model-benchmarksReal-time AI model capability tracking via leaderboards (LMSYS Arena, HuggingFace, etc.) for intelligent compute routing and cost optimization
Install via ClawdBot CLI:
clawdbot install notestone/model-benchmarksGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Calls external URL not in known-safe list
https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboardAudited Apr 17, 2026 · audit v1.0
Generated Mar 21, 2026
A dev team uses this skill to route coding tasks like code generation and debugging to cost-effective models like Gemini 2.0 Flash for simple scripts and Claude 3.5 Sonnet for complex programming, achieving up to 78% cost reduction while maintaining quality.
An agency leverages the skill to optimize model selection for writing and translation tasks, using Gemini 2.0 Flash for translations and Claude 3.5 Sonnet for creative content, resulting in 65% savings through task-specific routing.
A research lab employs the skill for data analysis and math tasks, routing to models like GPT-4o for reasoning-heavy work, gaining 45% efficiency improvements by matching models to specific analytical needs.
An e-commerce platform uses the skill to handle simple Q&A and translation tasks with models like GPT-4o Mini, reducing costs by up to 95% for routine inquiries while ensuring accurate responses.
Offer this skill as a premium service with real-time updates and advanced analytics, charging monthly fees based on usage tiers or number of models tracked, targeting enterprises for ongoing cost optimization.
Provide custom integration services to help clients set up and configure the skill with their AI workflows, including training and support, generating one-time project fees or retainer contracts.
License the benchmark data and API endpoints to third-party platforms or developers, enabling them to incorporate model intelligence into their tools, with revenue from licensing agreements or API usage fees.
💬 Integration Tip
Start by running the fetch command daily via cron to keep data fresh, then use the recommend command to set optimal models in OpenClaw configuration for each task type.
Scored Jun 19, 2026
Use CodexBar CLI local cost usage to summarize per-model usage for Codex or Claude, including the current (most recent) model or a full model breakdown. Trigger when asked for model-level usage/cost data from codexbar, or when you need a scriptable per-model summary from codexbar cost JSON.
Gemini CLI for one-shot Q&A, summaries, and generation.
Manages free AI models from OpenRouter for OpenClaw. Automatically ranks models by quality, configures fallbacks for rate-limit handling, and updates opencla...
Manages free AI models from OpenRouter for OpenClaw. Automatically ranks models by quality, configures fallbacks for rate-limit handling, and updates opencla...
Manages free AI models from OpenRouter for OpenClaw. Automatically ranks models by quality, configures fallbacks for rate-limit handling, and updates openclaw.json. Use when the user mentions free AI, OpenRouter, model switching, rate limits, or wants to reduce AI costs.
使用 MiniMax MCP 进行图像理解和分析。触发条件:(1) 用户要求分析图片、理解图像、描述图片内容 (2) 需要识别图片中的物体、文字、场景 (3) 使用 MiniMax 的 understand_image 功能