Claude Skills For Site Reliability (2026)

Site reliability engineers need tools that handle incident response, log analysis, monitoring, and system debugging. Claude Code provides skills that integrate with common SRE tooling to accelerate these workflows. This guide covers practical applications for SRE teams.

Incident Response Automation

When a production incident occurs, speed matters Claude Code helps you build incident response runbooks that execute directly in your terminal. Describe your alerting setup and Claude generates bash scripts for common remediation steps.

For example, a high-memory alert response might look like this:

#!/bin/bash
Incident response: high memory remediation
HOST=$1
THRESHOLD=90
Check current memory usage
MEM_USAGE=$(ssh $HOST "free | grep Mem | awk '{printf \"%.0f\", \$3/\$2 * 100}'")
if [ "$MEM_USAGE" -gt "$THRESHOLD" ]; then
 # Find top memory consumers
 ssh $HOST "ps aux --sort=-%mem | head -10"
 
 # Restart largest consumer if it's a known service
 ssh $HOST "systemctl restart $(ssh $HOST "ps aux --sort=-%mem | head -1 | awk '{print \$11}'" | xargs basename)"
 
 # Log the action
 echo "$(date): Restarted process on $HOST due to memory pressure" >> /var/log/incident.log
fi

Claude can also help you structure incident post-mortems. Paste your incident timeline and ask Claude to format it using the standard industry format: summary, impact, root cause, trigger, resolution, and action items.

Log Analysis and Pattern Detection

SREs spend significant time grepping through logs. Claude Code enhances this workflow by helping you construct precise log queries and recognize patterns across multiple log sources.

When analyzing application logs, describe the error patterns you’re seeing:

I'm seeing timeout errors across three services. Help me construct a grep command that finds all timeout exceptions in /var/log/app/ from the last hour, grouped by service name.

Claude generates commands like:

Find timeout errors across services
for log in /var/log/app/*.log; do
 service=$(basename $log .log)
 timeout_count=$(grep -c "timeout" "$log" 2>/dev/null)
 if [ "$timeout_count" -gt 0 ]; then
 echo "$service: $timeout_count timeouts"
 fi
done

For structured logs in JSON format, Claude helps you use jq effectively:

Extract error rates from JSON logs
cat /var/log/app.json | jq -c 'select(.level=="error") | {timestamp, service, message}' | \
 jq -s 'group_by(.service) | map({service: .[0].service, count: length})'

Monitoring Dashboard Construction

Building Prometheus alerts or Grafana dashboards becomes faster with Claude’s assistance. Describe your metrics and desired visualization, and Claude generates the configuration.

For Prometheus alerting rules:

groups:
 - name: service-health
 rules:
 - alert: HighErrorRate
 expr: sum(rate(http_requests_total{status=~"5.."}[5m])) / sum(rate(http_requests_total[5m])) > 0.05
 for: 2m
 labels:
 severity: critical
 annotations:
 summary: "High error rate on {{ $labels.service }}"
 description: "{{ $labels.service }} error rate is {{ $value | humanizePercentage }}"
 
 - alert: HighLatency
 expr: histogram_quantile(0.95, sum(rate(http_request_duration_seconds_bucket[5m])) by (le, service)) > 2
 for: 5m
 labels:
 severity: warning
 annotations:
 summary: "High p95 latency on {{ $labels.service }}"

Claude also helps you write Grafana panel JSON by describing your visualization needs. Specify the metric, aggregation, and visual style, and receive ready-to-paste dashboard configurations.

On-Call Workflow Enhancement

Managing on-call rotations and escalations requires clear runbooks and automation. Claude helps you build scripts that integrate with PagerDuty, OpsGenie, or similar tools.

A basic escalation script might look like:

#!/bin/bash
Automated escalation check
INCIDENT_ID=$1
CURRENT_ESCALATION=$2
Get incident details via PagerDuty API
INCIDENT=$(curl -s -H "Authorization: Token token=$PAGERDUTY_API_KEY" \
 "https://api.pagerduty.com/incidents/$INCIDENT_ID")
Check if incident is acknowledged
STATUS=$(echo "$INCIDENT" | jq -r '.incident.status')
if [ "$STATUS" == "triggered" ]; then
 # Calculate time since trigger
 CREATED_AT=$(echo "$INCIDENT" | jq -r '.incident.created_at')
 NOW=$(date -u +%Y-%m-%dT%H:%M:%SZ)
 SECONDS_SINCE=$(( $(date -d "$NOW" +%s) - $(date -d "$CREATED_AT" +%s) ))
 
 # Escalate if unacknowledged for more than 15 minutes
 if [ "$SECONDS_SINCE" -gt 900 ]; then
 curl -s -X PUT -H "Authorization: Token token=$PAGERDUTY_API_KEY" \
 -H "Content-Type: application/json" \
 -d "{\"incident\": {\"type\": \"incident_reference\", \"escalation_policy\": \"$NEXT_ESCALATION_POLICY\"}}" \
 "https://api.pagerduty.com/incidents/$INCIDENT_ID"
 echo "Incident escalated after $(($SECONDS_SINCE / 60)) minutes"
 fi
fi

Chaos Engineering and Testing

SRE teams increasingly practice chaos engineering. Claude helps you write chaos scripts that safely inject failures to test system resilience.

#!/usr/bin/env python3
Simple chaos monkey: randomly terminate containers
import subprocess
import random
import time
def terminate_random_container():
 # List running containers
 result = subprocess.run(
 ["docker", "ps", "--format", "{{.Names}}"],
 capture_output=True, text=True
 )
 containers = result.stdout.strip().split('\n')
 
 if containers and containers[0]:
 target = random.choice(containers)
 print(f"Terminating {target} for chaos testing")
 subprocess.run(["docker", "kill", "--signal", "SIGTERM", target])
Run every 30 minutes during business hours
while True:
 hour = int(time.strftime("%H"))
 if 9 <= hour <= 17: # Business hours only
 if random.random() < 0.1: # 10% chance each interval
 terminate_random_container()
 time.sleep(1800)

Capacity Planning and Resource Analysis

Claude assists with analyzing resource usage data and generating capacity reports. Feed it your Prometheus metrics and ask for projections:

Our database CPU averages 70% with 5000 connections. Generate a capacity projection for 2x traffic growth assuming linear scaling.

Claude helps you build the analysis queries:

Get CPU usage percentiles via Prometheus HTTP API
curl -g 'http://localhost:9090/api/v1/query?query=histogram_quantile(0.50,rate(node_cpu_seconds_total{mode="idle"}[5m]))by(instance)'
curl -g 'http://localhost:9090/api/v1/query?query=histogram_quantile(0.95,rate(node_cpu_seconds_total{mode="idle"}[5m]))by(instance)'
curl -g 'http://localhost:9090/api/v1/query?query=histogram_quantile(0.99,rate(node_cpu_seconds_total{mode="idle"}[5m]))by(instance)'

Key Takeaways

Claude Code skills accelerate SRE workflows across multiple domains: incident response automation, log analysis, monitoring configuration, on-call management, chaos engineering, and capacity planning. The key is describing your infrastructure and goals clearly, then iterating on the generated code.

Start by integrating Claude into your most frequent SRE tasks. Build reusable scripts for common incidents, standardize your log queries, and create templates for monitoring dashboards. Over time, these scripts become institutional knowledge that your entire team can share and build on.

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Related Reading

Step-by-Step: Using Claude Skills for On-Call Workflows

  1. Connect your observability stack: integrate Claude with Datadog, Prometheus, or Grafana. Claude can then query metrics directly during an incident.
  2. Create a runbook skill: convert existing runbooks into a Claude skill. When an alert fires, Claude walks through the runbook steps against live metrics.
  3. Set up log analysis: connect Claude to Splunk, Loki, or CloudWatch Logs. Ask it to surface the top 5 error patterns from the last 15 minutes.
  4. Automate postmortem drafts: ask Claude to draft the postmortem from the incident timeline. It populates timeline, impact, root cause, and action items.
  5. Build a change risk scorer: give Claude access to deployment history to score the risk of a proposed change based on time of day, recent incident rate, and affected components.

Error Budget Tracking

Claude can perform SLO and error budget calculations on demand. A 99.9% monthly SLO allows 43.8 minutes of downtime per 30-day month. Ask Claude to calculate the current error budget burn rate given your uptime data and recommend whether a deployment freeze is warranted.

Run this as a daily scheduled skill that posts the error budget status to your team Slack channel.

SRE Task Automation Comparison

Task Manual time With Claude Skills Time saved
Incident triage 20-30 min 5-8 min ~75%
Postmortem draft 2-3 hours 20-30 min ~85%
Capacity planning report 4-6 hours 30-60 min ~87%
Runbook review 1-2 hours 15-20 min ~80%
On-call handoff notes 20-30 min 5 min ~83%

Advanced: Automated Alert Explanations

Connect Claude to your alerting system so it generates a plain-language explanation whenever a P1 or P2 alert fires. Pass the alert description, recent metrics summary, and error log samples. Ask Claude to explain the likely cause, customer impact, and first two diagnostic steps.

async def explain_alert(description, metrics_summary, error_samples):
 prompt = (
 "Alert: " + description + "\n\n"
 "Recent metrics: " + metrics_summary + "\n\n"
 "Error samples: " + error_samples + "\n\n"
 "Explain the likely cause, customer impact, and first two diagnostic steps."
 )
 return await claude.complete(prompt)

Post the explanation to the incident Slack channel within seconds of the alert firing.

Troubleshooting

Inconsistent incident summaries: Instruct Claude to respond with JSON containing summary, impact, likely_cause, and next_steps fields. Parse this JSON rather than treating the response as free text.

Metrics context too large: Downsample time series before passing to Claude. Send 48 thirty-minute averages instead of 1,440 one-minute data points.

Postmortem missing action items: Include the explicit moment each mitigation was applied and whether metrics improved. Claude can then infer what worked and what systemic changes would prevent recurrence.

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