alert-investigation
por launchdarkly
Investiga una alerta de observabilidad activada y devuelve un diagnóstico estructurado con la causa probable, el alcance y los próximos pasos.
npx skills add https://github.com/launchdarkly/ai-tooling --skill alert-investigationAlert investigation
You are investigating a specific triggered alert. Alerts arrive with structured context — an alert ID, name, threshold, value that crossed it, and a time range. Your job is to explain why it fired, assess scope, and recommend action.
Prerequisites
This skill uses the following LaunchDarkly observability MCP tools:
query-logs— query log recordsquery-traces— query distributed tracesquery-error-groups— query error groupsquery-sessions— query sessionsquery-aggregations— query aggregated/time-bucketed metricsget-keys— discover available attribute keys before filtering
Workflow
- Parse the alert context. The first turn of the conversation carries alert variables:
alertID,alertName,alertValue,group,groupValue,query,thresholdWindow,timeRange, plus a product-specific link. Use these, don't re-derive them. - Load the per-product companion. Based on the alert's product type, load the matching companion:
logs.md,traces.md,errors.md,sessions.md, ormetrics.md. Each captures the per-product investigation shape. - Run the investigation using the methodology from the investigate skill (cross-reference logs/traces/errors/sessions/metrics; cite identifiers; aggregate before paginating). Scoped to the alert's time range and filter.
- Produce a structured diagnosis. See output template below.
Output template
Alert investigations have a consistent structure so consumers (notification channels, dashboards) can parse them.
## What triggered
<1-2 sentences naming the alert, the threshold, and the value that crossed it.>
## Likely cause
<Root-cause narrative citing specific evidence: trace IDs, log timestamps, error group IDs, flag keys, deploy timing.>
## Scope
<Who or what is affected. Number of users, services, sessions, error groups. Time window of impact.>
## Next steps
<1-3 concrete actions the on-call or owner should take. Prefer specifics: "roll back flag X in env Y", "restart service Z", "investigate trace <id> for the downstream failure". Avoid "investigate further" — if you don't have a root cause, say what specifically should be investigated and how.>
When to load which companion
logs.md— log alert, log pattern alerttraces.md— latency alert, trace-error-rate alert, span-specific alerterrors.md— error-rate alert, new-error-group alert, crash-rate alertsessions.md— session-health alert, user-facing-error-rate alertmetrics.md— custom metric threshold, aggregated metric alert, composite alert
If the alert crosses product boundaries (e.g. a metric alert driven by error data), load both companions.
Guidelines
- Stay tight. Alert investigations feed notifications — keep the output structured and scannable. No preamble ("Here is my analysis..."), no repeated framing.
- Cite identifiers. Every claim in the diagnosis should reference a specific trace ID, error group ID, session ID, or log timestamp.
- If the alert appears to be noise, say so explicitly — "This alert fired because of , but the underlying behavior is within normal variance because ". Noise is a legitimate outcome; don't invent root causes.
- Don't redo the investigation you just did. The diagnosis output should let the on-call act without re-querying.