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MadAppGang
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performance-tracking

Track and analyze AI agent, skill, and model performance metrics.

7|4|Updated Feb 26, 2026
Search Tags:#performance#routing#optimization#metrics#tracking#agent performance#model cost
One-click install
npx skills add https://github.com/MadAppGang/magus --skill performance-tracking
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: performance-tracking
Source: https://github.com/MadAppGang/magus/tree/main/plugins/multimodel/skills/performance-tracking
Command: npx skills add https://github.com/MadAppGang/magus --skill performance-tracking

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of understanding and improving the efficiency, cost-effectiveness, and success rates of AI agents, skills, and models within a workflow.

Core Features & Use Cases

  • Performance Monitoring: Track key metrics like success rates, latency, and cost for agents, skills, and models.
  • Optimization Insights: Identify underperforming agents, cost-inefficient models, and opportunities to refine routing strategies.
  • Use Case: A team notices their AI development process is slow and expensive. By using this Skill, they can pinpoint that a specific agent has a low success rate on complex tasks and that a cheaper model performs nearly as well for simpler tasks, leading to targeted improvements and cost savings.

Quick Start

Use the performance-tracking skill to analyze agent success rates over the last week.

Frequently Asked Questions about performance-tracking

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I track AI agent success rates and latency in my workflow?▼

You can track AI model cost per task by using a performance monitoring skill that records cost metrics alongside success rates and latency. It stores historical data in structured JSON to provide actionable insights for cost optimization.

What is AI routing effectiveness and how does tracking it improve workflows?▼

AI routing effectiveness measures how well tasks are assigned to appropriate agents or models. Tracking routing effectiveness reveals cost-inefficient models and underperforming agents, enabling targeted workflow improvements and cost savings.

Can I analyze model cost per task to find cheaper AI alternatives?▼

Yes, you can analyze model cost per task to identify cost-inefficient models. Performance tracking reveals when a cheaper model performs nearly as well for simpler tasks, enabling targeted cost savings and routing strategy refinements.

Do I need persistent storage for AI performance metrics tracking?▼

Yes, persistent storage is required for AI performance tracking. The skill stores historical data in a structured JSON format to enable trend analysis and generate actionable optimization insights over time.

How do I identify underperforming AI agents in my development process?▼

You can identify underperforming AI agents by monitoring performance metrics like success rates on complex tasks. Tracking these metrics over time pinpoints specific agents requiring targeted improvements or routing strategy adjustments.

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