Can this skill automatically fix performance issues?

Yes, by using the --fix flag with the bottleneck detection command, the skill can apply optimizations such as topology shifts and caching enhancements.

What formats are supported for performance reports?

You can generate reports in HTML for visualization, Markdown for documentation, or JSON for programmatic use and CI/CD integration.

How does it help with agent coordination?

It analyzes coordination overhead and workload balance, recommending improvements such as switching from mesh to hierarchical topologies for better efficiency.

What types of bottlenecks can this skill detect?

The skill identifies delays in agent communication, task processing inefficiencies, memory access patterns, and network API latency.

Performance Analysis for Claude Flow

Name: Performance Analysis for Claude Flow
Author: ricable

byricable

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Analytics & Monitoring

Analyzes bottlenecks, profiles agent operations, and provides actionable optimization recommendations for AI agent swarms.

About

This diagnostic suite is designed for developers building multi-agent systems with Claude Flow, offering a robust set of tools to monitor and enhance swarm efficiency. It identifies critical bottlenecks across communication, processing, memory, and network layers, providing deep insights into agent utilization and resource contention. Whether you need to generate detailed HTML performance reports for stakeholders or apply automated 'quick fixes' to optimize agent topology and caching, this skill ensures your AI workflows operate with minimal latency and maximum throughput.

Key Features

Multi-domain Bottleneck Detection covering communication, processing, memory, and network
Automated Optimization Recommendations with 'Quick Fix' CLI capabilities
Comprehensive Report Generation in HTML, Markdown, and JSON formats
CI/CD Integration support for automated performance auditing and threshold checks
Real-time Performance Profiling for agent utilization and resource patterns
2 GitHub stars

Use Cases

Debugging latency issues in complex inter-agent communication and task handoffs
Tracking performance regressions during AI agent development cycles
Optimizing resource allocation and agent topology for large-scale AI deployments

About

Key Features

Multi-domain Bottleneck Detection covering communication, processing, memory, and network
Automated Optimization Recommendations with 'Quick Fix' CLI capabilities
Comprehensive Report Generation in HTML, Markdown, and JSON formats
CI/CD Integration support for automated performance auditing and threshold checks
Real-time Performance Profiling for agent utilization and resource patterns
2 GitHub stars

Use Cases

Debugging latency issues in complex inter-agent communication and task handoffs
Tracking performance regressions during AI agent development cycles
Optimizing resource allocation and agent topology for large-scale AI deployments