sre

Generates SLOs, chaos experiments, incident runbooks, and capacity plans for production systems.

176|49|Updated Mar 4, 2026
One-click install
npx skills add https://github.com/nagisanzenin/production-grade --skill sre-nagisanzenin
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sre
Source: https://github.com/nagisanzenin/production-grade/tree/main/skills/sre
Command: npx skills add https://github.com/nagisanzenin/production-grade --skill sre-nagisanzenin

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Teams shipping to production often lack defined reliability targets, actionable alerting, tested disaster recovery, and runbooks an on-call engineer can actually follow at 3 AM. This Skill turns existing infrastructure and monitoring configs into a complete site reliability engineering practice. ## Core Features & Use Cases - Production Readiness Review: Audits Kubernetes manifests, Terraform configs, and application settings against a checklist covering health probes, graceful shutdown, timeouts, retries, and resource limits, producing findings with severity and remediation steps. - SLO & Error Budget Definition: Generates SLI definitions, SLO targets, error budget policies, and multi-window burn-rate alert rules in Prometheus format. - Chaos Engineering & Game Days: Authors Chaos Mesh scenarios (pod failure, network partition, dependency failure, resource pressure) with steady-state hypotheses and a game-day playbook with abort criteria. - Incident Management & Runbooks: Produces severity classifications, on-call rotations, escalation policies, communication templates, war-room procedures, and per-service runbooks with exact commands and decision trees. - Capacity Planning: Builds load models at 1x/10x/100x scale, validated HPA configurations, cost projections, and bottleneck analysis. - Use Case: After deploying a Kubernetes-based API, run this Skill to get SLO dashboards, burn-rate alerts, four chaos scenarios, and runbooks for high error rate, latency, OOM, and dependency failures. ## Quick Start Ask the agent to run the SRE skill against your infrastructure directory to produce a production readiness review, SLO definitions, and on-call runbooks.

Frequently Asked Questions about sre

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I define SLOs and error budgets for a Kubernetes service?▼

The Skill generates SLI definitions in YAML covering availability, latency, and correctness, then sets SLO targets with an error budget policy defining enforcement actions. It also produces multi-window burn-rate alert rules following the Google SRE workbook method.

How to write on-call runbooks that engineers can actually follow?▼

Runbooks are generated per service with exact kubectl and Prometheus commands, real pod labels and metric names, decision trees for triage, rollback procedures, and escalation paths. Generic advice like "check the logs" is explicitly rejected.

What chaos engineering scenarios should I run before production launch?▼

The Skill creates at least four Chaos Mesh scenarios: pod failure, network partition, dependency failure, and resource pressure. Each includes a measurable steady-state hypothesis, expected behavior, and a game-day playbook with explicit abort criteria.

Does this work with existing monitoring like Prometheus and Grafana?▼

Yes, it reads existing monitoring configs as critical input and extends them rather than replacing them. Outputs include Prometheus burn-rate alert rules and a Grafana dashboard JSON for SLO status and error budget tracking.

What is the difference between DevOps and SRE responsibilities here?▼

DevOps owns infrastructure provisioning, CI/CD, and base monitoring installation, while this Skill owns SLO definitions, error budgets, service-level alerting, incident processes, and capacity planning. DevOps implements the thresholds that SRE defines.

When should I not set 99.99% availability targets?▼

Setting 99.99% SLOs for every service leaves near-zero error budget and blocks all deployments. The Skill recommends targets based on user-observable impact, starting around 99.5% and tightening over time, with lower targets for internal services.