name	analyzer
description	Analyze queried data for trends, week-over-week comparisons, distributions, funnels, cohorts, top-N lists, anomalies, sanity checks, and report-ready findings. Use after or alongside ClickHouse queries when the user wants insight rather than raw rows.

Analyzer

Turn data into defensible findings instead of only returning rows.

Choose the smallest pattern that answers the question:

Trend: metric over time at the right grain.
Comparison: current period vs prior period, release vs baseline, or segment A vs B.
Distribution: percentiles, skew, tails, and outliers.
Funnel: step counts, conversion rates, and drop-offs.
Cohort: behavior grouped by start date, version, source, or first action.
Top-N: largest contributors with share of total.
Sanity check: row counts, null rates, first/last seen, duplicates, and data freshness.

Verify the time window and grain match the user's question.
Check sample size, nulls, and whether the metric is dominated by a small tail.
Look for freshness, rollout, telemetry opt-in, or version-coverage issues.
Avoid causal language unless the query design supports causality.
If the result is surprising, run or propose one validation query before presenting it as fact.

Finding: ...
Evidence: ...
Confidence: High/Medium/Low because ...
Caveats: ...
Recommended next check: ...

name	analyzer
description	Analyze queried data for trends, week-over-week comparisons, distributions, funnels, cohorts, top-N lists, anomalies, sanity checks, and report-ready findings. Use after or alongside ClickHouse queries when the user wants insight rather than raw rows.

Turn data into defensible findings instead of only returning rows.

Choose the smallest pattern that answers the question:

Trend: metric over time at the right grain.
Comparison: current period vs prior period, release vs baseline, or segment A vs B.
Distribution: percentiles, skew, tails, and outliers.
Funnel: step counts, conversion rates, and drop-offs.
Cohort: behavior grouped by start date, version, source, or first action.
Top-N: largest contributors with share of total.
Sanity check: row counts, null rates, first/last seen, duplicates, and data freshness.

Verify the time window and grain match the user's question.
Check sample size, nulls, and whether the metric is dominated by a small tail.
Look for freshness, rollout, telemetry opt-in, or version-coverage issues.
Avoid causal language unless the query design supports causality.
If the result is surprising, run or propose one validation query before presenting it as fact.

Finding: ...
Evidence: ...
Confidence: High/Medium/Low because ...
Caveats: ...
Recommended next check: ...

analyzer