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Automation
Skill

/cohort-analysis

Standard method for slicing bookings, pipeline, and retention cohorts

From plugin
gtm-agents
368200 skills200 agents199 commands
Install
$ npx -y skills add gtmagents/gtm-agents --skill cohort-analysis --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/cohort-analysis

Context preview

The summary Claude sees to decide when to auto-load this skill.

Standard method for slicing bookings, pipeline, and retention cohorts

SKILL.md

cohort-analysis.SKILL.md
name: cohort-analysis
description: Standard method for slicing bookings, pipeline, and retention cohorts
  for diagnostics.

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.

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