sre-engineer skill

by Jeffallan·Jeffallan/claude-skills·12k stars·MIT

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.

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Install the sre-engineer skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description.

git clone --depth 1 https://github.com/Jeffallan/claude-skills.git /tmp/claude-skills
mkdir -p ~/.claude/skills
cp -r /tmp/claude-skills/skills/sre-engineer ~/.claude/skills/sre-engineer

available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

SRE Engineer

Core Workflow

Assess reliability - Review architecture, SLOs, incidents, toil levels
Define SLOs - Identify meaningful SLIs and set appropriate targets
Verify alignment - Confirm SLO targets reflect user expectations before proceeding
Implement monitoring - Build golden signal dashboards and alerting
Automate toil - Identify repetitive tasks and build automation
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:

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:

SLO definitions with SLI measurements and targets
Monitoring/alerting configuration (Prometheus, etc.)
Automation scripts (Python, Go, Terraform)
Runbooks with clear remediation steps
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__ == "__main__":
    service, namespace, deployment = sys.argv[1], sys.argv[2], sys.argv[3]
    rate = get_error_rate(service)
    print(f"Error rate for {service}: {rate:.2%}")
    if rate > ERROR_THRESHOLD:
        restart_deployment(nam

Documentation

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