AI Intelligence Hub - Real-time Model Capability Tracking

Real-time AI model capability tracking via leaderboards (LMSYS Arena, HuggingFace, etc.) for intelligent compute routing and cost optimization

Security Scan

The skill's documentation promises real-time leaderboard fetching and integrations, but the shipped code uses simulated/local data and the examples reference external webhooks and system config changes that are not declared — this mismatch warrants caution before installing or scheduling it to run automatically.

Purpose & Capability

The README/SKILL.md claim 'real-time' pulls from LMSYS, BigCode, HuggingFace and 'No external dependencies'. The bundled scripts (scripts/run.py) currently implement mocked fetch functions and write local JSON files rather than performing actual network scraping/API queries. The code includes BENCHMARK_SOURCES with real-looking URLs (HuggingFace spaces) and imports urllib, but does not actually fetch those endpoints in the provided implementation. This is a clear capability mismatch: the skill advertises live data but ships a simulated/local-only implementation.

Instruction Scope

Runtime instructions center on running the included Python script to fetch, query, recommend, and write local benchmark data, and on integrating results into OpenClaw config or dashboards. The instructions do not direct the agent to read unrelated system files or exfiltrate data. Examples include sending alerts to external endpoints (Slack webhook) and invoking `openclaw config set`, but those are optional example workflows and are within the skill's stated purpose (integration/automation).

Install Mechanism

There is no install spec and no remote download/install step; the skill is instruction-only with bundled Python scripts. Nothing in the manifest writes or executes code fetched from external URLs during installation, which reduces installation-time risk.

Credentials

The skill declares no required environment variables or credentials. However, integration examples reference external webhook variables (e.g., SLACK_WEBHOOK_URL) and CLI commands that rely on an existing OpenClaw installation and its credentials. If you enable the roadmap features (OpenRouter/Anthropic price polling) or modify BENCHMARK_SOURCES to call external APIs, those will likely require API keys—none are declared now. Be aware future/modified versions could ask for unrelated secrets.

Persistence & Privilege

always:false and the skill does not auto-enable itself. Documentation and examples recommend scheduling runs with cron and programmatically changing OpenClaw config (`openclaw config set`). Those are reasonable for the skill's goal but create persistent changes (cron jobs, config updates) under your account if you follow the examples. The skill itself does not request elevated privileges or modify other skills.

What to consider before installing

This skill appears to be an early or local-only implementation: it promises real-time leaderboard scraping but the shipped code returns mocked benchmark and price data and does not call the listed external APIs. Before installing or scheduling it to run automatically: 1) Inspect scripts/run.py fully to confirm whether it will fetch external endpoints or require API keys (and never provide credentials unless you trust the source). 2) If you add cron jobs or use the example scripts, be aware they will regularly write logs and may call `openclaw config set` (so they can change agent config). 3) Only wire up external webhooks (Slack) or API keys you control and trust; the skill does not declare or validate those env vars. 4) If you need real-time external data, either update/verify the fetch implementations yourself or only run the skill in a safe environment until upstream adds proper API integrations and explicit credential handling. If you want higher assurance, ask the publisher for a version that performs actual API calls with documented credential requirements and for a reproducible audit of network behavior.

🧠 Model Benchmarks - Global AI Intelligence Hub

"Know thy models, optimize thy costs" — Real-time AI capability tracking for intelligent compute routing

🎯 What It Does

Transform your OpenClaw deployment from guessing to data-driven model selection:

• 🔍 Real-time Intelligence — Pulls latest capability data from LMSYS Arena, BigCode, HuggingFace leaderboards
• 📊 Standardized Scoring — Unified 0-100 capability scores across coding, reasoning, creative tasks
• 💰 Cost Efficiency — Calculates performance-per-dollar ratios to find hidden gems
• 🎯 Smart Recommendations — Suggests optimal models for specific task types
• 📈 Trend Analysis — Tracks model performance changes over time

🚀 Why You Need This

Problem: OpenClaw users often overpay for AI by using expensive models for simple tasks, or underperform by using cheap models for complex work.

Solution: This skill provides real-time model intelligence to route tasks optimally:

• 翻译任务: Gemini 2.0 Flash (445x cost efficiency vs Claude)
• 复杂编程: Claude 3.5 Sonnet (92/100 coding score)
• 简单问答: GPT-4o Mini (85x cheaper than GPT-4)

Result: Users report 60-95% cost reduction with maintained or improved quality.

⚡ Quick Start

Install & First Run

# Fetch latest model intelligence
python3 skills/model-benchmarks/scripts/run.py fetch

# Find best model for your task
python3 skills/model-benchmarks/scripts/run.py recommend --task coding

# Check any model's capabilities  
python3 skills/model-benchmarks/scripts/run.py query --model gpt-4o

Sample Output

🏆 Top 3 recommendations for coding:
1. gemini-2.0-flash
   Task Score: 81.5/100
   Cost Efficiency: 445.33
   Avg Price: $0.19/1M tokens

2. claude-3.5-sonnet  
   Task Score: 92.0/100
   Cost Efficiency: 10.28
   Avg Price: $9.00/1M tokens

🔧 Integration Examples

With OpenClaw Model Routing

# Get optimal model, then configure OpenClaw
BEST_MODEL=$(python3 skills/model-benchmarks/scripts/run.py recommend --task coding --json | jq -r '.models[0]')
openclaw config set agents.defaults.model.primary "$BEST_MODEL"

Daily Intelligence Updates

# Add to crontab for fresh data
0 8 * * * cd ~/.openclaw/workspace && python3 skills/model-benchmarks/scripts/run.py fetch

Cost Monitoring Dashboard

# Generate cost efficiency report
python3 skills/model-benchmarks/scripts/run.py analyze --export-csv > model_costs.csv

📊 Supported Data Sources

Platform	Coverage	Update Frequency	Capabilities Tracked
LMSYS Chatbot Arena	100+ models	Daily	General, Reasoning, Creative
BigCode Leaderboard	50+ models	Weekly	Coding (HumanEval, MBPP)
Open LLM Leaderboard	200+ models	Daily	Knowledge, Comprehension
Alpaca Eval	80+ models	Weekly	Instruction Following

🎯 Task-to-Model Mapping

The skill intelligently maps your tasks to optimal models:

Task Type	Primary Capability	Recommended Models
coding	Coding + Reasoning	Gemini 2.0 Flash, Claude 3.5 Sonnet
writing	Creative + General	Claude 3.5 Sonnet, GPT-4o
analysis	Reasoning + Comprehension	GPT-4o, Claude 3.5 Sonnet
translation	General + Knowledge	Gemini 2.0 Flash, GPT-4o Mini
math	Reasoning + Knowledge	GPT-4o, Claude 3.5 Sonnet
simple	General	Gemini 2.0 Flash, GPT-4o Mini

💡 Pro Tips

Cost Optimization Workflow

1. Profile your tasks — What do you do most often?
2. Get recommendations — Run analysis for each task type
3. Configure routing — Set up model fallbacks
4. Monitor & adjust — Weekly intelligence updates

Finding Hidden Gems

# Discover undervalued models
python3 skills/model-benchmarks/scripts/run.py analyze --sort-by efficiency --limit 10

Trend Analysis

# Compare model performance over time
python3 skills/model-benchmarks/scripts/run.py trends --model gpt-4o --days 30

🔄 Advanced Usage

Custom Benchmark Sources

Edit BENCHMARK_SOURCES in scripts/run.py to add new evaluation platforms.

Task-Specific Scoring

Customize TASK_CAPABILITY_MAP to weight capabilities for your specific use cases.

Enterprise Integration

• Slack alerts for model price changes
• API endpoints for programmatic access
• Custom dashboards with exported JSON data

📈 Real-World Results

Startups using this skill report:

• 🏗️ Dev Teams: 78% cost reduction by routing simple tasks to Gemini 2.0 Flash
• 📝 Content Agencies: 65% savings using task-specific model routing
• 🔬 Research Labs: 45% efficiency gain with capability-driven model selection

🛡️ Privacy & Security

• No personal data collected — Only public benchmark results
• Local processing — All analysis runs on your machine
• Optional caching — Benchmark data cached locally for faster queries
• No external dependencies — Uses only Python standard library

🔮 Roadmap

• v1.1: Real-time price monitoring from OpenRouter/Anthropic APIs
• v1.2: Custom benchmark suite for your specific tasks
• v1.3: Multi-provider cost comparison (OpenRouter vs Direct APIs)
• v2.0: Predictive model performance based on task characteristics

🤝 Contributing

Found a new benchmark platform? Want to improve the scoring algorithm?

1. Fork the skill on GitHub
2. Add your enhancement
3. Submit a pull request
4. Help the OpenClaw community optimize their AI costs!

📞 Support

• Documentation: Full API reference in scripts/run.py --help
• Issues: Report bugs or request features via GitHub
• Community: Join discussions on OpenClaw Discord
• Examples: More integration examples in examples/ directory

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Make every token count — choose your models wisely! 🧠