prd-v08-monitoring-setup

name	prd-v08-monitoring-setup
description	Define monitoring strategy, metrics collection, and alerting thresholds during PRD v0.8 Deployment & Ops. Triggers on requests to set up monitoring, define alerts, or when user asks "what should we monitor?", "alerting strategy", "observability", "metrics", "SLOs", "dashboards", "monitoring setup". Outputs MON- entries with monitoring rules and alert configurations.
context	fork
allowed-tools	["Read","Write","Edit","Glob","Grep","Bash"]
execution_modes	{"default":"standard","supports":["quick","standard","deep"]}

Monitoring Setup

Position in workflow: v0.8 Runbook Creation → v0.8 Monitoring Setup → v0.9 GTM Strategy

Execution Mode

Default is standard. See .claude/rules/08-skill-execution-modes.md for selection logic.

Mode	What this skill produces
quick	RED metrics on critical path only; 3–5 alerts linked to RUN-; single overview dashboard
standard	RED + USE coverage; SLOs for tier-1 services; full alert routing to RUN-; dashboards by audience
deep	Layered coverage (RED + USE + business + UX); multi-tier SLOs with error budgets; baseline calibration from staging; escalation routing

Consumes

This skill requires prior work from v0.8 Runbook Creation and earlier stages:

• RUN-* runbook entries (from v0.8 Runbook Creation) — Incident response runbooks define alerting scenarios; critical alerts must link to RUN- procedures
• DEP-* deployment entries (from v0.8 Release Planning) — DEP- rollback thresholds and post-deploy validation steps inform MON- alert conditions and SLO targets
• API-* endpoint contracts (from v0.6 Technical Specification) — Define baseline latency, throughput, and error rates for application-layer metrics
• KPI-* metrics (from v0.3 Outcome Definition and v0.9 Launch Metrics) — Business metrics (signups, conversions, retention) inform dashboard design and business layer monitoring
• ARC-* architecture decisions (from v0.6 Architecture Design) — System structure determines which components to monitor (monolith has different metrics than distributed services)
• TECH-* technology stack (from v0.5 Technical Stack Selection) — Technology choices (database, cloud provider, APM tools) determine available metrics and monitoring tools

This skill assumes DEP- and RUN- entries are complete with thresholds, rollback conditions, and incident procedures defined.

Produces

This skill creates/updates:

• MON-* entries (monitoring specifications, metric/alert/dashboard/SLO types) — Concrete monitoring rules with thresholds, alert conditions, dashboards, SLO definitions, linked to RUN- procedures
• Alert routing configuration — Mapping of MON- alerts to notification channels and teams; links alerts to RUN- incident procedures
• Observability baseline — Metrics gathered from staging/production, establishing normal operating ranges for alert thresholds

All MON- entries are operational monitoring specifications, not confidence-based. They are:

• Measurable (every metric has a source, unit, and aggregation method)
• Actionable (every alert has a RUN- procedure; no orphaned alerts)
• Thresholded (critical/warning severity with specific numeric conditions)
• Dashboarded (MON- dashboard entries provide visibility to operators and stakeholders)
• SLO-backed (SLO entries tie monitoring to product commitments)

Example MON- entries:

MON-001: API Request Latency (p95)
Type: Metric
Layer: Application
Owner: Backend Team

Name: api.request.latency.p95
Description: 95th percentile response time for all API endpoints (from API-001–020)
Unit: ms
Source: Application APM (Datadog custom instrumentation)
Aggregation: p95 over 5-minute window
Retention: 90 days

Linked IDs: API-001 to API-020, DEP-004 (baseline from staging)

---

MON-002: High Latency Alert (Warning)
Type: Alert
Layer: Application
Owner: Backend Team

Metric: MON-001 (api.request.latency.p95)
Condition: >500ms (from DEP-002 baseline)
Window: 5 minutes
Severity: Warning
Runbook: RUN-001 (Performance Degradation Investigation)

Notification:
  - Channel: Slack #backend-alerts
  - Recipients: Backend on-call, team notified during business hours

Silencing: During scheduled maintenance windows (DEP-004 notifications)

Linked IDs: MON-001, RUN-001, DEP-002

---

MON-003: Critical Latency Alert
Type: Alert
Layer: Application
Owner: Backend Team

Metric: MON-001 (api.request.latency.p95)
Condition: >2000ms (SLA breach, from KPI-001 target)
Window: 2 minutes
Severity: Critical
Runbook: RUN-001 (Performance Degradation Investigation)

Notification:
  - Channel: PagerDuty (wake on-call)
  - Recipients: Backend on-call, Tech Lead, escalate if not acknowledged in 5 min

Silencing: None (critical alerts never silenced)

Linked IDs: MON-001, RUN-001, KPI-001

---

MON-004: API Availability SLO
Type: SLO
Layer: Application
Owner: Platform Team

Objective: API endpoints return non-5xx response
Target: 99.9% uptime (from DEP-002 / KPI-001)
Window: Rolling 30 days
Error Budget: 43.2 minutes/month

Alerting:
  - 50% error budget consumed → Warning to engineering (slow-burn alert)
  - 75% error budget consumed → Critical, freeze non-essential deploys
  - 100% error budget consumed → Post-incident review required (RUN-008 procedure)

Linked IDs: API-001–020, DEP-003 (rollback triggers), RUN-008 (incident review)

---

MON-005: System Health Dashboard
Type: Dashboard
Layer: Infrastructure + Application
Owner: Platform Team

Purpose: Quick health check for on-call engineers (run from RUN-002, RUN-001)
Audience: On-call engineers, engineering leadership, ops team
Panels:
  - API Request Rate (last 1h): Should be steady or increasing
  - API Latency (p50, p95, p99): Watch for p95/p99 creeping up
  - Error Rate by Endpoint: Any 5xx > 0 is concerning
  - Active Critical Alerts: Should be none
  - Database Connection Pool (from MON-006): Trending toward threshold
  - CPU/Memory by Service: Identify resource exhaustion
  - Deployment Status: Current version, time of last deploy
Refresh: 30 seconds

Linked IDs: MON-001, MON-002, MON-003, MON-006, DEP-001, RUN-001/002

---

MON-006: Database Connection Pool Utilization
Type: Metric
Layer: Infrastructure
Owner: Database Team

Name: db.connection_pool.utilized_percent
Description: Percentage of available connections in use (from DEP-001 pool size)
Unit: percentage
Source: Database monitoring (RDS Enhanced Monitoring or custom query)
Aggregation: avg over 1-minute window
Retention: 30 days

Linked IDs: DEP-001 (pool config), RUN-001 (incident when >90%)

Core Concept: Monitoring as Early Warning

Monitoring is not about collecting data—it is about detecting problems before users do. Every metric should answer: "Is this working? If not, what's broken?"

Monitoring Layers

Layer	What to Measure	Why It Matters
Infrastructure	CPU, memory, disk, network	System health foundation
Application	Latency, errors, throughput	User-facing performance
Business	Signups, conversions, revenue	Product health
User Experience	Page load, interaction time	Real user impact

Execution

1. Define SLOs (Service Level Objectives)

   • What uptime do we promise?
   • What latency is acceptable?
   • What error rate is tolerable?

2. Identify key metrics per layer

   • Infrastructure: Resource utilization
   • Application: RED metrics (Rate, Errors, Duration)
   • Business: KPI- from v0.3 and v0.9
   • User: Core Web Vitals, journey completion

3. Set alert thresholds

   • Warning: Investigate soon
   • Critical: Act immediately
   • Base on SLOs and historical data

4. Map alerts to runbooks

   • Every critical alert → RUN- procedure
   • No alert without action path

5. Design dashboards

   • Overview: System health at a glance
   • Deep-dive: Per-service details
   • Business: KPI tracking

6. Create MON- entries with full traceability

MON- Output Template

MON-XXX: [Monitoring Rule Title]
Type: [Metric | Alert | Dashboard | SLO]
Layer: [Infrastructure | Application | Business | User Experience]
Owner: [Team responsible for this metric/alert]

For Metric Type:
  Name: [metric.name.format]
  Description: [What this measures]
  Unit: [count | ms | percentage | bytes]
  Source: [Where this comes from]
  Aggregation: [avg | sum | p50 | p95 | p99]
  Retention: [How long to keep data]

For Alert Type:
  Metric: [MON-YYY or metric name]
  Condition: [Threshold expression]
  Window: [Time window for evaluation]
  Severity: [Critical | Warning | Info]
  Runbook: [RUN-XXX to follow when fired]
  Notification:
    - Channel: [Slack, PagerDuty, Email]
    - Recipients: [Team or individuals]
  Silencing: [When to suppress, e.g., maintenance windows]

For Dashboard Type:
  Purpose: [What questions this answers]
  Audience: [Who uses this dashboard]
  Panels: [List of visualizations]
  Refresh: [How often to update]

For SLO Type:
  Objective: [What we promise]
  Target: [Percentage, e.g., 99.9%]
  Window: [Rolling 30 days]
  Error Budget: [How much downtime allowed]
  Alerting: [When error budget is at risk]

Linked IDs: [API-XXX, UJ-XXX, KPI-XXX, RUN-XXX related]

Example MON- entries:

MON-001: API Request Latency (p95)
Type: Metric
Layer: Application
Owner: Backend Team

Name: api.request.latency.p95
Description: 95th percentile response time for all API endpoints
Unit: ms
Source: Application APM (Datadog/New Relic)
Aggregation: p95
Retention: 90 days

Linked IDs: API-001 to API-020

MON-002: High Latency Alert
Type: Alert
Layer: Application
Owner: Backend Team

Metric: MON-001 (api.request.latency.p95)
Condition: > 500ms
Window: 5 minutes
Severity: Warning
Runbook: RUN-006 (Performance Degradation Investigation)

Notification:
  - Channel: Slack #backend-alerts
  - Recipients: Backend on-call

Silencing: During scheduled deployments (DEP-002 windows)

Linked IDs: MON-001, RUN-006, DEP-002

MON-003: Critical Latency Alert
Type: Alert
Layer: Application
Owner: Backend Team

Metric: MON-001 (api.request.latency.p95)
Condition: > 2000ms
Window: 2 minutes
Severity: Critical
Runbook: RUN-006 (Performance Degradation Investigation)

Notification:
  - Channel: PagerDuty
  - Recipients: Backend on-call, Tech Lead

Silencing: None (always alert on critical)

Linked IDs: MON-001, RUN-006

MON-004: API Availability SLO
Type: SLO
Layer: Application
Owner: Platform Team

Objective: API endpoints return non-5xx response
Target: 99.9%
Window: Rolling 30 days
Error Budget: 43.2 minutes/month

Alerting:
  - 50% budget consumed → Warning to engineering
  - 75% budget consumed → Critical, freeze non-essential deploys
  - 100% budget consumed → Incident review required

Linked IDs: API-001 to API-020, DEP-003

MON-005: System Health Dashboard
Type: Dashboard
Layer: Infrastructure + Application
Owner: Platform Team

Purpose: Quick health check for on-call engineers
Audience: On-call, engineering leadership
Panels:
  - API Request Rate (last 1h)
  - API Latency (p50, p95, p99)
  - Error Rate by Endpoint
  - Active Alerts
  - Database Connection Pool
  - CPU/Memory by Service
Refresh: 30 seconds

Linked IDs: MON-001, MON-002, MON-003

The RED Method (Application Monitoring)

For each service, measure:

Metric	What It Measures	Alert Threshold
Rate	Requests per second	Anomaly detection
Errors	Failed requests / total	>1% warning, >5% critical
Duration	Request latency (p95, p99)	>500ms warning, >2s critical

The USE Method (Infrastructure Monitoring)

For each resource (CPU, memory, disk, network):

Metric	What It Measures	Alert Threshold
Utilization	% of capacity used	>80% warning, >95% critical
Saturation	Queue depth, waiting	>0 for critical resources
Errors	Error count/rate	Any errors = investigate

SLO Framework

Tier	Availability	Latency (p95)	Use For
Tier 1	99.99% (52 min/yr)	<100ms	Payment, auth
Tier 2	99.9% (8.7 hr/yr)	<500ms	Core features
Tier 3	99% (3.6 days/yr)	<2s	Background jobs

Alert Severity Matrix

Severity	User Impact	Response Time	Notification
Critical	Service unusable	<5 min	PagerDuty (wake up)
Warning	Degraded experience	<30 min	Slack (business hours)
Info	No immediate impact	Next day	Dashboard/log

Dashboard Design Principles

Principle	Implementation
Answer questions	Each panel answers "Is X working?"
Hierarchy	Overview → Service → Component
Context	Show thresholds, comparisons
Actionable	Link to runbooks from alerts
Fast	Quick load, auto-refresh

Anti-Patterns

Pattern	Signal	Fix
Alert fatigue	Too many alerts, team ignores	Tune thresholds, remove noise
No runbook link	Alert fires, no one knows what to do	Every alert → RUN-
Vanity metrics	"1 million requests!" without context	Focus on user-impacting metrics
Missing baselines	No historical comparison	Establish baselines before launch
Over-monitoring	500 metrics, can't find signal	Focus on RED/USE fundamentals
Under-monitoring	"We'll add monitoring later"	Monitoring ships with code

Quality Gates

Before proceeding to v0.9 GTM Strategy:

• SLOs defined for critical services (MON- SLO type)
• RED metrics configured for application layer
• USE metrics configured for infrastructure layer
• Critical alerts linked to RUN- procedures
• Overview dashboard created for on-call
• Alert notification channels configured
• Baseline metrics established from staging

Downstream Connections

Consumer	What It Uses	Example
On-Call Team	MON- alerts trigger response	MON-003 → page engineer
v0.9 Launch Metrics	MON- provides baseline data	MON-001 baseline → KPI-010 target
Post-Mortems	MON- data for incident analysis	"MON-005 showed spike at 14:32"
Capacity Planning	MON- trends inform scaling	USE metrics → infrastructure planning
DEP- Rollback	MON- thresholds trigger rollback	MON-002 breach → DEP-003 rollback

Detailed References

• Monitoring stack examples: See references/monitoring-stack.md
• MON- entry template: See assets/mon-template.md
• SLO calculation guide: See references/slo-guide.md
• Dashboard best practices: See references/dashboard-guide.md