Observability Design Review

Observability Design Review

Review logging, metrics, tracing, context propagation, SLO/SLI, alerts, dashboards, sampling, retention, privacy, and cost designs before implementation. It produces OBS-## findings and validation preparation; it does not read runtime signals to declare health or choose SLO/incident severity.

When to Use

• Use it to check whether signal fields/dimensions, semantics, correlation IDs, sampling, retention, and alerts are actionable.
• Use it to identify sensitive-data, cardinality, alert-noise, blind-spot, and cost risks.
• Use it when runtime data is unavailable and the telemetry design itself needs review.

Do not use it to query production logs, execute probes, analyze a real incident, or declare system health.

Output Format Options

• Use Markdown by default; when a table, CSV, or JSON is requested, preserve the same evidence, status, impact, owner, and validation fields.
• Do not present a structured format or static inventory as execution, pass, approval, or release evidence.

How to Use

1. Read this Skill's primary prompt and provide the objective, scope, material, environment, and available evidence.
2. Follow the prompt's input audit and output contract; deliver a bounded first pass when information is incomplete.
3. Retain source, evidence status, impact, owner role, close condition, and validation method for every finding.

Workflow

1. Read prompts/observability-design-review.md and audit objective, service scope, time window, privacy, and sources.
2. Classify material as known, missing, conflicting, stale, out_of_scope, and assumptions.
3. Build a signal/service coverage matrix and record field semantics, detection action, impact, owner, and evidence in OBS-## findings.
4. Separate design facts, evidence-backed inferences, recommendations, and Human decisions; without runtime signals mark conclusions unverified or unassessed.
5. Give safe minimum validations for sensitive fields, unbounded cardinality, sampling gaps, and non-actionable alerts.

Core Constraints

• Do not read or modify real production signals or execute probes; a dashboard is not proof of alert effectiveness.
• Do not choose SLOs, incident severity, sample rate, retention, cost budget, or owners by default.
• Every OBS-## includes signal, object, fields/dimensions, semantics, source/evidence, gap, impact, detection action, owner, and validation.
• Without runtime identity, time, environment, and raw signals, runtime conclusions remain unverified, unexecuted, or unassessed.

Reference Files

• Always read prompts/observability-design-review.md before producing a review.
• For regression, read evals/eval.yaml and its cases; a design check is not log, trace, or metric analysis.
• For trigger checks, use evals/trigger-prompts.csv and evals/local-rules.json; missing selection trace is BLOCKED.

Best Practices

• Prioritize high-impact gaps with a verifiable next action, using the smallest useful experiment or evidence request.
• Separate facts, evidence-backed inferences, recommendations, and Human decisions; never upgrade an assumption into a conclusion.

Delivery Checklist

• Audit services, signals, scope, privacy, cost, and evidence.
• Cover logs, metrics, traces, propagation, SLO/SLI, alerts, dashboards, sampling, retention, and sensitive data.
• Give every OBS-## field semantics, impact, owner, and validation method.
• Separate design presence from real runtime signals.
• Do not choose SLOs, incident severity, or risk acceptance for a Human.

Common Pitfalls

• Treating a dashboard as an actionable alert.
• Listing signal names without fields, dimensions, semantics, or correlation.
• Ignoring sensitive data, cardinality, sampling, retention, and cost constraints.