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analyzer

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.

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SKILL.md（原文）

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Analyzer

Turn data into defensible findings instead of only returning rows.

Analysis patterns

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.

Before concluding

  • 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 format

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

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