Mode d’emploi
- Copiez la commande et examinez le paquet avant de l’installer dans votre environnement agent.
- Consultez le document original pour les outils, identifiants et environnements requis.
- Décrivez les entrées et le livrable souhaité ; comparez la sortie aux sources.
npx skills add https://github.com/jeffallan/claude-skills --skill sre-engineerAvant de commencer
- L’installation n’inclut ni abonnement au modèle, ni quota API, ni accès aux fichiers.
- Étoiles et installations reflètent l’activité, pas un test indépendant de qualité ou de sécurité.
Document Skill original
Rendu depuis le dépôt source en conservant titres, exemples, code, tableaux, liens et images.
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 releasesPrometheus SLO Alerting Rule (Multiwindow Burn Rate)
yaml
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
promql
# 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)
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
