mcpField
R
root-cause-investigation

root-cause-investigation

github.com/nimrodfisherData & databases504253 installsUpdated 5w ago
Repository

About root-cause-investigation

A data-analysis skill for investigating unexpected metric changes, from validation through a quantified root-cause report. It is intended for teams that need evidence-based explanations and recommendations.

Key features

  1. 01Validate changes against normal variance
  2. 02Trace when a change began
  3. 03Decompose metrics into components
  4. 04Rank dimensional contributors by impact
  5. 05Test hypotheses against evidence
  6. 06Generate a structured root-cause report

Use cases

  1. 01Investigate an unexpected metric drop or spike
  2. 02Assess whether a change is signal or noise
  3. 03Analyze metric impact after an incident
  4. 04Review a trend shift retrospectively

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/nimrodfisher/data-analytics-skills --skill root-cause-investigation

After installing, type/root-cause-investigationin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

assets/
rca_report_template.md
references/
hypothesis_testing_guide.md
rca_framework.md
scripts/
drilldown_analyzer.py

Details

From the project README

Root Cause Investigation

When to use

  • A key metric dropped (or spiked) unexpectedly and the team needs an explanation
  • Stakeholders are asking "why did X happen?" and need an evidence-based answer
  • A metric change has been observed but the team is unsure whether it's noise or signal
  • Preparing a post-mortem after an incident that affected business metrics
  • A trend change happened weeks ago and needs retrospective investigation

Process

  1. Validate the change — confirm the metric changed beyond normal variance using a z-score or simple comparison to the rolling average. If the change is within ±1.5 standard deviations, document it as within normal range and close. Use scripts/drilldown_analyzer.py --validate.
  2. Establish a timeline — plot the metric over time to pinpoint when the change started. A sudden step change suggests a specific event; a gradual drift suggests a structural shift.
  3. Decompose the metric — break the metric into its constituent parts (e.g., revenue = volume × price × mix). Determine which component is driving the change before drilling into dimensions.
  4. Drill down systematically — compare the metric before vs. after the change across available dimensions (geography, platform, channel, product category, user segment). Sort by absolute contribution to identify the primary driver. Use scripts/drilldown_analyzer.py --drilldown. See references/rca_framework.md for the structured approach.
  5. Test hypotheses — generate explicit hypotheses (volume drop, mix shift, per-unit quality change, data issue) and accept or reject each with evidence. Correlate the timeline with known events from references/hypothesis_testing_guide.md.
  6. Write the root cause report — document the primary driver (quantified share of impact), supporting evidence, rejected hypotheses, and tiered recommendations (immediate / short-term / long-term). Use assets/rca_report_template.md.

Inputs the skill needs

  • Metric name and historical values (at least 30 days before the change)
  • Granular data with dimensional breakdowns (geography, platform, segment, etc.)
  • The date or date range when the change was noticed
  • A change log or incident log for the same period (product releases, campaigns, outages)
  • The business context: what decisions depend on this metric

Output

  • scripts/drilldown_analyzer.py — validates the change, computes dimensional drill-downs, and ranks contributors by impact
  • references/rca_framework.md — structured five-step RCA method with decision rules
  • references/hypothesis_testing_guide.md — checklist of common root causes and how to test each
  • assets/rca_report_template.md — report template: what changed, when, primary driver, supporting evidence, timeline, recommendations

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