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Found 2 Skills
Analyze VictoriaMetrics time series cardinality to find optimization opportunities — unused metrics, high-cardinality labels, problematic label values, histogram bloat. Produces actionable report with relabeling and stream aggregation recommendations. Use whenever the user mentions cardinality analysis, series reduction, unused metrics, high cardinality labels, TSDB optimization, storage cost reduction, metric cleanup, too many time series, or wants to reduce cardinality. Also trigger when discussing relabeling strategies, streaming aggregation opportunities, or "which metrics can we drop".
Design VictoriaMetrics stream aggregation rules to reduce cardinality, sample frequency, or query load. Use whenever the user wants to apply, plan, or troubleshoot stream aggregation (`-streamAggr.config`, `-remoteWrite.streamAggr.config`), asks how to aggregate a high-cardinality metric, mentions downsampling at vmagent, replacing recording rules with stream aggregation, picking the right aggregation output (`total`, `rate_sum`, `sum_samples`, `quantiles`, `histogram_bucket`, etc.), choosing an aggregation interval, deciding where to place vmagent in the delivery pipeline, or whether to scale vmagent for aggregation. Also triggers on "aggregate histograms", "drop labels via vmagent", "reduce ingested samples", "pre-aggregate at vmagent", "stream aggregation accuracy", and "stream aggregation duplicates in cluster".