Llm Monitoring Dashboard
Helps with LLMs, monitoring as part of building AI and machine learning applications workflows.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add llm-monitoring-dashboard npx -- -y @trustedskills/llm-monitoring-dashboard
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"llm-monitoring-dashboard": {
"command": "npx",
"args": [
"-y",
"@trustedskills/llm-monitoring-dashboard"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
The LLM Monitoring Dashboard provides real-time insights into the performance and behavior of large language models. It tracks metrics such as response time, token usage, error rates, and model accuracy to help users optimize and troubleshoot their AI systems effectively.
When to use it
- During model training or inference to monitor for anomalies in performance.
- To analyze trends in query latency and resource consumption over time.
- When debugging issues with model outputs or unexpected behavior.
- For capacity planning and ensuring efficient utilization of computational resources.
Key capabilities
- Real-time tracking of model response times and error rates
- Visualization of token usage patterns across different queries
- Historical performance analysis for trend identification
- Integration with multiple LLMs for centralized monitoring
Example prompts
- "Show me the average response time for the last 24 hours."
- "Generate a report on token usage by model type over the past week."
- "Highlight any unusual spikes in error rates during the morning hours."
Tips & gotchas
- Ensure that your LLMs are properly configured to send performance metrics to the dashboard.
- Use this tool alongside logging and alerting systems for comprehensive monitoring.
Tags
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Security Audits
| Gen Agent Trust Hub | Pass |
| Socket | Pass |
| Snyk | Pass |
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Passed automated security scans.