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metric-reconciliation

Trace and resolve discrepancies when the same metric shows different values in two or more sources. Use before reporting, after pipeline changes, or when stakeholders question a number.

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包含的文件(4)

  • SKILL.md2.5 KB
  • assets/reconciliation_report_template.md1.6 KB
  • references/reconciliation_patterns.md2.4 KB
  • scripts/reconcile_metrics.py4.9 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Metric Reconciliation

When to use

  • Two dashboards or reports show different values for the same KPI
  • A metric changed unexpectedly after a data pipeline update
  • Stakeholders question a number and need an authoritative explanation
  • Preparing to merge or deprecate a legacy reporting source
  • Onboarding analysts to a new data model and validating it against the old one

Process

  1. Define the metric and scope — confirm the exact definition (numerator, denominator, filters, time zone) and the period under investigation. Mismatched definitions are the most common cause of discrepancy.
  2. Pull values from both sources — extract the metric values for the same period from each source. Record absolute values, row counts, and the query or calculation path used.
  3. Compute the gap — calculate the absolute difference and percentage gap. If the gap is within an agreed tolerance (e.g., ±0.1%), document it as accepted and close. See references/reconciliation_patterns.md for tolerance guidelines.
  4. Trace the computation path — walk each source's query or pipeline step by step. Common divergence points: different join types, filter order, null handling, date truncation, or deduplication logic.
  5. Identify the root cause — classify the cause using references/metric_discrepancy_guide.md (definition mismatch, data freshness, aggregation grain, calculation bug). Document the divergence point with a code snippet or query excerpt.
  6. Resolve and document — fix the calculation or accept a canonical source, then complete assets/reconciliation_report_template.md and share with stakeholders.

Inputs the skill needs

  • The metric name and business definition (numerator, denominator, any known variants)
  • Access to both sources (queries, dashboard SQL, or raw data)
  • The time period showing the discrepancy
  • Row counts or record-level data to enable line-by-line comparison if needed
  • Any known recent changes to pipelines, schema, or business rules

Output

  • assets/reconciliation_report_template.md — completed report showing source A vs. source B, the gap, root cause, and resolution status
  • A corrected query or pipeline change (if a bug was found)
  • A documented tolerance agreement (if the gap is acceptable)

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