Is it possible to integrate this into a CI/CD pipeline?

Absolutely. The skill is designed for automation, allowing you to run performance checks and export JSON results during build or deployment stages to catch regressions.

What formats are available for the performance reports?

The skill supports multiple export formats including HTML for visual review, JSON for programmatic analysis, and Markdown for project documentation.

How does this skill identify performance bottlenecks?

It analyzes metrics such as message queue delays, agent response times, resource contention, and network latency to pinpoint the specific components slowing down your swarm execution.

Can I use this skill to automatically fix performance issues?

Yes, by using the --fix flag with the bottleneck command, the skill can automatically apply optimizations like topology changes, smart caching, and priority adjustments.

Performance Analysis for Claude Flow

Name: Performance Analysis for Claude Flow
Author: ellisapotheosis

byellisapotheosis

Analytics & Monitoring

Analyzes and optimizes Claude Flow swarm performance by detecting bottlenecks and providing actionable architectural recommendations.

About

The Performance Analysis skill provides a comprehensive suite for monitoring and optimizing multi-agent Claude Flow swarms. It identifies critical bottlenecks across communication channels, processing tasks, memory access, and network latency, offering both detailed manual reports and automated optimization fixes. Whether you are scaling a complex agentic workflow or debugging slow response times, this tool provides the real-time profiling and historical analysis needed to ensure peak efficiency and resource utilization in your AI-driven applications.

Key Features

AI-powered optimization suggestions and automated 'quick-fix' capabilities
Comprehensive report generation in HTML, JSON, and Markdown formats
Seamless integration with MCP and CI/CD pipelines for continuous monitoring
Automated bottleneck detection across communication, memory, and processing
0 GitHub stars
Real-time profiling of swarm operations and agent utilization rates

Use Cases

Automated architectural tuning to improve task throughput and resource management
Identifying and resolving latency issues in multi-agent coordination swarms
Generating performance benchmarks and audit reports for production AI workflows

About

Key Features

AI-powered optimization suggestions and automated 'quick-fix' capabilities
Comprehensive report generation in HTML, JSON, and Markdown formats
Seamless integration with MCP and CI/CD pipelines for continuous monitoring
Automated bottleneck detection across communication, memory, and processing
0 GitHub stars
Real-time profiling of swarm operations and agent utilization rates

Use Cases

Automated architectural tuning to improve task throughput and resource management
Identifying and resolving latency issues in multi-agent coordination swarms
Generating performance benchmarks and audit reports for production AI workflows