observability-plan

name

observability-plan

description

Design an observability plan for a service — user-journey SLIs, SLOs with error budgets, symptom-based burn-rate alerting (page vs ticket, every page actionable and runbook-linked), and a deliberate metrics/logs/traces split with a cardinality and cost budget. Use when the user needs SLOs, alerting design, a monitoring/observability strategy, dashboards, or wants to fix alert fatigue and know what to measure.

observability-plan

Designs the telemetry that makes a service debuggable at 3am and detectable before users complain — SLOs measured from the user's experience, alerts that fire only when a human must act, and the three pillars each used for what they're good at. Expands the one-line observability requirement from nfr-spec into a full operating plan; upstream of incident-comms and postmortem-generator (you can't communicate or analyze an incident you couldn't detect). Distinct from success-metrics (business outcomes) — this is operational health.

How to respond

1. Start from user journeys, not from the resource dashboard. The first artifact is the list of critical journeys ("log in", "place order", "load feed") — because an SLI measures what the user experiences, and CPU graphs measure what's easy. Ask at most 2 questions, spent on the most critical journey and what "down" means to a user there. Resource metrics (the golden signals) still get covered — but as saturation/cause signals, never as the SLO.

2. Define SLIs as good-events / valid-events from the user's side (catalog in reference.md) — availability (success rate), latency (a threshold, measured at a percentile: "% of requests < 300ms"), and where relevant freshness, correctness, throughput. ✅ "SLI: proportion of checkout requests returning 2xx within 1s, measured at the load balancer" — ❌ "SLI: average CPU" (the user never feels your CPU). Latency SLIs are a threshold count, not an average — averages hide the tail that hurts.

3. Set SLOs with an explicit error budget and window. Each SLI gets a target over a rolling window: "99.9% over 28 days." That target is the error budget (0.1% = ~40 min/28d) — the quantity that governs both alerting and how aggressively you ship. State the budget in human terms (minutes/month) and name the error-budget policy: what happens when it's spent (freeze risky releases, redirect to reliability) — an SLO with no policy is a number nobody defends. Pick targets honestly (99.9% is not free; see nfr-spec's down-for-an-hour calibration) — more nines is exponentially more cost.

4. Alert on symptoms, page only on what's actionable now. The hard rule that kills alert fatigue: a page means a human must act immediately; everything else is a ticket or a dashboard. Alert on SLO burn (the user is being hurt), not on every cause (a single CPU spike that auto-recovered). Cause-based signals inform diagnosis once you're paged — they don't page. Use multi-window, multi-burn-rate alerts (reference.md): a fast burn (budget being consumed quickly) pages; a slow burn opens a ticket. Every page is actionable, deduplicated, severity-tiered, and maps to a runbook entry (→ runbook-generator) — a page with no runbook is a 3am puzzle, not an alert.

5. Split the three pillars deliberately — each for what it's good at:

   • Metrics — cheap, aggregatable, the basis of SLOs and alerts. Low-cardinality. Answer "is it broken and how much?"
   • Logs — expensive, high-detail, for debugging the specific failure. Structured (not string soup), sampled on the hot path, with a retention/cost budget. Answer "what exactly happened to this request?"
   • Traces — request flow across services, for "where in the chain did the latency/error come from?" Sample intelligently (tail-based on errors/slow); propagate context across every hop. Don't put in logs what should be a metric (cardinality and cost explode), and don't try to metric what needs a trace (you'll never reconstruct the path from counters).

6. Set a cardinality and cost budget. High-cardinality labels (user_id, request_id, full URL) on metrics multiply time series and the bill without bound — name which labels are allowed on metrics (bounded sets: region, status_class, endpoint-template) and push the high-cardinality identifiers to logs/traces where they belong. State the log-volume and metric-series budget (ties to capacity-cost-model); observability is routinely a top-3 cloud line item.

7. Specify dashboards by the question each answers, not by graph count. A service dashboard shows the golden signals + SLO/budget status at a glance (the at-a-glance health check); a debugging dashboard supports drill-down. The 3am questions from nfr-spec become concrete panels. A wall of 100 graphs is where signal goes to hide.

8. Emit with templates/observability-plan.md in one message: critical journeys, SLI/SLO table with error budgets, the error-budget policy, the alert table (condition / window / burn-rate / page-or-ticket / runbook link), the metrics-logs-traces split, the cardinality+cost budget, and dashboards-by-question. Instrumentation naming conventions and trace-context propagation noted.

Useful references in this skill

• reference.md — golden signals, SLI types by component, SLO/error-budget math, the multi-window burn-rate alert table, the metrics-vs-logs-vs-traces decision guide, and alert-quality rules
• templates/observability-plan.md — the plan skeleton with SLO, alert, and pillar tables

Quality bar

• SLIs measure user experience (good/valid events), not resource utilization. Latency SLIs are threshold counts at a percentile, never averages.
• Every SLO has an error budget in human terms and a policy for when it's spent. A target with no budget consequence is decoration.
• Pages are actionable, symptom-based, and runbook-linked. If a page doesn't require an immediate human action, it's a ticket — the plan re-tiers it.
• Burn-rate alerting is used, not static "CPU > 80%" thresholds, for SLO-backed surfaces.
• The three pillars are split by purpose, with high-cardinality identifiers kept off metrics. Logs are structured and budgeted.
• A cardinality/cost budget exists. Observability without a cost ceiling becomes the surprise invoice.
• Dashboards are defined by the question they answer, not by quantity.

When to use this skill

• ✅ Standing up monitoring/SLOs for a new or existing service
• ✅ "What should we measure / alert on?" / designing an alerting strategy
• ✅ Fixing alert fatigue — too many pages, most non-actionable
• ✅ Turning nfr-spec's observability requirement into an implementable plan

When NOT to use this skill

• ❌ Business/product success metrics — that's success-metrics
• ❌ Customer/stakeholder comms during an outage — that's incident-comms
• ❌ The post-incident analysis — that's postmortem-generator
• ❌ The one-line observability requirement in a spec — that's nfr-spec; this is the full design beneath it

Anti-patterns to avoid

• ❌ Resource metrics as SLOs. Alerting on CPU/memory/disk as if they were user pain — they auto-recover, they spike harmlessly, and they page you at 3am for nothing while the actual user-facing error goes unnoticed.
• ❌ Averages for latency. "Average response 120ms" with a p99 of 4s means a lot of users are suffering invisibly. Measure the percentile and the threshold.
• ❌ Cause-based paging. A page for every component hiccup. Page on the symptom (users hurt / budget burning fast); let causes inform diagnosis, not wake people.
• ❌ The alert with no runbook. Firing a page that says "high error rate" with no "here's what to check" — that's a puzzle handed to someone half-asleep.
• ❌ Unbounded metric cardinality. user_id or request_id as a metric label — millions of time series, a five-figure bill, and a dashboard that times out. Those belong in logs/traces.
• ❌ String-soup logs. Unstructured printf logs you can't query or aggregate, retained forever at full volume. Structure them, sample the hot path, budget the retention.
• ❌ The 100-graph dashboard. Built to look thorough, used by no one, because no panel answers a question anyone asks under pressure.
• ❌ 99.99% by reflex. Copying a target off a blog instead of from the down-for-an-hour cost calibration; every nine multiplies the engineering and on-call burden.