Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Productivity
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
skill openclawclawhub Free
Real-time AI model capability tracking via leaderboards (LMSYS Arena, HuggingFace, etc.) for intelligent compute routing and cost optimization
โฌ 0 downloads โ
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- README.md, SKILL.md, scripts/run.py, benchmarks/latest.json, benchmarks/2026-03-01.json, examples/daily-optimization.sh
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
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New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
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- Notestone
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Documentation
๐ง Model Benchmarks - Global AI Intelligence Hub
"Know thy models, optimize thy costs" โ Real-time AI capability tracking for intelligent compute routing
๐ฏ What It Does
Transform your OpenClaw deployment from guessing to data-driven model selection: ๐ Real-time Intelligence โ Pulls latest capability data from LMSYS Arena, BigCode, HuggingFace leaderboards ๐ Standardized Scoring โ Unified 0-100 capability scores across coding, reasoning, creative tasks ๐ฐ Cost Efficiency โ Calculates performance-per-dollar ratios to find hidden gems ๐ฏ Smart Recommendations โ Suggests optimal models for specific task types ๐ Trend Analysis โ Tracks model performance changes over time
๐ 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: ็ฟป่ฏไปปๅก: Gemini 2.0 Flash (445x cost efficiency vs Claude) ๅคๆ็ผ็จ: Claude 3.5 Sonnet (92/100 coding score) ็ฎๅ้ฎ็ญ: GPT-4o Mini (85x cheaper than GPT-4) Result: Users report 60-95% cost reduction with maintained or improved quality.
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
๐ 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: $9.00/1M tokens
With OpenClaw Model Routing
# Get optimal model, then configure OpenClaw BEST_MODEL=$(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
๐ Supported Data Sources
PlatformCoverageUpdate FrequencyCapabilities TrackedLMSYS Chatbot Arena100+ modelsDailyGeneral, Reasoning, CreativeBigCode Leaderboard50+ modelsWeeklyCoding (HumanEval, MBPP)Open LLM Leaderboard200+ modelsDailyKnowledge, ComprehensionAlpaca Eval80+ modelsWeeklyInstruction Following
๐ฏ Task-to-Model Mapping
The skill intelligently maps your tasks to optimal models: Task TypePrimary CapabilityRecommended ModelscodingCoding + ReasoningGemini 2.0 Flash, Claude 3.5 SonnetwritingCreative + GeneralClaude 3.5 Sonnet, GPT-4oanalysisReasoning + ComprehensionGPT-4o, Claude 3.5 SonnettranslationGeneral + KnowledgeGemini 2.0 Flash, GPT-4o MinimathReasoning + KnowledgeGPT-4o, Claude 3.5 SonnetsimpleGeneralGemini 2.0 Flash, GPT-4o Mini
Cost Optimization Workflow
Profile your tasks โ What do you do most often? Get recommendations โ Run analysis for each task type Configure routing โ Set up model fallbacks Monitor & adjust โ 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
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
๐ Real-World Results
Startups using this skill report: ๐๏ธ Dev Teams: 78% cost reduction by routing simple tasks to Gemini 2.0 Flash ๐ Content Agencies: 65% savings using task-specific model routing ๐ฌ Research Labs: 45% efficiency gain with capability-driven model selection
๐ก๏ธ Privacy & Security
No personal data collected โ Only public benchmark results Local processing โ All analysis runs on your machine Optional caching โ Benchmark data cached locally for faster queries No external dependencies โ Uses only Python standard library
๐ฎ 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
๐ค Contributing
Found a new benchmark platform? Want to improve the scoring algorithm? Fork the skill on GitHub Add your enhancement Submit a pull request Help the OpenClaw community optimize their AI costs!
๐ 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 Make every token count โ choose your models wisely! ๐ง
Category context
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Source: Tencent SkillHub
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Package contents
Included in package
2 Docs2 Scripts2 Config
- SKILL.md
- README.md
- examples/daily-optimization.sh
- scripts/run.py
- benchmarks/2026-03-01.json
- benchmarks/latest.json