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cohort-analysis

Skill6 starsChanged 3 months agoApache-2.0View on GitHub

Standard method for slicing bookings, pipeline, and retention cohorts

20 other projects use a version of this.

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Projects with it also use positioning-ideas, gtm-motions and 8 more.

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  • skytiger6724Skill

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  • 45ckSkill

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  • +3 more from About-Intelligence
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The file

We split this skill into the rules it gives an agent

1 rule. 0 are common, 1 is distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 13 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Keep cohorts mutually exclusive to avoid double-counting.

    line 27

“Common” is measured: kept by at least 6 in 10 of the 13 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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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