete Example

{
  "agents": {
    "defaults": {
      "model": {
        "primary": "opencode/minimax-m2.1-free",
        "fallbacks": [
          "opencode/kimi-k2.5-free",
          "opencode/glm-4.7-free",
          "opencode/gpt-5-nano"
        ]
      },
      "models": {
        "opencode/minimax-m2.1-free": { "alias": "MiniMax M2.1" },
        "opencode/kimi-k2.5-free": { "alias": "Kimi K2.5" },
        "opencode/glm-4.7-free": {},
        "opencode/gpt-5-nano": {}
      },
       
         
         
      
       
         
      
    
  

Applying Configuration

Use OpenClaw CLI:

openclaw config.patch --raw '{
  "agents": {
    "defaults": {
      "model": {
        "primary": "opencode/minimax-m2.1-free",
        "fallbacks": ["opencode/kimi-k2.5-free", "opencode/glm-4.7-free", "opencode/gpt-5-nano"]
      },
      "heartbeat": { "model": "opencode/gpt-5-nano" },
      "subagents": { "model": "opencode/kimi-k2.5-free" }
    }
  }
}'

Best Practices

1. Always have fallbacks - Configure at least 2-3 fallback models
2. Mix providers - Reduces single-provider risk
3. Match model to task - Don't use MiniMax for simple checks
4. Test fallback order - Put more capable models first
5. Monitor usage - Track which models get used most

Troubleshooting

Rate limits still occurring?

• Add more fallback models
• Consider reducing heartbeat frequency

Responses too slow?

• Move GPT 5 Nano higher in fallback chain
• Use simpler model for subtasks

Model not available?

• Check model ID format: opencode/model-id-free
• Verify model is still free (check models.md)

References

models.md

Complete reference of all free models with capabilities, providers, performance comparisons, and error handling.

templates.md

Ready-to-use configuration templates for different use cases (minimal, complete, cost-optimized, performance-optimized).

examples/usage.md

Practical examples showing how to use this skill in real scenarios.