sre-engineer

name	sre-engineer
description	Defines service level objectives, creates error budget policies, designs incident response procedures, develops capacity models, and produces monitoring configurations and automation scripts for production systems. Use when defining SLIs/SLOs, managing error budgets, building reliable systems at scale, incident management, chaos engineering, toil reduction, or capacity planning.
license	MIT
metadata	{"author":"https://github.com/Jeffallan","version":"1.1.0","domain":"devops","triggers":"SRE, site reliability, SLO, SLI, error budget, incident management, chaos engineering, toil reduction, on-call, MTTR","role":"specialist","scope":"implementation","output-format":"code","related-skills":"devops-engineer, cloud-architect, kubernetes-specialist"}

SRE Engineer

Core Workflow

1. Assess reliability - Review architecture, SLOs, incidents, toil levels
2. Define SLOs - Identify meaningful SLIs and set appropriate targets
3. Verify alignment - Confirm SLO targets reflect user expectations before proceeding
4. Implement monitoring - Build golden signal dashboards and alerting
5. Automate toil - Identify repetitive tasks and build automation
6. Test resilience - Design and execute chaos experiments; verify recovery meets RTO/RPO targets before marking the experiment complete; validate recovery behavior end-to-end

Reference Guide

Load detailed guidance based on context:

Topic	Reference	Load When
SLO/SLI	references/slo-sli-management.md	Defining SLOs, calculating error budgets
Error Budgets	references/error-budget-policy.md	Managing budgets, burn rates, policies
Monitoring	references/monitoring-alerting.md	Golden signals, alert design, dashboards
Automation	references/automation-toil.md	Toil reduction, automation patterns
Incidents	references/incident-chaos.md	Incident response, chaos engineering

Constraints

MUST DO

• Define quantitative SLOs (e.g., 99.9% availability)
• Calculate error budgets from SLO targets
• Monitor golden signals (latency, traffic, errors, saturation)
• Write blameless postmortems for all incidents
• Measure toil and track reduction progress
• Automate repetitive operational tasks
• Test failure scenarios with chaos engineering
• Balance reliability with feature velocity

MUST NOT DO

• Set SLOs without user impact justification
• Alert on symptoms without actionable runbooks
• Tolerate >50% toil without automation plan
• Skip postmortems or assign blame
• Implement manual processes for recurring tasks
• Deploy without capacity planning
• Ignore error budget exhaustion
• Build systems that can't degrade gracefully

Output Templates

When implementing SRE practices, provide:

1. SLO definitions with SLI measurements and targets
2. Monitoring/alerting configuration (Prometheus, etc.)
3. Automation scripts (Python, Go, Terraform)
4. Runbooks with clear remediation steps
5. Brief explanation of reliability impact

Concrete Examples

SLO Definition & Error Budget Calculation

# 99.9% availability SLO over a 30-day window
# Allowed downtime: (1 - 0.999) * 30 * 24 * 60 = 43.2 minutes/month
# Error budget (request-based): 0.001 * total_requests

# Example: 10M requests/month → 10,000 error budget requests
# If 5,000 errors consumed in week 1 → 50% budget burned in 25% of window
# → Trigger error budget policy: freeze non-critical releases

Prometheus SLO Alerting Rule (Multiwindow Burn Rate)

groups:
  - name: slo_availability
    rules:
      # Fast burn: 2% budget in 1h (14.4x burn rate)
      - alert: HighErrorBudgetBurn
        expr: |
          (
            sum(rate(http_requests_total{status=~"5.."}[1h]))
            /
            sum(rate(http_requests_total[1h]))
          ) > 0.014400
          and
          (
            sum(rate(http_requests_total{status=~"5.."}[5m]))
            /
            sum(rate(http_requests_total[5m]))
          ) > 0.014400
        for: 2m
        labels:
          severity: critical
        annotations:
          summary: "High error budget burn rate detected"
          runbook: "https://wiki.internal/runbooks/high-error-burn"

      # Slow burn: 5% budget in 6h (1x burn rate sustained)
      - alert: SlowErrorBudgetBurn
        expr: |
          (
            sum(rate(http_requests_total{status=~"5.."}[6h]))
            /
            sum(rate(http_requests_total[6h]))
          ) > 0.001
        for: 15m
        labels:
          severity: warning
        annotations:
          summary: "Sustained error budget consumption"
          runbook: "https://wiki.internal/runbooks/slow-error-burn"

PromQL Golden Signal Queries

# Latency — 99th percentile request duration
histogram_quantile(0.99, sum(rate(http_request_duration_seconds_bucket[5m])) by (le, service))

# Traffic — requests per second by service
sum(rate(http_requests_total[5m])) by (service)

# Errors — error rate ratio
sum(rate(http_requests_total{status=~"5.."}[5m])) by (service)
  /
sum(rate(http_requests_total[5m])) by (service)

# Saturation — CPU throttling ratio
sum(rate(container_cpu_cfs_throttled_seconds_total[5m])) by (pod)
  /
sum(rate(container_cpu_cfs_periods_total[5m])) by (pod)

Toil Automation Script (Python)

#!/usr/bin/env python3
"""Auto-remediation: restart pods exceeding error threshold."""
import subprocess, sys, json

ERROR_THRESHOLD = 0.05  # 5% error rate triggers restart

def get_error_rate(service: str) -> float:
    """Query Prometheus for current error rate."""
    import urllib.request
    query = f'sum(rate(http_requests_total{{status=~"5..",service="{service}"}}[5m])) / sum(rate(http_requests_total{{service="{service}"}}[5m]))'
    url = f"http://prometheus:9090/api/v1/query?query={urllib.request.quote(query)}"
    with urllib.request.urlopen(url) as resp:
        data = json.load(resp)
    results = data["data"]["result"]
    return float(results[0]["value"][1]) if results else 0.0

def restart_deployment(namespace: str, deployment: str) -> None:
    subprocess.run(
        ["kubectl", "rollout", "restart", f"deployment/{deployment}", "-n", namespace],
        check=True
    )
    print(f"Restarted {namespace}/{deployment}")

if __name__ == :
    service, namespace, deployment = sys.argv[], sys.argv[], sys.argv[]
    rate = get_error_rate(service)
    ()
     rate > ERROR_THRESHOLD:
        restart_deployment(namespace, deployment)
    :
        ()

Documentation