Back to Skills

tracking-service-reliability

jeremylongshore
Updated Yesterday
21 views
712
74
712
View on GitHub
Otherai

About

This skill helps developers define and track service reliability metrics like SLAs, SLIs, and SLOs for availability, latency, and error rates. Use it when establishing reliability targets or monitoring ongoing service health. It automates setting performance targets and calculating error budgets based on your defined indicators.

Quick Install

Claude Code

Recommended
Plugin CommandRecommended
/plugin add https://github.com/jeremylongshore/claude-code-plugins-plus
Git CloneAlternative
git clone https://github.com/jeremylongshore/claude-code-plugins-plus.git ~/.claude/skills/tracking-service-reliability

Copy and paste this command in Claude Code to install this skill

Documentation

Overview

This skill provides a structured approach to defining and tracking SLAs, SLIs, and SLOs, which are essential for ensuring service reliability. It automates the process of setting performance targets and monitoring actual performance, enabling proactive identification and resolution of potential issues.

How It Works

  1. SLI Definition: The skill guides the user to define Service Level Indicators (SLIs) such as availability, latency, error rate, and throughput.
  2. SLO Target Setting: The skill assists in setting Service Level Objectives (SLOs) by establishing target values for the defined SLIs (e.g., 99.9% availability).
  3. SLA Establishment: The skill helps in formalizing Service Level Agreements (SLAs), which are customer-facing commitments based on the defined SLOs.

When to Use This Skill

This skill activates when you need to:

  • Define SLAs, SLIs, and SLOs for a service.
  • Track service performance against defined objectives.
  • Calculate error budgets based on SLOs.

Examples

Example 1: Defining SLOs for a New Service

User request: "Create SLOs for our new payment processing service."

The skill will:

  1. Prompt the user to define SLIs (e.g., latency, error rate).
  2. Assist in setting target values for each SLI (e.g., p99 latency < 100ms, error rate < 0.01%).

Example 2: Tracking Availability

User request: "Track the availability SLI for the database service."

The skill will:

  1. Guide the user in setting up the tracking of the availability SLI.
  2. Visualize availability performance against the defined SLO.

Best Practices

  • Granularity: Define SLIs that are specific and measurable.
  • Realism: Set SLOs that are challenging but achievable.
  • Alignment: Ensure SLAs align with the defined SLOs and business requirements.

Integration

This skill can be integrated with monitoring tools to automatically collect SLI data and track performance against SLOs. It can also be used in conjunction with alerting systems to trigger notifications when SLO violations occur.

Prerequisites

  • SLI definitions stored in {baseDir}/slos/sli-definitions.yaml
  • Access to monitoring and metrics systems
  • Historical performance data for baseline
  • Business requirements for service reliability

Instructions

  1. Define Service Level Indicators (availability, latency, error rate, throughput)
  2. Set Service Level Objectives with target values (e.g., 99.9% availability)
  3. Formalize Service Level Agreements with customer commitments
  4. Configure automated SLI data collection
  5. Calculate error budgets based on SLOs
  6. Track performance and alert on SLO violations

Output

  • SLI/SLO/SLA definition documents
  • Real-time SLI metric dashboards
  • Error budget calculations and burn rate
  • SLO compliance reports
  • Alerting configurations for violations

Error Handling

If SLI/SLO tracking fails:

  • Verify SLI definition completeness
  • Check metric collection infrastructure
  • Validate data accuracy and granularity
  • Ensure alerting system connectivity
  • Review error budget calculation logic

Resources

  • Google SRE book on SLIs and SLOs
  • Error budget implementation guides
  • Service reliability engineering practices
  • SLO definition templates and examples

GitHub Repository

jeremylongshore/claude-code-plugins-plus
Path: plugins/performance/sla-sli-tracker/skills/sla-sli-tracker
aiautomationclaude-codedevopsmarketplacemcp

Related Skills

sglang

Meta

SGLang 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.

View skill

evaluating-llms-harness

Testing

This 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.

View skill

llamaguard

Other

LlamaGuard 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.

View skill

langchain

Meta

LangChain 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.

View skill