alert noise correlator

The problem

Alert storms overwhelm on-call engineers with dozens of notifications for a single incident. Determining whether a database lag caused the API errors or vice versa is impossible when staring at a wall of timestamped logs.

What it does

• Normalizes mixed or ambiguous timezones to UTC to prevent false causal sequencing.
• Reconstructs causal chains by mapping dependencies between services, resources, and alert semantics.
• Identifies feedback loops, such as restart storms or OOM-kill cycles, that linear fixes won't solve.
• Calculates the specific leverage point in each chain that silences the most downstream links.
• Isolates independent alerts that lack temporal or dependency connections to the main incident.

Why this beats prompting it yourself

General LLMs often hallucinate causation based strictly on which alert appeared first in a list. This skill enforces a rigorous link-grading system that requires dependency evidence before labeling an event as a cause, preventing dangerous "fix the symptom" loops. It also includes specific logic to detect clock skew and timezone ambiguity, which are the primary causes of incorrect incident post-mortems.

Use cases

• Collapsing a flood of PagerDuty or Datadog alerts into a single root cause during a live incident.
• Reconstructing the propagation path of a cascading failure across microservices.
• Identifying cyclical restart loops that are being misdiagnosed as linear service failures.
• Auditing historical alert data to find the single most effective place to add circuit breakers.

Known limitations

Input is capped at 60 alerts per pass to maintain reasoning density. Requires at least two timestamped alerts to establish causation; otherwise, it defaults to resource-grouping only.