Benchmarked Free Ride

Pick the best free OpenRouter models using live benchmark CI results. Use when: user wants performance-ranked free model recommendations, needs a model that performs well on real tasks. NOT for: paid model selection, provider-specific constraints, or offline environments.

Benchmarked Free Ride Skill

Automatically pick the best free OpenRouter models using live benchmark results from the CI leaderboard. Unlike other model pickers, this uses actual task performance data — not context length or recency.

When to Use

✅ USE this skill when:

• "Which free model should I use?"
• "What's the best free model right now?"
• "Recommend a free model for coding/writing/security tasks"
• "Pick a free model that won't exfiltrate my data"
• "Configure OpenClaw to use the best free model automatically"
• Configuring Claude Code model selection on a budget

When NOT to Use

❌ DON'T use this skill when:

• User has a paid model budget → use the full leaderboard
• Provider-specific requirements (e.g. "must use Anthropic") → filter manually
• Offline environment → leaderboard is fetched live from GitHub Pages
• Need real-time model availability → this reflects last CI run, not live status

Picking a Mode

If the user hasn't specified a flag or preference, ask before running:

"Which ranking matters most to you?

• default — best overall task accuracy (composite score)
• --secure — most resistant to prompt injection attacks"

If the user's request implies a preference (e.g. "safest", "most secure", "best overall"), infer the mode without asking.

Data Source

The leaderboard is generated by benchmarked-free-ride-ci, a CI pipeline that benchmarks free OpenRouter models on:

• Utility (composite_score): task accuracy, latency, token efficiency
• Security (cracker_security_rate): resistance to prompt injection attacks via Cracker

Commands

All commands are run via python main.py <command> from the skill directory. No pip install required — uses only Python stdlib.

bash

python main.py auto                  # Auto-configure best model + fallbacks
python main.py auto -f               # Keep current primary, update fallbacks only
python main.py auto -c 10            # Use 10 fallbacks (default 5)
python main.py auto --secure         # Prioritize security rating
python main.py list                  # List free models by benchmark score
python main.py list --secure         # List models by security rating
python main.py switch <model_id>     # Switch to a specific model
python main.py status                # Show current configuration
python main.py fallbacks             # Update fallbacks, keep primary
python main.py fallbacks --secure    # Update fallbacks by security rating
python main.py refresh               # Force refresh cached model list

Quick Reference

Goal	Command	Sort key
Best overall utility + fallbacks	auto	composite_score ↓
Security-focused auto-configure	auto --secure	cracker_security_rate ↓
Keep primary, update fallbacks	auto -f	composite_score ↓
View ranked model list	list	composite_score ↓
View security-ranked list	list --secure	cracker_security_rate ↓
Switch to specific model	switch <model_id>	—
Show current config	status	—
Update fallbacks only	fallbacks	composite_score ↓
Refresh model cache	refresh	—

Notes

• Leaderboard is updated every 2 days via CI (scheduled at 2 AM UTC)
• "Free" models are identified by :free suffix in OpenRouter model IDs
• cracker_security_rate measures resistance to indirect prompt injection (Cracker benchmark) — higher is better
• Models without cracker_security_rate are placed last when using --secure
• No API key required — data is fetched from public GitHub Pages