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Agent Evaluation

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supercent-io·vlatest·agents

Supercent-io's agent-evaluation assesses agent performance against defined metrics, providing actionable insights for improvement.

Repository

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This skill no longer installs under the name agent-evaluation. supercent-io/skills-template is deleted or private, so it cannot be installed from.

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Repository install instructions

What we know about this skill

Publisher

supercent-io

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

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Not scanned. repository not found or private Source recorded as supercent-io/skills-template.
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About this skill

What it does

The agent-evaluation skill enables users to assess and compare AI agents based on predefined criteria such as performance, accuracy, efficiency, and user feedback. It provides structured evaluation frameworks that help identify strengths and weaknesses of different agents in specific use cases.

When to use it

  • Evaluating multiple AI agents for a project to determine the best fit
  • Comparing agent outputs against ground truth data or benchmarks
  • Gathering user feedback on agent performance in real-world scenarios

Key capabilities

  • Structured evaluation frameworks
  • Performance benchmarking
  • User feedback analysis
  • Comparative agent scoring

Example prompts

  • "Compare Agent A and Agent B based on response accuracy and speed."
  • "Evaluate this AI agent's performance using the latest dataset."
  • "Generate a report comparing three agents in terms of user satisfaction and task completion rates."

Tips & gotchas

  • Ensure all agents being evaluated are trained on similar datasets for fair comparison.
  • Use consistent metrics across evaluations to maintain reliability and avoid bias.