observability-designer

これはレビュー時に保存された抜粋です。完全で最新の内容は外部ソースを確認してください。

---
name: "observability-designer"
description: "Design production-ready observability strategies combining metrics, logs, and traces. Includes SLI/SLO design, golden-signals monitoring, alert optimization. Use when adding observability to a new service, refactoring alerting that is too noisy, or designing an SLO program before scaling production load."
---

# Observability Designer (POWERFUL)

**Category:** Engineering  
**Tier:** POWERFUL  
**Description:** Design comprehensive observability strategies for production systems including SLI/SLO frameworks, alerting optimization, and dashboard generation.

## Overview

Observability Designer creates production-ready dashboards, alert configurations, and monitoring strategies across the three pillars (metrics, logs, traces).

**When NOT to use → slo-architect.** For SLO/SLI design with error-budget math, multi-window burn-rate alerting thresholds, and SLO review gates, route to `slo-architect` — it is the authoritative skill for that half. This skill's `slo_designer.py` produces a quick scaffold only. This skill's lane: dashboards (`dashboard_generator.py`) and alert-noise reduction (`alert_optimizer.py`).

## Quick Start

```bash
# Dashboard spec (Grafana JSON + docs) for a service
python3 scripts/dashboard_generator.py --service-type api --name payments --criticality critical --role sre --format grafana -o dashboard.json --doc-output dashboard.md

# Analyze an existing alert config for noise, duplicates, and coverage gaps
python3 scripts/alert_optimizer.py --input alerts.json --analyze-only --report alert_report.json
# ...then emit the optimized config once the report is reviewed:
python3 scripts/alert_optimizer.py --input alerts.json --output alerts_optimized.json

# Quick SLO scaffold (hand off to slo-architect for the real error-budget work)
python3 scripts/slo_designer.py --service-type api --criticality high --user-facing true --service-name payments -o slo_scaffold.json
```

**Verification loop:** after deploying optimized alerts, track the report's noise metrics for one on-call rotation — if the actionable-alert ratio didn't improve, re-run `--analyze-only` against the live config and iterate. Import the generated dashboard into Grafana and confirm every golden-signal panel renders with live data before closing the task.

## Core Competencies

### SLI/SLO/SLA Framework Design
- **Service Level Indicators (SLI):** Define measurable signals that indicate service health
- **Service Level Objectives (SLO):** Set reliability targets based on user experience
- **Service Level Agreements (SLA):** Establish customer-facing commitments with consequences
- **Error Budget Management:** Calculate and track error budget consumption
- **Burn Rate Alerting:** Multi-window burn rate alerts for proactive SLO protection

### Three Pillars of Observability

#### Metrics
- **Golden Signals:** Latency, traffic, errors, and saturation monitoring
- **RED Method:** Rate, Errors, and Duration for request-driven services
- **USE Method:** Utilization, Saturation, and Errors for resource monitoring
- **Business Metrics:** Revenue, user engagement, and feature adoption tracking
- **Infrastructure Metrics:** CPU, memory, disk, network, and custom resource metrics

#### Logs
- **Structured Logging:** JSON-based log formats with consistent fields
- **Log Aggregation:** Centralized log collection and indexing strategies
- **Log Levels:** Appropriate use of DEBUG, INFO, WARN, ERROR, FATAL levels
- **Correlation IDs:** Request tracing through distributed systems
- **Log Sampling:** Volume management for high-throughput systems

#### Traces
- **Distributed Tracing:** End-to-end request flow visualization
- **Span Design:** Meaningful span boundaries and metadata
- **Trace Sampling:** Intelligent sampling strategies for performance and cost
- **Service Maps:** Automatic dependency discovery through traces
- **Root Cause Analysis:** Trace-driven debugging workflows

### Dashboard Design Principles

#### In