Makes systems debuggable and reliably operable — instrumentation, alerting that is worth waking for, service objectives, and learning from failure. Use this to instrument a service, fix alerting that is ignored, set error budgets or reliability targets, prepare for on-call, or run a blameless post-incident review.
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Monitoring tells you a thing you predicted is happening. Observability lets you ask a question you did not anticipate. Production failures are mostly the unanticipated kind.
Emit structured events with enough context to slice afterwards — request identifiers, user or tenant, version, dependency, outcome, duration. Free-text logs are unsearchable at volume and become expensive noise.
Propagate a correlation identifier across every hop. Without it, a distributed system is a set of independent stories and reconstructing one request is manual archaeology.
Measure what the user experiences at the percentile they experience it. A p50 latency graph is mostly a graph of the people who were not affected.
Alert when users are affected or imminently will be. High CPU is not an alert; requests failing or slowing is. Cause-based alerting produces pages for conditions the system handled and no page for novel failures that hurt.
Every alert must be actionable, urgent and specific. If the recipient's honest response is to look and close it, delete the alert — it is training the on-call to ignore the page, and the ignored page is eventually the real one.
Alert fatigue is the actual reliability risk in most organizations. Fewer, better alerts beat coverage.
Set service level objectives from what users need, then treat the remainder as a budget to spend. This converts a sterile argument between shipping and stability into arithmetic: budget remaining means ship, budget exhausted means the next work is reliability.
Keep the internal objective tighter than any external commitment made through
operations:service-level-management, so you find out before the customer does.
Post-incident review exists to find what made the failure possible and hard to detect, not who touched it last. Human error is a starting question, never the finding: what made the error easy, and why did nothing catch it?
Track the time to detect separately from time to resolve. Long detection is an observability defect, and it is the part that repeats.
Produce a small number of real actions with owners and dates. A review generating fifteen actions generates none.
references/sources.md in this skill lists the outside authorities that settle the questions
here — what each one is authoritative for, and what you may do with it. Check them before
answering on anything they cover, and cite what you used. Most are free to read and not free
to reproduce; the use note on each is binding.
Metrics and traces: Datadog, Grafana with Prometheus, New Relic, Honeycomb, and similar. Errors: Sentry, Rollbar, and similar. Logs: Elastic, OpenSearch, Loki, Splunk, and similar.
On-call and incident management: PagerDuty, Opsgenie, incident.io, FireHydrant, and similar.
Instrument with OpenTelemetry wherever you can. Vendor-specific instrumentation is the part that makes leaving expensive.
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Designs and audits who can reach what — authentication, authorization models, privileged access, service credentials, and joiner-mover-leaver process. Use this to design a permissions model, run an access review, reduce standing privilege, handle offboarding, set up SSO or MFA, manage service and machine credentials, or diagnose why permissions have sprawled.
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Concentrates marketing and sales effort on a named set of accounts rather than on volume — qualifying whether the model fits your economics at all, building the account list and the buying group inside each, tiering effort against account value, coordinating so the account experiences one campaign rather than several, and measuring account progression instead of leads. Use this to decide whether to run an account-based program, build one, or work out why an existing one produces activity and no pipeline.
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Gets new users from signup to first real value — signup flow, onboarding, time-to-value, and the early experience that determines whether someone becomes a user or a lapsed account. Use this to design or fix signup and onboarding, diagnose why signups do not convert to active use, reduce time-to-value, or decide what a new user must accomplish first.
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Designs orchestrator-and-subagent hierarchies for a repository — splitting agents by exclusive write surface, pairing every producer with an independent auditor, and enforcing the split with a script that runs in CI. Use this whenever the user wants to set up, expand, audit, or fix a multi-agent or subagent structure for a codebase; asks how to divide work between agents; wants agent charters, roles, or a surface map written; or is hitting agents that collide on the same files, review their own work, or drift from their remit. Also use when sizing a roster or deciding whether a new agent is justified.
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Governs models and AI systems in production — intended use, evaluation, monitoring, human oversight, documentation, and the decision to deploy or retire. Use this before deploying a model or AI feature, when defining evaluation criteria, when a model's behavior has drifted, when assessing AI risk or regulatory exposure, or when deciding whether an AI system is fit for a consequential decision.
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Produces executive-level research — market sizing, competitor mapping, trend analysis, and strategic intelligence — grounded in cited sources with the confidence in each claim made explicit. Use this to analyze a market or industry, map competitors, evaluate a market-entry or build-versus-buy decision, produce a research brief, or assemble evidence for a decision. Also use when comparing options that need a structured, evidence-based verdict rather than an opinion.
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