모니터링 & Alerting 디자이너
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스킬 프롬프트
이 스킬은 findskill.ai에서 복사할 때 가장 잘 작동합니다 — 다른 곳에서는 변수와 포맷이 제대로 전송되지 않을 수 있습니다.
이 스킬 사용법
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스킬 복사 위의 버튼 사용
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AI 어시스턴트에 붙여넣기 (Claude, ChatGPT 등)
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아래에 정보 입력 (선택사항) 프롬프트에 포함할 내용 복사
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전송하고 대화 시작 AI와 함께
추천 맞춤 설정
| 설명 | 기본값 | 내 값 |
|---|---|---|
| Target SLO percentage (e.g., 99.95 for 99.95% availability) | 99.95 | |
| Time window for SLO evaluation (e.g., 30d, 7d, 1h) | 30d | |
| Burn rate multiplier for critical/page alerts | 14.4 | |
| Burn rate multiplier for warning/ticket alerts | 1.0 | |
| Target monitoring platform (prometheus, datadog, dynatrace, grafana) | prometheus | |
| Distributed tracing backend (jaeger, zipkin, tempo, datadog) | jaeger |
Design comprehensive observability systems that provide real-time visibility into system health, performance, and reliability. Create SLO-based alerting strategies with multi-burn-rate rules, reduce alert fatigue through intelligent optimization, and integrate monitoring with incident response workflows for faster resolution.
연구 출처
이 스킬은 다음 신뢰할 수 있는 출처의 연구를 바탕으로 만들어졌습니다:
- From Monitoring to Observability: A Paradigm Shift in IT Operations Comprehensive guide on the shift from traditional monitoring to observability covering logs, metrics, and traces
- Ways to Alert on Significant Events (Google SRE Workbook) Official Google approach to multi-burn-rate and multi-window SLO-based alerting strategies
- Designing Tomorrow's Observability: Software Architect's Guide Deep dive into observability architecture, tool selection, and implementation patterns
- Monitoring Distributed Cloud-Based Microservices Framework for monitoring cloud microservices covering APM, infrastructure health, and log aggregation
- Intelligent Alerting with AI-Powered Anomaly Detection Modern ML approaches to noise reduction including predictive alerting and Holt-Winters forecasting
- SLO Monitoring Guide - Measuring Service Reliability Practical guide on SLO setup, SLI definition, and actionable threshold configuration
- How We Use Sloth for SLO Monitoring with Prometheus Real-world implementation of multi-window, multi-burn-rate alerting at Mattermost
- Observability Best Practices - Embrace.io Best practices including actionable alerts, cross-department collaboration, and data quality