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chengzhang-98

Benchmarked Free Ride

by ChengZhang-98 · GitHub ↗ · v1.2.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install benchmarked-free-ride
Description
Pick the best free OpenRouter models using live benchmark CI results. Use when: user wants performance-ranked free model recommendations, needs a model that...
README (SKILL.md)

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 \x3Ccommand> from the skill directory. No pip install required — uses only Python stdlib.

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 \x3Cmodel_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 \x3Cmodel_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
Usage Guidance
This skill is internally consistent with its stated goal, but take these practical precautions before installing: 1) Inspect the leaderboard URL yourself (https://sequrity-ai.github.io/benchmarked-free-ride-ci/api/leaderboard.json) to confirm you trust the data source. 2) Back up ~/.openclaw/openclaw.json before running 'auto' so you can restore prior settings. 3) Run 'python main.py list' first to review which :free models will be chosen. 4) If you only want to update fallbacks, use 'auto -f' to preserve your current primary. 5) The test files reference DAYTONA_API_KEY/OPENROUTER_API_KEY for CI/integration testing only — you do not need to provide those to use the skill. 6) If you want extra caution, run the skill in a sandbox or review main.py (it only uses urllib + json + file I/O) before applying changes.
Capability Analysis
Type: OpenClaw Skill Name: benchmarked-free-ride Version: 1.2.0 The benchmarked-free-ride skill is a utility designed to automate the selection of free OpenRouter models based on performance benchmarks. It fetches a public leaderboard from a GitHub Pages URL (sequrity-ai.github.io) and updates the local OpenClaw configuration file (~/.openclaw/openclaw.json) to set primary and fallback models. The implementation in main.py is transparent, uses only the Python standard library, and lacks any indicators of data exfiltration, malicious execution, or harmful prompt injection instructions.
Capability Assessment
Purpose & Capability
The name/description match the requested artifacts: python3-only script that fetches a public leaderboard and writes model primary/fallback keys into ~/.openclaw/openclaw.json. There are no unexpected credential requests, unrelated binaries, or extra system paths in the manifest. Development/test files reference CI/integration tooling, but those are clearly ancillary (not required for normal operation).
Instruction Scope
SKILL.md instructs only to fetch leaderboard.json from sequrity-ai.github.io and run local CLI commands (auto/list/switch/status/fallbacks/refresh). The skill intentionally writes only the declared config keys. One noteworthy point: multiple test files and README mention Daytona and OPENROUTER_API_KEY for integration tests — those are for CI/testing and are not required by the runtime instructions or the skill itself. No instructions ask the agent to read unrelated system files or exfiltrate arbitrary data.
Install Mechanism
No install spec and the implementation uses only Python stdlib (urllib, json). The package contains source files and a standard setup.py, but there are no downloads from obscure URLs or extracted archives. This is low-risk from an install standpoint.
Credentials
The skill declares no required environment variables (correct for a public GitHub Pages data fetch). The presence of DAYTONA_API_KEY and OPENROUTER_API_KEY in the test files is expected for running integration tests and is not a runtime dependency of the skill; however users should be aware those tests will require secrets if they choose to run them. Overall, environment/credential requests are proportionate to the skill's purpose.
Persistence & Privilege
The skill does write to the user's ~/.openclaw/openclaw.json (only the documented agents.defaults.model.primary and fallbacks keys). always:false and no other elevated privileges are requested. Modifying the user's agent config is the declared purpose, so the level of persistence/privilege is appropriate; users should note this will change which model their agent uses.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install benchmarked-free-ride
  3. After installation, invoke the skill by name or use /benchmarked-free-ride
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.2.0
Fix: write to openclaw.json instead of config.json; add network/writes/install frontmatter to SKILL.md
v1.1.1
Update SKILL.md and skill.json for stdlib-only invocation (python main.py)
v1.1.0
Remove requests dependency — skill now uses only Python stdlib (urllib)
v1.0.0
Initial release: auto-configure best free OpenRouter models using live benchmark CI data
Metadata
Slug benchmarked-free-ride
Version 1.2.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 4
Frequently Asked Questions

What is 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... It is an AI Agent Skill for Claude Code / OpenClaw, with 116 downloads so far.

How do I install Benchmarked Free Ride?

Run "/install benchmarked-free-ride" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Benchmarked Free Ride free?

Yes, Benchmarked Free Ride is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Benchmarked Free Ride support?

Benchmarked Free Ride is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Benchmarked Free Ride?

It is built and maintained by ChengZhang-98 (@chengzhang-98); the current version is v1.2.0.

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