Analyzing System Throughput
About
This skill enables Claude to analyze and optimize system throughput using the `throughput-analyzer` plugin. It triggers when users request performance improvements, bottleneck identification, or capacity analysis. The skill assesses request/data throughput, concurrency, queue processing, and resource saturation to determine limiting factors and evaluate scaling strategies.
Documentation
Overview
This skill allows Claude to analyze system performance and identify areas for throughput optimization. It uses the throughput-analyzer plugin to provide insights into request handling, data processing, and resource utilization.
How It Works
- Identify Critical Components: Determines which system components are most relevant to throughput.
- Analyze Throughput Metrics: Gathers and analyzes current throughput metrics for the identified components.
- Identify Limiting Factors: Pinpoints the bottlenecks and constraints that are hindering optimal throughput.
- Evaluate Scaling Strategies: Explores potential scaling strategies and their impact on overall throughput.
When to Use This Skill
This skill activates when you need to:
- Analyze system throughput to identify performance bottlenecks.
- Optimize system performance for increased capacity.
- Evaluate scaling strategies to improve throughput.
Examples
Example 1: Analyzing Web Server Throughput
User request: "Analyze the throughput of my web server and identify any bottlenecks."
The skill will:
- Activate the
throughput-analyzerplugin. - Analyze request throughput, data throughput, and resource saturation of the web server.
- Provide a report identifying potential bottlenecks and optimization opportunities.
Example 2: Optimizing Data Processing Pipeline
User request: "Optimize the throughput of my data processing pipeline."
The skill will:
- Activate the
throughput-analyzerplugin. - Analyze data throughput, queue processing, and concurrency limits of the data processing pipeline.
- Suggest improvements to increase data processing rates and overall throughput.
Best Practices
- Component Selection: Focus the analysis on the most throughput-critical components to avoid unnecessary overhead.
- Metric Interpretation: Carefully interpret throughput metrics to accurately identify limiting factors.
- Scaling Evaluation: Thoroughly evaluate the potential impact of scaling strategies before implementation.
Integration
This skill can be used in conjunction with other monitoring and performance analysis tools to gain a more comprehensive understanding of system behavior. It provides a starting point for further investigation and optimization efforts.
Quick Install
/plugin add https://github.com/jeremylongshore/claude-code-plugins-plus-skills/tree/main/skill-adapterCopy and paste this command in Claude Code to install this skill
GitHub 仓库
Related Skills
sglang
MetaSGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.
evaluating-llms-harness
TestingThis Claude Skill runs the lm-evaluation-harness to benchmark LLMs across 60+ standardized academic tasks like MMLU and GSM8K. It's designed for developers to compare model quality, track training progress, or report academic results. The tool supports various backends including HuggingFace and vLLM models.
llamaguard
OtherLlamaGuard is Meta's 7-8B parameter model for moderating LLM inputs and outputs across six safety categories like violence and hate speech. It offers 94-95% accuracy and can be deployed using vLLM, Hugging Face, or Amazon SageMaker. Use this skill to easily integrate content filtering and safety guardrails into your AI applications.
langchain
MetaLangChain is a framework for building LLM applications using agents, chains, and RAG pipelines. It supports multiple LLM providers, offers 500+ integrations, and includes features like tool calling and memory management. Use it for rapid prototyping and deploying production systems like chatbots, autonomous agents, and question-answering services.