cohort-analysis
Standard method for slicing bookings, pipeline, and retention cohorts for diagnostics.
npx skills add majiayu000/claude-skill-registry --skill cohort-analysis-gtmagents-gtm-agents --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Cohort Analysis Framework Skill ## When to Use - Comparing performance across acquisition channels, segments, or product lines. - Diagnosing conversion drop-offs within specific booking/vintage cohorts. - Stress-testing forecast assumptions with historical baseline behavior. ## Framework 1. **Cohort Definition** – choose cohort key (signup month, lead source, product tier, segment). 2. **Metric Stack** – select KPIs (coverage, win rate, ACV, NRR, payback) per cohort. 3. **Normalization** – adjust for seasonality, deal size mix, or currency. 4. **Visualization** – waterfall tables, heatmaps, or overlapping curves to highlight divergence. 5. **Narrative Layer** – annotate drivers, anomalies, and recommended actions. ## Templates - Cohort definition worksheet (keys, filters, inclusion/exclusion rules). - Standardized chart pack for leadership readouts. - Diagnostic checklist for follow-up analyses. ## Tips - Keep cohorts mutually exclusive to avoid double-counting. - Pair with `inspect-pipeline-levers` to link cohort insights to pipeline stages. - Rebaseline quarterly so assumptions stay current. ---
- When to Use
- Framework
- Templates
- Tips
What does the cohort-analysis skill do?
Standard method for slicing bookings, pipeline, and retention cohorts for diagnostics.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill cohort-analysis-gtmagents-gtm-agents --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.