collecting-infrastructure-metrics
About
This skill automates the setup of comprehensive infrastructure monitoring by collecting performance metrics across compute, storage, network, and databases. It configures collection agents, sets up aggregation, and helps create dashboards for health and capacity tracking. Developers should use it when needing to implement monitoring with tools like Prometheus, Datadog, or CloudWatch.
Documentation
Overview
This skill automates the process of setting up infrastructure metrics collection. It identifies key performance indicators (KPIs) across various infrastructure layers, configures agents to collect these metrics, and assists in setting up central aggregation and visualization.
How It Works
- Identify Infrastructure Layers: Determines the infrastructure layers to monitor (compute, storage, network, containers, load balancers, databases).
- Configure Metrics Collection: Sets up agents (Prometheus, Datadog, CloudWatch) to collect metrics from the identified layers.
- Aggregate Metrics: Configures central aggregation of the collected metrics for analysis and visualization.
- Create Dashboards: Generates infrastructure dashboards for health monitoring, performance analysis, and capacity tracking.
When to Use This Skill
This skill activates when you need to:
- Monitor the performance of your infrastructure.
- Identify bottlenecks in your system.
- Set up dashboards for real-time monitoring.
Examples
Example 1: Setting up basic monitoring
User request: "Collect infrastructure metrics for my web server."
The skill will:
- Identify compute, storage, and network layers relevant to the web server.
- Configure Prometheus to collect CPU, memory, disk I/O, and network bandwidth metrics.
Example 2: Troubleshooting database performance
User request: "I'm seeing slow database queries. Can you help me monitor the database performance?"
The skill will:
- Identify the database layer and relevant metrics such as connection pool usage, replication lag, and cache hit rates.
- Configure Datadog to collect these metrics and create a dashboard to visualize performance trends.
Best Practices
- Agent Selection: Choose the appropriate agent (Prometheus, Datadog, CloudWatch) based on your existing infrastructure and monitoring tools.
- Metric Granularity: Balance the granularity of metrics collection with the storage and processing overhead. Collect only the essential metrics for your use case.
- Alerting: Configure alerts based on thresholds for key metrics to proactively identify and address performance issues.
Integration
This skill can be integrated with other Claude Code plugins for deployment, configuration management, and alerting to provide a comprehensive infrastructure management solution. For example, it can be used with a deployment plugin to automatically configure metrics collection after deploying new infrastructure.
Quick Install
/plugin add https://github.com/jeremylongshore/claude-code-plugins-plus/tree/main/infrastructure-metrics-collectorCopy 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.