Which AWS services are supported?

The skill includes specialized diagnostics for EC2, Lambda, S3, NAT Gateway, RDS, and Data Transfer services, among others.

What kind of output can I expect for my team?

It provides a Slack-ready summary, a detailed evidence-based analysis, estimated financial impact, containment actions, and a pre-formatted Jira ticket body.

Does it require CloudTrail data?

CloudTrail data is optional but recommended; if provided, the skill can correlate specific API calls with the timing of the spend spike to provide a higher confidence level in its diagnosis.

How does this skill identify the root cause of AWS spikes?

The skill parses billing diffs or anomaly alerts and correlates the data against a knowledge base of common service-specific issues like runaway Lambda triggers or NAT Gateway misconfigurations.

AWS Cost Anomaly Explainer

Name: AWS Cost Anomaly Explainer
Author: dvcrn

bydvcrn

•

Cloud Infrastructure

Diagnoses and explains AWS billing spikes and cost anomalies in plain English to help teams respond to incidents quickly.

The AWS Cost Anomaly Explainer acts as an automated cloud cost incident responder for Claude Code, designed to instantly diagnose the root cause of unexpected spend spikes. By parsing billing alerts or usage diffs, the skill identifies affected services—such as EC2, Lambda, or NAT Gateways—and correlates them with common failure patterns like infinite retry loops or cross-region replication errors. It provides a comprehensive response including estimated impact, immediate containment actions, and prevention strategies, even generating ready-to-paste Jira tickets and Slack summaries to streamline team communication during financial incidents.

Key Features

01Automated parsing of AWS billing alerts and usage diff data

02CloudTrail event correlation for high-confidence incident diagnosis

03Service-specific root cause correlation for major AWS resources

043 GitHub stars

05Standardized output for Jira tickets and Slack notifications

06Actionable containment steps to stop immediate spend leakage

Use Cases

01Explaining the technical root cause of an EC2 Auto Scaling misconfiguration to stakeholders

02Investigating a sudden spike in S3 or Data Transfer costs reported by AWS Budgets

03Generating incident documentation and prevention measures for DevOps post-mortems

Key Features

01Automated parsing of AWS billing alerts and usage diff data

02CloudTrail event correlation for high-confidence incident diagnosis

03Service-specific root cause correlation for major AWS resources

043 GitHub stars

05Standardized output for Jira tickets and Slack notifications

06Actionable containment steps to stop immediate spend leakage

Use Cases

01Explaining the technical root cause of an EC2 Auto Scaling misconfiguration to stakeholders

02Investigating a sudden spike in S3 or Data Transfer costs reported by AWS Budgets

03Generating incident documentation and prevention measures for DevOps post-mortems