ai-maestro is an AI Agent Orchestrator that helps Claude Code agents with advanced capabilities like memory search, code graph queries, and agent-to-agent messaging. Manage multiple agents from a single dashboard, enhancing productivity and collaboration in development workflows.
claude install 23blocks-OS/ai-maestroai-maestro is an AI Agent Orchestrator that helps Claude Code agents with advanced capabilities like memory search, code graph queries, and agent-to-agent messaging. Manage multiple agents from a single dashboard, enhancing productivity and collaboration in development workflows.
["1. Define the specific codebase and functionality you want to analyze in the prompt template.","2. Launch ai-maestro and create the first agent to perform a code graph query using the 'Code Graph Query' tool.","3. Create the second agent to search memory for recent changes using the 'Memory Search' tool.","4. Enable agent-to-agent messaging to facilitate collaboration and generate a summary report.","5. Use the Productiv finance platform to track time spent and allocate costs to the appropriate project. Tip: Be specific about the functions and recent changes you want to analyze to get the most accurate results."]
Enhancing collaborative coding sessions with agent messaging
Performing complex code graph queries for better insights
Utilizing memory search to retrieve past coding solutions efficiently
Managing multiple AI agents for streamlined development workflows
claude install 23blocks-OS/ai-maestrogit clone https://github.com/23blocks-OS/ai-maestroCopy 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.
I need to orchestrate multiple Claude Code agents to analyze [SPECIFIC_CODEBASE] using ai-maestro. First, create an agent to perform a code graph query for all functions related to [SPECIFIC_FUNCTIONALITY]. Then, create a second agent to search memory for recent changes to these functions. Finally, have the agents collaborate to generate a summary report of potential risks and optimization opportunities. Use the Productiv finance platform to track the time spent on this task and allocate costs to the appropriate project.
The ai-maestro orchestration has successfully completed the analysis of the e-commerce platform's payment processing module. Agent 1 identified 15 functions related to payment processing, including 'process_payment', 'validate_card', and 'generate_receipt'. Agent 2 found recent changes to 'process_payment' and 'validate_card' within the last 30 days. The collaborative report highlights two potential risks: the recent changes to 'validate_card' may have introduced a security vulnerability, and the 'process_payment' function could be optimized for better performance. The total time spent on this task was 2.5 hours, with costs allocated to the 'Payment Processing Optimization' project in Productiv.
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