sre-engineer
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
Works with every agent the skills CLI supports.
npx skills add https://github.com/jeffallan/claude-skills --skill sre-engineer- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads sre-engineer automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
Security audits
Independent scans of the skill's instructions and bundled files.
SKILL.md
The instructions your agent reads when the skill is triggered.
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:
| 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:
- 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(namespace, deployment)
else:
print("Within SLO threshold — no action required")
Maintained by @jeffallan, Principal Consultant at Synergetic Solutions
FAQ
What does the sre-engineer skill do?
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.
How do I install sre-engineer?
Run npx skills add https://github.com/jeffallan/claude-skills --skill sre-engineer in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/sre-engineer/ (personal) or .claude/skills/sre-engineer/ (project).
Which AI agents can use sre-engineer?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is sre-engineer safe to install?
Security partners have reviewed it: Gen Agent Trust Hub flagged risks; Socket passed; Snyk passed; Runlayer flagged risks; ZeroLeaks passed. As with any skill, read the SKILL.md and bundled scripts before installing.
Who maintains sre-engineer?
It is published by jeffallan in jeffallan/claude-skills. It has 5,085 recorded installs.
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