Skill Judge is an AI automation skill designed to evaluate and assess the performance of various AI agents. It streamlines the process of performance evaluation, saving developers and product managers valuable time while enhancing the efficiency of AI workflows.
git clone https://github.com/softaworks/agent-toolkit.gitSkill Judge is a powerful Claude Code skill that focuses on evaluating the performance of AI agents. By automating the assessment process, this skill allows developers and product managers to quickly determine the effectiveness of their AI implementations. It leverages predefined metrics to analyze various aspects of AI performance, ensuring that users can make informed decisions based on accurate data. One of the key benefits of Skill Judge is its ability to save time. Instead of manually evaluating AI agents, users can rely on this skill to provide instant feedback and insights. This not only accelerates the development cycle but also enhances the overall quality of AI solutions. With 1775 installs, it has proven to be a reliable tool for those looking to optimize their AI workflows. Skill Judge is particularly beneficial for developers, product managers, and AI practitioners who are involved in the deployment and monitoring of AI systems. It is ideal for teams looking to validate their AI models before full-scale deployment. Practical use cases include assessing chatbot performance, evaluating recommendation systems, and analyzing predictive analytics models. For example, a product manager can use Skill Judge to ensure that a customer support chatbot meets performance benchmarks before launch. The implementation of Skill Judge is straightforward, making it accessible even for those who may not have extensive experience with AI automation. By integrating this skill into existing AI-first workflows, teams can enhance their operational efficiency and ensure that their AI agents consistently meet performance standards. Overall, Skill Judge is a valuable addition to any AI toolkit, providing essential insights that drive better decision-making.
1. Identify the AI agent you want to evaluate and the specific task you want to assess. Be as detailed as possible about the task. 2. Use the provided prompt template to create your evaluation request. Make sure to include the AI agent's name, the specific task, and the criteria you want to use for evaluation. 3. Submit the request to your preferred AI tool, such as Claude or ChatGPT. 4. Review the evaluation results and suggested improvements. Use this information to refine and enhance the AI agent's performance. 5. For better results, provide as much context as possible about the AI agent and the task. This will help the evaluator provide more accurate and relevant feedback.
Assessing the performance of customer support chatbots
Evaluating the effectiveness of recommendation algorithms
Monitoring predictive analytics models for accuracy
Benchmarking AI agents against industry standards
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/softaworks/agent-toolkitCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Evaluate the performance of [AI_AGENT_NAME] on the task of [SPECIFIC_TASK]. Use the following criteria: accuracy, speed, and creativity. Provide a score out of 10 for each criterion and a brief explanation for each score. Additionally, suggest 2-3 improvements the agent could make to enhance its performance.
After evaluating the performance of 'DataSift AI' on the task of generating market research reports, here are the results: 1. Accuracy: 8/10 - The reports are generally accurate, but there were a few instances where the data interpretation was slightly off. For example, the report on Q3 sales trends misinterpreted a 5% increase as a 15% increase due to a misplaced decimal point. 2. Speed: 9/10 - The agent completed the task in under 10 minutes, which is impressive. However, it could be faster if it parallelized some of its data-gathering processes. 3. Creativity: 7/10 - The reports are well-structured and informative, but they lack a bit of creativity in presentation. Incorporating more visual aids and varied report formats could enhance the user experience. Suggested improvements: - Implement a double-check mechanism for data interpretation to improve accuracy. - Explore parallel processing techniques to further reduce task completion time. - Incorporate more creative elements, such as infographics and interactive charts, to make the reports more engaging.
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