A web UI for Claude Code agents. Operations teams use it to manage and interact with AI agents. It connects to Claude Code agents and provides a visual interface for agent operations.
git clone https://github.com/wbopan/cui.gitThe cui skill provides a web user interface designed specifically for managing and monitoring multiple Claude Code agent sessions. This intermediate-level automation skill simplifies the interaction with AI agents, allowing users to fork existing tasks into new branches seamlessly. By utilizing dictation features, it streamlines task creation, making it easier to input ideas and commands accurately. Users can also receive real-time notifications for task completions or when user input is required, ensuring that workflows remain efficient and organized. One of the key benefits of the cui skill is its potential to enhance productivity by consolidating multiple agent sessions into a single interface. This not only saves time but also reduces the complexity of managing various tasks across different agents. While the exact time savings are not quantified, the ability to access historical task data significantly improves workflow continuity, allowing teams to pick up from where they left off without losing progress. This is particularly beneficial for teams engaged in ongoing projects that require constant iteration and updates. The cui skill is ideal for developers, product managers, and AI practitioners who are looking to optimize their workflow automation processes. By providing a centralized platform to manage AI agent skills, it caters to those who work in environments that prioritize efficiency and streamlined operations. The skill’s medium GTM relevance indicates that it is well-suited for teams that are integrating AI into their existing workflows but may not yet be fully AI-first. Implementing the cui skill is straightforward, requiring approximately 30 minutes to set up. Given its intermediate complexity, users should have a basic understanding of Claude Code and AI automation principles to fully leverage its capabilities. As organizations increasingly adopt AI-driven solutions, the cui skill serves as a valuable tool in enhancing productivity and ensuring that teams can manage their AI agents effectively, ultimately contributing to a more efficient AI-first workflow.
[{"step":"Access the CUI interface and authenticate with your agent credentials.","tip":"Use the same credentials you configured in your Claude Code agent settings for seamless integration."},{"step":"Navigate to the 'Agents' tab and select the specific agent you want to manage.","tip":"Filter agents by project name or status (e.g., 'running', 'idle') to quickly locate your target agent."},{"step":"Use the 'Operations' panel to initiate tasks or review agent logs.","tip":"For complex tasks, break them into smaller steps and monitor progress in real-time using the live log viewer."},{"step":"After task completion, export the audit report for documentation or further analysis.","tip":"Save reports in JSON format for integration with your existing monitoring tools or ticketing systems."},{"step":"For recurring tasks, create automation scripts using the CUI's API endpoints.","tip":"Refer to the CUI documentation for API rate limits and authentication requirements."}]
Manage and monitor multiple Claude Code agent sessions from a single web interface.
Fork existing tasks to create new branches for ongoing projects without losing previous progress.
Utilize dictation features for accurate voice input, streamlining the task creation process.
Receive real-time notifications when tasks are completed or require user input.
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/wbopan/cuiCopy 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.
Use the CUI web interface to [TASK] for [AGENT_NAME] in the [PROJECT_NAME] project. Focus on [SPECIFIC_GOAL] and ensure all steps are logged in the audit trail. [ADDITIONAL_CONTEXT_IF_NEEDED].
``` # Agent Operations Report for 'data-pipeline-agent' in 'analytics-project' ## Task: Deploy updated ETL pipeline to production **Status**: Completed successfully **Duration**: 12 minutes 47 seconds **Agent Logs**: - 14:23:45: Validated data schema changes against staging environment - 14:25:12: Initiated blue-green deployment to production cluster - 14:27:33: Verified 0 errors in 1,247 records processed - 14:36:32: Updated monitoring dashboards with new metrics ## Key Metrics: - Pipeline throughput: 1.8x improvement (from 450 to 810 records/sec) - Error rate: 0.02% (below SLA threshold of 0.1%) - Resource utilization: CPU +12%, Memory -8% ## Recommendations: 1. Schedule performance tuning for next maintenance window 2. Update runbook with new error handling procedures 3. Tag this deployment as 'v2.1.0' in version control ```
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