MongoDB Aggregation Pipeline Optimization
General MongoDB aggregation pipeline optimization techniques including early filtering, index usage, array operators vs $unwind, $lookup optimization, and performance debugging. Use when writing aggregation queries for ANY MongoDB project, debugging slow pipelines, or analyzing query performance. For M32RIMM-specific patterns, use mongodb-m32rimm-patterns skill.
aggregating-gauge-metrics
Aggregate pre-computed metrics (gauge, counter, delta types) using OPAL. Use when analyzing request counts, error rates, resource utilization, or any numeric metrics over time. Covers align + m() + aggregate pattern, summary vs time-series output, and common aggregation functions. For percentile metrics (tdigest), see analyzing-tdigest-metrics skill.
aggregating-event-datasets
Aggregate and summarize event datasets (logs) using OPAL statsby. Use when you need to count, sum, or calculate statistics across log events. Covers make_col for derived columns, statsby for aggregation, group_by for grouping, aggregation functions (count, sum, avg, percentile), and topk for top N results. Returns single summary row per group across entire time range. For time-series trends, see time-series-analysis skill.