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Benchmarked Free Ride

作者 ChengZhang-98 · GitHub ↗ · v1.2.0 · MIT-0
cross-platform ✓ 安全检测通过
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当前安装
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在 OpenClaw 中安装
/install benchmarked-free-ride
功能描述
Pick the best free OpenRouter models using live benchmark CI results. Use when: user wants performance-ranked free model recommendations, needs a model that...
使用说明 (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
安全使用建议
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.
功能分析
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.
能力评估
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.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install benchmarked-free-ride
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /benchmarked-free-ride 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
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
元数据
Slug benchmarked-free-ride
版本 1.2.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 4
常见问题

Benchmarked Free Ride 是什么?

Pick the best free OpenRouter models using live benchmark CI results. Use when: user wants performance-ranked free model recommendations, needs a model that... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 116 次。

如何安装 Benchmarked Free Ride?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install benchmarked-free-ride」即可一键安装,无需额外配置。

Benchmarked Free Ride 是免费的吗?

是的,Benchmarked Free Ride 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Benchmarked Free Ride 支持哪些平台?

Benchmarked Free Ride 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Benchmarked Free Ride?

由 ChengZhang-98(@chengzhang-98)开发并维护,当前版本 v1.2.0。

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