agentsclimarketplace

Drilldown analyzer

Skill wachawo/claude-skills/skills/drilldown-analyzer

Claude Skills for software engineers and developers

Install
npx -y skills add wachawo/claude-skills --skill drilldown-analyzer

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Systematic investigation of metric changes and anomalies. Use when a metric unexpectedly changes, investigating business metric drops, explaining performance variations, or drilling into aggregated metric drivers.

SKILL.md

3.0 KB, as published. Nobody here has run it

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

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.