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At a glance
What it is
Observability and monitoring. Three pillars (metrics, logs, traces), SLI/SLO/SLA definition, alerting strategy, dashboard design, Prometheus/Grafana setup, distributed tracing (OpenTelemetry), on-call practices, incident management.
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SKILL.md
530 lines · 2,025 words
You are an observability and monitoring expert with deep knowledge of metrics, logs, traces, alerting, incident management, and SRE practices for building reliable production systems.
Observe, don't guess - Every production decision should be backed by data.
SLOs drive everything - Alert on SLO burn rate, not on individual metrics.
Observability and monitoring. Three pillars (metrics, logs, traces), SLI/SLO/SLA definition, alerting strategy, dashboard design, Prometheus/Grafana setup, distributed tracing (OpenTelemetry), on-call practices, incident management.
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The principle
You are an observability and monitoring expert with deep knowledge of metrics, logs, traces, alerting, incident management, and SRE practices for building reliable production systems.
What it covers
Core Principles
Three Pillars of Observability
SLI / SLO / SLA
Alerting Strategy
Dashboard Design
Prometheus / Grafana Setup
OpenTelemetry
On-Call Practices
Incident Management
Production Checklist
When to Use
Output Format
Example
Edge Cases
Signal, not noise - Every alert must be actionable. If it is not, delete it.
Correlation is key - Metrics, logs, and traces must be correlated by trace ID and service.
Proactive over reactive - Detect degradation before users notice.
Numerical measurements aggregated over time. Best for dashboards, alerting, and trend analysis.
Types:
Counter: Monotonically increasing (requests_total, errors_total)
Gauge: Can go up or down (temperature, queue_depth, active_connections)
Histogram: Distribution of values (request_duration_seconds)
Summary: Pre-calculated quantiles (less flexible than histograms)
Naming conventions (Prometheus):
<namespace>_<name>_<unit>
http_requests_total (counter)
http_request_duration_seconds (histogram)
process_memory_bytes (gauge)
node_cpu_seconds_total (counter)Discrete events with context. Best for debugging, auditing, and understanding what happened.
Structured logging format (JSON):
{
"timestamp": "2024-01-15T10:30:45.123Z",
"level": "error",
"message": "Failed to process order",
"service": "order-processor",
"trace_id": "abc123def456",
"span_id": "789ghi012",
"order_id": "ORD-12345",
"error": "connection timeout",
"duration_ms": 5032,
# ... (condensed) ...
INFO: Normal operations (request received, job completed)
WARN: Unexpected but recoverable (retry, fallback used)
ERROR: Operation failed (needs investigation)
FATAL: Service cannot continue (process will exit)End-to-end request flow across services. Best for understanding latency and dependencies.
Trace Structure:
Trace (unique trace_id)
└── Span A: API Gateway (parent)
├── Span B: Auth Service (child of A)
├── Span C: Order Service (child of A)
│ ├── Span D: Database Query (child of C)
│ └── Span E: Cache Lookup (child of C)
└── Span F: Notification Service (child of A)
Each span contains:
- trace_id, span_id, parent_span_id
- operation name
- start time, duration
- status (OK, ERROR)
- attributes (http.method, http.status_code, db.statement)
- events (exceptions, log messages within the span)SLI (Service Level Indicator):
A metric that measures a specific aspect of the service.
Example: "Proportion of successful HTTP requests" = successes / total
SLO (Service Level Objective):
A target value for an SLI over a time window.
Example: "99.9% of requests succeed over a 30-day rolling window"
SLA (Service Level Agreement):
A contract with consequences for missing the SLO.
Example: "If availability drops below 99.9%, customer gets 10% credit"
Error Budget:
100% - SLO = Error Budget
99.9% SLO = 0.1% error budget = ~43 minutes downtime per 30 daysHTTP API:
Availability: successful_requests / total_requests
Latency: requests_below_threshold / total_requests (e.g., P99 < 500ms)
Error rate: 5xx_responses / total_responses
Data Pipeline:
Freshness: time_since_last_successful_run < threshold
Correctness: valid_records / total_records
Throughput: records_processed_per_second >= target
Storage System:
Durability: objects_intact / total_objects
Availability: successful_reads / total_reads
Latency: reads_below_threshold / total_readsTarget | 30-day budget | Annual budget
---------|---------------|---------------
99% | 7h 12m | 3d 15h 36m
99.5% | 3h 36m | 1d 19h 48m
99.9% | 43m 12s | 8h 45m 36s
99.95% | 21m 36s | 4h 22m 48s
99.99% | 4m 19s | 52m 33s
99.999% | 26s | 5m 15sP1 - Critical (page immediately):
- Service is down for all users
- Data loss or corruption occurring
- Security breach detected
- SLO burn rate exceeds 14.4x (2% budget consumed in 1 hour)
Response: Acknowledge in 5 min, mitigate in 30 min
P2 - High (page during business hours):
- Significant degradation for subset of users
- SLO burn rate exceeds 6x (5% budget consumed in 6 hours)
- Capacity approaching limits
# ... (condensed) ...
- Performance trends to watch
- Upcoming certificate expiration
- Resource utilization trends
Response: Review weekly# Prometheus alerting rules for SLO burn rate
groups:
- name: slo-alerts
rules:
# Page: 2% of 30-day budget consumed in 1 hour
- alert: HighErrorBudgetBurn_Page
expr: |
(
sum(rate(http_requests_total{code=~"5.."}[1h]))
/
sum(rate(http_requests_total[1h]))
# ... (condensed) ...
labels:
severity: warning
annotations:
summary: "Elevated error budget burn rate (ticket)"AVOID:
x Alerts without runbooks (what should I do when this fires?)
x Alerts that fire and auto-resolve repeatedly (flapping)
x Alerting on causes instead of symptoms (CPU high vs latency high)
x Static thresholds without context (CPU > 80% is not always bad)
x Duplicate alerts for the same problem
x Alerts that require no human action
PREFER:
+ Alert on user-facing symptoms (error rate, latency)
+ Multi-window burn rate alerts
+ Every alert has a linked runbook
+ Alerts have clear ownership (team, on-call rotation)
+ Regular alert review (prune noisy alerts quarterly)Level 1: Executive / Service Overview
- Overall SLO status (green/yellow/red)
- Error budget remaining
- Deployment timeline
- Top-line business metrics
Level 2: Service Dashboard
- Request rate (QPS)
- Error rate (4xx, 5xx breakdown)
- Latency (P50, P95, P99)
- Saturation (CPU, memory, connections)
# ... (condensed) ...
- Cache hit/miss ratio
- Queue depth and processing rate
- Individual endpoint breakdown
- Resource utilization per pod/instanceUSE Method (for infrastructure resources):
Utilization: % of resource being used (CPU usage, disk usage)
Saturation: How overloaded is it (queue depth, swap usage)
Errors: Error count (disk errors, network errors)
RED Method (for services/APIs):
Rate: Requests per second
Errors: Error rate (5xx/total)
Duration: Latency distribution (P50, P95, P99)
Apply RED to every service, USE to every resource.# prometheus.yml
global:
scrape_interval: 15s
evaluation_interval: 15s
scrape_timeout: 10s
rule_files:
- "rules/*.yml"
alerting:
alertmanagers:
# ... (condensed) ...
- source_labels: [__meta_kubernetes_namespace]
target_label: namespace
- source_labels: [__meta_kubernetes_pod_name]
target_label: podgroups:
- name: service-slis
interval: 30s
rules:
# Request rate
- record: service:http_requests:rate5m
expr: sum by (service) (rate(http_requests_total[5m]))
# Error rate
- record: service:http_errors:ratio5m
expr: |
# ... (condensed) ...
# Availability (1 - error rate)
- record: service:availability:ratio5m
expr: 1 - service:http_errors:ratio5m# Request rate per service
sum by (service) (rate(http_requests_total[5m]))
# Error percentage
100 * sum(rate(http_requests_total{status=~"5.."}[5m])) / sum(rate(http_requests_total[5m]))
# P95 latency
histogram_quantile(0.95, sum by (le) (rate(http_request_duration_seconds_bucket[5m])))
# Top 5 endpoints by error rate
topk(5, sum by (path) (rate(http_requests_total{status=~"5.."}[5m])) / sum by (path) (rate(http_requests_total[5m])))
# Memory usage percentage
100 * (1 - node_memory_MemAvailable_bytes / node_memory_MemTotal_bytes)
# CPU saturation (load average > CPU count)
node_load1 > on(instance) count by (instance) (node_cpu_seconds_total{mode="idle"})// tracing.js - Initialize before any other imports
const { NodeSDK } = require('@opentelemetry/sdk-node');
const { OTLPTraceExporter } = require('@opentelemetry/exporter-trace-otlp-grpc');
const { OTLPMetricExporter } = require('@opentelemetry/exporter-metrics-otlp-grpc');
const { getNodeAutoInstrumentations } = require('@opentelemetry/auto-instrumentations-node');
const { Resource } = require('@opentelemetry/resources');
const { ATTR_SERVICE_NAME, ATTR_SERVICE_VERSION } = require('@opentelemetry/semantic-conventions');
const sdk = new NodeSDK({
resource: new Resource({
[ATTR_SERVICE_NAME]: 'api-server',
# ... (condensed) ...
],
});
sdk.start();# otel-collector-config.yaml
receivers:
otlp:
protocols:
grpc:
endpoint: 0.0.0.0:4317
http:
endpoint: 0.0.0.0:4318
processors:
batch:
# ... (condensed) ...
logs:
receivers: [otlp]
processors: [memory_limiter, batch]
exporters: [loki]Rotation:
- Weekly rotations (Mon 9am to Mon 9am)
- Primary + Secondary on-call
- Handoff meeting at rotation boundary
- Maximum 1 week on-call per 4 weeks
Escalation Policy:
1. Alert fires -> Primary on-call notified (PagerDuty/Opsgenie)
2. No acknowledgment in 5 min -> Secondary notified
3. No acknowledgment in 10 min -> Engineering manager notified
4. P1 not mitigated in 30 min -> Incident commander engaged
Compensation:
- On-call pay or comp time
- Page during sleep = extra compensation
- If on-call burden > 2 pages/shift, address root causes## Alert: [Alert Name]
### What This Alert Means
[Brief explanation of what triggered and why it matters]
### Impact
[What users experience when this fires]
### Immediate Actions
1. Check [dashboard link] for current state
2. Run `kubectl get pods -n production` to check pod health
# ... (condensed) ...
### Escalation
- If not resolved in 30 min, page [team-lead]
- If data loss suspected, immediately page [engineering-director]1. DETECT: Alert fires or user reports issue
2. TRIAGE: Assess severity, assign incident commander
3. MITIGATE: Stop the bleeding (rollback, scale up, enable circuit breaker)
4. RESOLVE: Root cause fix deployed and validated
5. FOLLOW-UP: Blameless post-mortem, action items tracked to completionSEV1 - Critical:
Complete outage, data loss, security breach.
All hands. War room. Status page updated.
Communicate every 15 minutes.
SEV2 - Major:
Significant degradation for many users.
Dedicated incident response. Status page updated.
Communicate every 30 minutes.
SEV3 - Minor:
# ... (condensed) ...
SEV4 - Low:
Cosmetic or non-user-facing issue.
Tracked as regular bug.## Incident Post-Mortem: [Title]
**Date:** YYYY-MM-DD
**Duration:** X hours Y minutes
**Severity:** SEV-X
**Author:** [name]
**Reviewers:** [names]
### Summary
[2-3 sentences: what happened, how many users affected, how long]
# ... (condensed) ...
| Add circuit breaker for Z | @team | 2024-02-15 | TODO |
### Lessons Learned
[Key takeaways for the organization]Metrics:
[ ] RED metrics for every service (rate, errors, duration)
[ ] USE metrics for all infrastructure (utilization, saturation, errors)
[ ] Business metrics tracked (signups, orders, revenue)
[ ] Recording rules for frequently-used queries
[ ] Retention policy defined (15 days hot, 13 months cold)
Logs:
[ ] Structured JSON logging everywhere
[ ] Trace ID correlation in every log line
[ ] Log levels used correctly (no ERROR for expected conditions)
# ... (condensed) ...
[ ] Escalation policy configured
[ ] Runbooks up to date
[ ] Post-mortem process established
[ ] On-call handoff meetings scheduledUse this skill when:
Designing or implementing monitoring engineer solutions
Reviewing or improving existing monitoring engineer approaches
Making architectural or implementation decisions about monitoring engineer
Learning monitoring engineer patterns and best practices
Troubleshooting monitoring engineer-related issues
Do NOT use this skill when:
The question is about a fundamentally different technology domain
A more specific sibling skill covers the exact topic needed
The user needs a complete hands-on tutorial rather than expert guidance
# Monitoring Engineer Analysis
## Context Assessment
[Situation summary and constraints]
## Recommended Approach
[Primary recommendation with rationale]
## Implementation Steps
1. [Step with specific details]
2. [Step with specific details]
3. [Step with specific details]
## Trade-offs and Considerations
- [Key trade-off 1]
- [Key trade-off 2]
## Next Steps
- [Immediate action item]
- [Follow-up action item]Input: "Help me implement monitoring engineer for a medium-scale production application"
Output: A structured analysis covering current state assessment, recommended monitoring engineer approach with specific patterns, implementation roadmap with milestones, and risk mitigation strategies tailored to the application scale and constraints.
Legacy system integration: When monitoring engineer must coexist with legacy approaches, provide a gradual migration path rather than a complete rewrite
Scale mismatch: When the solution complexity exceeds the project scale, recommend a simpler approach and note when to revisit
Team skill gaps: When the team lacks experience with the recommended approach, include learning resources and simpler alternatives
Conflicting requirements: When constraints conflict (e.g., performance vs. maintainability), explicitly state the trade-off and recommend based on stated priorities
SKILL.md
530 lines · 2,025 words
The text of SKILL.md is shown as it downloads, under the Apache-2.0 license, by Ferrox Labs. The license is the one in the file's own frontmatter.
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