# Observability > Comprehensive system understanding through metrics, logs, traces, and alerts for debugging and monitoring - Skill: `ffsshhttiikk/observability-2` (Agent Skill) - Install (CLI): `npx skillmds@latest add ffsshhttiikk/observability-2` - Raw SKILL.md: https://api.skillmd.com/api/skills/ffsshhttiikk/observability-2/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: ffsshhttiikk (https://skillmd.com/u/ffsshhttiikk) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/ffsshhttiikk/observability-2 --- # Observability ## What I Do I provide comprehensive visibility into complex systems by collecting, correlating, and analyzing metrics, logs, and traces. I enable teams to understand system behavior, diagnose issues, and maintain confidence in their infrastructure. ## When to Use Me - Debugging distributed systems - Understanding system behavior in production - Proactive monitoring and alerting - Performance optimization - Incident investigation and resolution - Capacity planning - Service level management ## Core Concepts - **Metrics**: Quantitative measurements over time - **Logs**: Discrete event records - **Traces**: End-to-end request tracking - **Dashboards**: Visual aggregations of data - **Alerts**: Notifications when conditions are met - **MELT**: Metrics, Events, Logs, Traces - **Service Level Indicators (SLIs)**: Reliability measurements - **Service Level Objectives (SLOs)**: Target reliability levels - **OpenTelemetry**: Standard for telemetry data - **Distributed Tracing**: Tracking across service boundaries ## Code Examples **OpenTelemetry Configuration (YAML):** ```yaml receivers: otlp: protocols: grpc: endpoint: 0.0.0.0:4317 http: endpoint: 0.0.0.0:4318 prometheus: config: scrape_configs: - job_name: 'otel-collector' scrape_interval: 10s static_configs: - targets: ['localhost:8888'] processors: batch: timeout: 1s send_batch_size: 1024 memory_limiter: check_interval: 1s limit_mib: 1000 spike_limit_mib: 200 resource: attributes: - key: service.name value: my-service action: upsert - key: deployment.environment value: production action: upsert exporters: prometheusremotewrite: endpoint: https://prometheus.example.com/api/v1/write tls: insecure: false jaeger: endpoint: jaeger-collector:14250 tls: insecure: true loki: endpoint: https://loki.example.com/loki/api/v1/push tls: insecure: false service: pipelines: traces: receivers: [otlp] processors: [memory_limiter, batch, resource] exporters: [jaeger] metrics: receivers: [prometheus, otlp] processors: [memory_limiter, batch] exporters: [prometheusremotewrite] logs: receivers: [otlp] processors: [resource] exporters: [loki] ``` **Prometheus Metrics (Python):** ```python from prometheus_client import Counter, Histogram, Gauge, Summary from prometheus_client import start_http_server, REGISTRY import time import random # Custom metrics REQUEST_COUNT = Counter( 'http_requests_total', 'Total HTTP requests', ['method', 'endpoint', 'status'] ) REQUEST_LATENCY = Histogram( 'http_request_duration_seconds', 'HTTP request latency', ['method', 'endpoint'], buckets=[0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1.0, 2.5, 5.0, 10.0] ) ACTIVE_USERS = Gauge( 'active_users', 'Number of currently active users', ['region'] ) QUEUE_SIZE = Gauge( 'job_queue_size', 'Size of job processing queue', ['worker_group'] ) BUSINESS_METRICS = Summary( 'orders_processed', 'Number of orders processed', ['product_type'] ) class MetricsMiddleware: def __init__(self, app): self.app = app def __call__(self, environ, start_response): start_time = time.time() def start_response_wrapper(status, headers): response = self.app(environ, start_response) duration = time.time() - start_time method = environ.get('REQUEST_METHOD', 'UNKNOWN') path = environ.get('PATH_INFO', '/') status_code = int(status.split()[0]) REQUEST_COUNT.labels( method=method, endpoint=path, status=status_code ).inc() REQUEST_LATENCY.labels( method=method, endpoint=path ).observe(duration) return response return self.app(environ, start_response_wrapper) # Start metrics server start_http_server(8000) ``` **Structured Logging (JSON):** ```json { "level": "INFO", "timestamp": "2024-01-15T10:30:00.123Z", "service": "payment-api", "version": "2.3.1", "environment": "production", "trace_id": "00-1234567890abcdef-1234567890abcdef-01", "span_id": "1234567890abcdef", "event": "payment_processed", "user_id": "usr_12345", "order_id": "ord_67890", "amount": 99.99, "currency": "USD", "payment_method": "credit_card", "duration_ms": 245, "status": "success", "metadata": { "processor": "stripe", "processor_transaction_id": "tx_abc123", "retry_count": 0 }, "message": "Payment processed successfully for order ord_67890" } ``` **Grafana Dashboard (JSON):** ```json { "dashboard": { "title": "Service Health Overview", "tags": ["production", "health"], "timezone": "browser", "refresh": "30s", "panels": [ { "id": 1, "title": "Request Rate (RPM)", "type": "graph", "gridPos": {"h": 8, "w": 12, "x": 0, "y": 0}, "targets": [ { "expr": "sum(rate(http_requests_total{service=~\"$service\"}[5m])) by (endpoint)", "legendFormat": "{{ endpoint }}" } ], "alert": { "name": "HighRequestRate", "conditions": [ { "evaluator": { "params": [10000], "type": "gt" } } ], "executionErrorState": "alerting", "frequency": "1m" } }, { "id": 2, "title": "Error Rate (%)", "type": "gauge", "gridPos": {"h": 8, "w": 6, "x": 12, "y": 0}, "targets": [ { "expr": "sum(rate(http_requests_total{status=~\"5..\",service=~\"$service\"}[5m])) / sum(rate(http_requests_total{service=~\"$service\"}[5m])) * 100" } ], "fieldConfig": { "defaults": { "max": 5, "thresholds": [ {"color": "green", "value": null}, {"color": "yellow", "value": 1}, {"color": "red", "value": 3} ] } } }, { "id": 3, "title": "P95 Latency (ms)", "type": "timeseries", "gridPos": {"h": 8, "w": 12, "x": 0, "y": 8}, "targets": [ { "expr": "histogram_quantile(0.95, sum(rate(http_request_duration_seconds_bucket{service=~\"$service\"}[5m])) by (le)) * 1000", "legendFormat": "P95" }, { "expr": "histogram_quantile(0.99, sum(rate(http_request_duration_seconds_bucket{service=~\"$service\"}[5m])) by (le)) * 1000", "legendFormat": "P99" } ] }, { "id": 4, "title": "Active Traces", "type": "stat", "gridPos": {"h": 4, "w": 6, "x": 12, "y": 8}, "targets": [ { "expr": "sum(traces_spanmetrics_calls_total{service=~\"$service\"})" } ] } ], "templating": { "list": [ { "name": "service", "type": "query", "datasource": "Prometheus", "query": "label_values(http_requests_total, service)", "refresh": 2 } ] } } } ``` ## Best Practices 1. **Implement structured logging** - JSON format with consistent fields 2. **Use OpenTelemetry** - Vendor-neutral instrumentation 3. **Correlate metrics, logs, traces** - Understand the full picture 4. **Define SLOs and alert on them** - Measure user-impacting issues 5. **Create golden signals dashboards** - Latency, traffic, errors, saturation 6. **Retain data strategically** - Hot, warm, cold storage tiers 7. **Use sampling for traces** - Manage volume while maintaining visibility 8. **Label everything consistently** - Service, environment, region, team 9. **Test your alerts** - Don't discover issues during incidents 10. **Automate remediation** - Self-healing for known failure modes