Evaluate and rank agent performance metrics in AgentHub sessions.
npx skills add https://github.com/4lerman/text_evaluator --skill eval-4lerman
Please help me install this Agent Skill. Skill: eval Source: https://github.com/4lerman/text_evaluator/tree/main/.agents/skills/engineering-advanced-skills/agenthub/skills/eval Command: npx skills add https://github.com/4lerman/text_evaluator --skill eval-4lerman
The eval Skill solves the problem of efficiently ranking and evaluating agent results in an AgentHub session, ensuring clear and fair performance assessments.
Run the eval Skill by entering the command /hub:eval to evaluate the latest session.
High-intent search queries and answers about installing and using this skill.
You can evaluate agent performance in an AgentHub session by running the eval Skill, which automates performance evaluation and ranking based on your session metrics.
Yes, you can use the LLM judge mode to rank agent results. This Skill supports both metric-based evaluation and LLM judge comparisons to suit different performance assessment needs.
To combine metric analysis and LLM judgment for agent ranking, use the hybrid evaluation mode. This approach integrates both metric-based and LLM judge rankings to provide a more comprehensive evaluation.
No, the eval Skill does not require any external dependencies. It operates entirely within the AgentHub environment to provide an efficient and accurate evaluation process.
The best way to automate agent ranking for your latest session is to enter the command `/hub:eval`. This triggers the evaluation process and generates a clear ranking system automatically.
Veloxia-agencyInitialize an AgentHub session directory with configuration and optional baseline.
ExuberantWitnessDevelop, benchmark, and optimize modular AI agent skills with automated testing.
whatagraphCreate, update, and audit metric-based marketing goals with Whatagraph MCP tools.
Patasse97Rank AgentHub session agent results using metrics or an LLM judge.
