Showing 12 of 56804 results
ruvnet/claude-flow
Generates precise, structured agent specifications with modular components, defining roles, capabilities, constraints, and interaction protocols for AI system design.
ruvnet/claude-flow
Orchestrates multiple AI agents to collaboratively solve complex problems through dynamic task allocation, communication, and parallel processing
ruvnet/claude-flow
Orchestrates distributed memory management for AI agent swarms, enabling efficient context sharing, retrieval, and collaborative knowledge retention across multiple agents.
ruvnet/claude-flow
Synchronizes and coordinates multi-agent workflows, ensuring seamless communication, task allocation, and state management across distributed AI systems.
ruvnet/claude-flow
Automates test-driven development workflows using London-style testing, swarm intelligence, and collaborative agent-based strategies for complex software projects.
ruvnet/claude-flow
Automated testing framework for Claude AI agents, generating comprehensive test scenarios, validating responses, and assessing performance metrics.
1nferencesh/s
Streamlines AI agent development by providing modular, reusable tools for task planning, execution, and intelligent workflow management.
ruvnet/claude-flow
Optimizes complex multi-agent network architectures by analyzing interaction patterns, resource allocation, and communication efficiency for scalable AI systems.
ruvnet/claude-flow
Streamlines user interaction workflows by providing intelligent tool selection, context management, and adaptive communication strategies for AI agents.
ruvnet/claude-flow
Designs and implements advanced AI agent architectures, orchestrating complex multi-agent workflows and integration strategies for scalable enterprise solutions.
ruvnet/claude-flow
Optimizes long-term memory management for Claude agents, enabling efficient context retention, retrieval, and dynamic knowledge integration across conversations.
ruvnet/claude-flow
Optimizes code performance by analyzing bottlenecks, recommending refactoring strategies, and providing targeted improvements for system efficiency