Alert Investigation

作者 launchdarkly2fc544d3140fApache-2.026 个星标收录于 2026年10月8日更新于 2026年10月8日仓库昨天更新

Investigates a triggered observability alert and returns a structured diagnosis with likely cause, scope, and next steps.

AI 生成的概览

调查已触发的可观测性告警,并返回包含可能原因、影响范围和后续步骤的结构化诊断。

功能
引导智能体利用告警的结构化上下文(如告警 ID、名称、阈值、越界值和时间范围)调查特定已触发告警。它会根据产品类型加载对应的日志、链路追踪、错误、会话或指标配套文件,并在这些信号上执行限定范围的调查。最终产出结构化诊断,涵盖触发内容、可能原因、影响范围和后续步骤,并引用链路追踪 ID、错误组 ID、会话 ID 和日志时间戳等标识符。
适用场景
适用于可观测性告警已触发、值班人员或负责人需要了解触发原因、受影响范围以及下一步操作时。适合与日志、链路追踪、错误、会话或指标相关的告警,包括跨产品边界的复合告警。
运行要求
需要远程托管的 LaunchDarkly 可观测性 MCP 服务器,以及用于查询日志、链路追踪、错误组、会话、聚合指标和属性键的工具。不附带脚本,仅为说明文档。

Alert 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 records
  • query-traces — query distributed traces
  • query-error-groups — query error groups
  • query-sessions — query sessions
  • query-aggregations — query aggregated/time-bucketed metrics
  • get-keys — discover available attribute keys before filtering

Workflow

  1. 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.
  2. Load the per-product companion. Based on the alert's product type, load the matching companion: logs.md, traces.md, errors.md, sessions.md, or metrics.md. Each captures the per-product investigation shape.
  3. 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.
  4. 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 alert
  • traces.md — latency alert, trace-error-rate alert, span-specific alert
  • errors.md — error-rate alert, new-error-group alert, crash-rate alert
  • sessions.md — session-health alert, user-facing-error-rate alert
  • metrics.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 <X>, but the underlying behavior is within normal variance because <Y>". 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.

来源与署名

来源:launchdarkly/ai-tooling位于skills/observability/alert-investigation提交2fc544d

许可证: Apache-2.0

内容归原作者所有。SourceWeft 从公开仓库中收录这些内容。

举报或申请下架