4-stage evaluation framework for testing Claude Code plugin component triggering. Validates skills, agents, and commands activate correctly via programmatic detection and LLM judgment.
git clone https://github.com/sjnims/cc-plugin-eval.gitThe cc-plugin-eval skill provides a robust 4-stage evaluation framework specifically designed for testing Claude Code plugin components. This skill enables developers and AI practitioners to ensure that skills, agents, and commands are activated correctly through both programmatic detection and LLM judgment. By implementing this framework, users can systematically validate their plugin functionality, ensuring a seamless integration into their existing workflows. One of the key benefits of the cc-plugin-eval skill is its ability to enhance the reliability of AI automation processes. While the exact time savings are not quantified, the structured evaluation reduces the likelihood of errors and the need for extensive debugging, ultimately leading to a more efficient development cycle. This skill is particularly beneficial for developers and product managers who are focused on maintaining high-quality standards in their AI projects, allowing them to allocate more time to innovation rather than troubleshooting. This skill is ideal for developers, product managers, and AI practitioners who are involved in creating or managing AI agents and automation workflows. Its intermediate complexity means that users should have a foundational understanding of Claude Code and AI automation principles, making it suitable for teams looking to enhance their AI-first workflows. By integrating the cc-plugin-eval skill, teams can improve their testing processes, leading to faster deployment and better-performing AI agents. Implementation of the cc-plugin-eval skill is straightforward, with an estimated time of 30 minutes to set up. This makes it an accessible option for teams looking to incorporate a reliable testing framework into their existing processes. As AI automation continues to evolve, having a structured approach to plugin evaluation will be crucial in ensuring that AI agents perform optimally, thereby enhancing overall productivity and effectiveness in AI-driven projects.
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/sjnims/cc-plugin-evalCopy 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 a Claude Code plugin using the 4-stage framework. The plugin is for [COMPANY] in the [INDUSTRY] sector. Here's the [DATA] to test: [INPUT]. Validate if the skills, agents, and commands trigger correctly. Provide programmatic detection results and LLM judgment.
## Claude Code Plugin Evaluation Report ### Stage 1: Skill Triggering - **Programmatic Detection**: Skill 'data_analysis' triggered successfully - **LLM Judgment**: Skill output matches expected format and content ### Stage 2: Agent Activation - **Programmatic Detection**: Agent 'data_processor' activated - **LLM Judgment**: Agent handled data as expected ### Stage 3: Command Execution - **Programmatic Detection**: Command 'generate_report' executed - **LLM Judgment**: Report generated with correct data ### Stage 4: Overall Assessment - **Programmatic Detection**: All components triggered - **LLM Judgment**: Plugin performed as intended with no errors
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