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Product analytics investigator

Skill sisodiabhumca/agent-skills/skills/product-analytics-investigator

Production-Ready Agent Skills : product analytics, growth experiments, CRM, research synthesis, postmortems, data contracts, SaaS spend, compliance, architecture maps, and LLM eval and many more.

Install
npx -y skills add sisodiabhumca/agent-skills --skill product-analytics-investigator

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Use when a PM, data PM, or analyst needs to investigate product metrics in Amplitude or Mixpanel — diagnosing drops in activation, retention, or funnel conversion, or attributing changes to releases, segments, or experiments. Pulls events, runs cohort/funnel analysis, and writes a root-cause memo.

SKILL.md

1.9 KB, 397 tokens by cl100k_base, as published. Nobody here has run it

Product Analytics Investigator

When to invoke

  • "Why did activation drop last week?"
  • "Investigate the checkout funnel for mobile users."
  • "Compare retention before/after the v4.2 release."

Inputs needed

  1. Metric or funnel under investigation (event names + ordering).
  2. Time window (default: last 14 days vs prior 14 days).
  3. Segments to slice by (platform, plan, country, cohort).
  4. Hypotheses (optional) — release dates, experiments, marketing pushes.

If any are missing, ask the user once before running.

Workflow

  1. Frame — restate the metric, window, baseline, and segments.
  2. Pull data — call investigate.py (Amplitude/Mixpanel API or warehouse).
  3. Decompose — break the change by segment, surface, and step.
  4. Correlate — overlay releases, experiments, incidents.
  5. Report — produce a memo: TL;DR, evidence, top 3 hypotheses ranked by likelihood, recommended next steps.

Output format

## TL;DR
<one sentence: what changed, by how much, who is affected>

## Evidence
- Funnel step deltas (table)
- Top contributing segments
- Timeline overlay with releases/experiments

## Hypotheses (ranked)
1. <hypothesis> — supporting evidence — confidence
2. ...

## Recommended next steps
- [ ] Owner — Action — ETA

Guardrails

  • Never claim causation from correlation alone. Surface confounders.
  • Always show absolute counts alongside percentages.
  • Flag low-sample segments (n < 100) explicitly.

Reference code

See investigate.py for a runnable funnel + cohort comparison against Amplitude or Mixpanel APIs (or a CSV export).

Keep looking

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