Setting Up Synthetic Monitoring
About
This skill automates the configuration of synthetic monitoring for applications, enabling proactive tracking of uptime, transactions, and API performance. Developers should use it when they need to set up monitoring, track application performance, or configure alerts and dashboards. It guides users to identify critical endpoints, design monitoring scenarios, and establish alerting systems.
Documentation
Overview
This skill streamlines the process of setting up synthetic monitoring, enabling proactive performance tracking for applications. It guides the user through defining key monitoring scenarios and configuring alerts to ensure optimal application performance and availability.
How It Works
- Identify Monitoring Needs: Determine the critical endpoints, user journeys, and APIs to monitor based on the user's application requirements.
- Design Monitoring Scenarios: Create specific monitoring scenarios for uptime, transactions, and API performance, including frequency and location.
- Configure Monitoring: Set up the synthetic monitoring tool with the designed scenarios, including alerts and dashboards for performance visualization.
When to Use This Skill
This skill activates when you need to:
- Implement uptime monitoring for a web application.
- Track the performance of critical user journeys through transaction monitoring.
- Monitor the response time and availability of API endpoints.
Examples
Example 1: Setting up Uptime Monitoring
User request: "Set up uptime monitoring for my website example.com."
The skill will:
- Identify example.com as the target endpoint.
- Configure uptime monitoring to check the availability of example.com every 5 minutes from multiple locations.
Example 2: Monitoring API Performance
User request: "Configure API monitoring for the /users endpoint of my application."
The skill will:
- Identify the /users endpoint as the target for API monitoring.
- Set up monitoring to track the response time and status code of the /users endpoint every minute.
Best Practices
- Prioritize Critical Endpoints: Focus on monitoring the most critical endpoints and user journeys that directly impact user experience.
- Set Realistic Thresholds: Configure alerts with realistic thresholds to avoid false positives and ensure timely notifications.
- Regularly Review and Adjust: Periodically review the monitoring configuration and adjust scenarios and thresholds based on application changes and performance trends.
Integration
This skill can be integrated with other plugins for incident management and alerting, such as those that handle notifications via Slack or PagerDuty, allowing for automated incident response workflows based on synthetic monitoring results.
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.