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/metric-reconciliation

@fcb0454

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.

Use this Skill: https://skilld.dev/gh/nimrodfisher/data-analytics-skills/metric-reconciliation

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SKILL.md

≈52 tokens always: the name and description. ≈570 when used: this file. ≈1k more on demand in 2 files.

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)

Source: SKILL.md on GitHub

No alerts7d4 checks · Risk SAFE
  • Gen Agent Trust Hub7d

    The skill is designed for data reconciliation and metric comparison across different sources using Python scripts. The technical risk is minimal, primarily involving the ingestion of external data from CSV files and SQL databases, which serves as a surface for potential indirect prompt injection attacks.

  • Socket7d

    No alerts

  • Snyk7d

    Risk: LOW · No issues

  • ZeroLeaks5mo

    Score: 93/100 · 2 sections analyzed

Signed by skilld at fcb0454. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 5 days ago.

Activeupdated 5 months ago

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