Artifacts

@alex #dashboard AWS spending by service for the last 30 days
@oliver #report quarterly security assessment across all accounts
@kai #dashboard cluster resource utilization

@alex #dashboard Generate a comprehensive AWS cost dashboard for [start_date] to [end_date].

Include:
- Monthly spending trends by service with month-over-month growth rates
- Top 10 cost drivers and their utilization patterns
- Reserved Instance vs On-Demand cost comparison
- Cost anomalies and optimization opportunities with estimated savings

Segment by: [cost allocation tags such as environment, team, or application]

@anna #dashboard Create an operational dashboard correlating database performance with infrastructure costs for [time_period].

Analyze:
- Aurora and DocumentDB query performance metrics
- Resource utilization and spending patterns
- Correlation between database load and compute and storage costs

Context: [recent changes, migrations, or specific concerns]

@tony #chart Show query execution time trends for Aurora cluster [cluster-identifier] over the past [time_period].

- Metrics: p50, p95, p99 query latency
- Separate lines for read queries vs write queries
- Highlight queries exceeding [threshold] ms

Template: database_performance_review
@tony #dashboard Create a performance dashboard for Aurora cluster {cluster_id} covering {time_period}.

Include:
- Slow query analysis (queries exceeding {latency_threshold} ms)
- Resource utilization trends (CPU, memory, IOPS)
- Replica lag monitoring
- Connection pool health

Compare against baseline: {comparison_period}
Alert on: queries exceeding p95 latency of {latency_threshold} ms

Template: cost_anomaly_investigation
@alex #report Investigate the cost anomaly for {service_name} on {date}.

- Compare costs to the 7-day and 30-day averages
- Break down by cost component (compute, storage, I/O, data transfer)
- Identify the specific resources driving the increase and quantify the impact
- Recommend immediate actions to mitigate ongoing cost increases