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.
98
100%
Does it follow best practices?
Impact
96%
1.07xAverage score across 6 eval scenarios
Passed
No known issues
Evaluation results
100%
Define Reliability Targets for an E-Commerce Checkout API
SLO/SLI definition and error budget policy
Quantitative SLO targets
100%
100%
User impact justification
100%
100%
Error budget calculation
100%
100%
4xx counted as good events
100%
100%
SLO review checklist coverage
100%
100%
Tiered budget policy
100%
100%
Feature freeze on exhaustion
100%
100%
Velocity balance stated
100%
100%
Multiple SLO types
100%
100%
30-day measurement window
100%
100%
Prometheus/PromQL queries
100%
100%
Reliability impact explanation
100%
100%
96%
3%Set Up Production Monitoring for a User-Facing API Service
Golden signals monitoring and burn rate alerting
All four golden signals
100%
100%
Latency percentiles
100%
100%
Fast burn rate threshold
80%
100%
Medium burn rate threshold
100%
100%
Multi-window burn rate detection
100%
100%
Runbook links in alerts
50%
50%
Runbook content present
100%
100%
Prometheus recording rules
100%
100%
Alert severity labels
100%
100%
Page-worthy criteria respected
87%
100%
Reliability impact explanation
100%
100%
Saturation threshold defined
100%
100%
86%
10%Post-Incident Review and Operations Improvement Plan
Blameless postmortem and toil reduction planning
Blameless language
87%
100%
Postmortem impact section
75%
87%
UTC timeline present
100%
100%
Root cause section
100%
100%
Action items with owners
62%
100%
What went well / could improve
100%
100%
Toil measurement present
100%
100%
ROI-based prioritization
70%
100%
50% toil ceiling acknowledged
37%
37%
Automation plan produced
100%
100%
Runbook or remediation steps
40%
50%
Reliability impact explanation
60%
70%
98%
14%Game Day: Resilience Validation for Order Processing Service
Chaos experiment design and safety constraints
Hypothesis defined
100%
100%
Blast radius scoped
100%
100%
Rollback plan present
87%
100%
Success criteria defined
62%
100%
Error rate abort threshold
50%
100%
Latency abort threshold
50%
100%
RTO/RPO verification
100%
91%
Recovery validation end-to-end
100%
100%
Rollback always executed
75%
100%
Reliability impact explanation
100%
87%
Multiple experiment scenarios
100%
100%
100%
Pre-Launch Reliability Review: Consumer Analytics Platform
Capacity planning and graceful degradation
Capacity forecast produced
100%
100%
Forecast horizon specified
100%
100%
Scaling recommendation included
100%
100%
Deployment decision tied to error budget
100%
100%
Budget-based deployment gates
100%
100%
Graceful degradation design
100%
100%
Exception process defined
100%
100%
Threshold for scale-up action
100%
100%
Reliability impact explanation
100%
100%
Monitoring config produced
100%
100%
Automation script produced
100%
100%
96%
10%On-Call Readiness: Incident Response Playbook and Self-Healing Automation
Incident response roles and post-resolution workflow
SEV1-SEV4 classification
100%
100%
Incident commander role
100%
100%
Communication lead role
100%
100%
On-call engineer role
37%
100%
Post-resolution monitoring window
50%
100%
Postmortem scheduled within 48 hours
80%
100%
Retry logic in self-healing
100%
100%
Escalation on remediation failure
100%
100%
Severity-differentiated response
100%
100%
Reliability impact explanation
75%
50%
Runbook or remediation steps
100%
100%
- Repository
- jeffallan/claude-skills
- Commit
5b76101
- Evaluated
- Agent
- Claude Code
- Model
- Claude Sonnet 4.6
Table of Contents
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