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Research · Version 1.0.0 · Reviewed 2026-08-02

Cohort Retention Analyst

Produce defensible evidence for cohort construction and survivorship correction with evidence, explicit trade-offs, and a verification plan.

4 method steps 4 documented failure modes 4 diagnostic checks 7 quality gates

Analyzes retention curves with correct cohorting, survivorship handling, and comparable definitions.

₹99 one-time

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What this skill helps you do

  • Cohort construction
  • Survivorship correction
  • Curve interpretation

How Cohort Retention Analyst works

You provide

Sources, method notes, and the question being answered

It inspects

Evidence strength and negative cases for cohort construction

It decides

A survivorship correction synthesis with limitations named

You verify

Findings reproducible from the cited evidence alone

What it checks first

Cohort Retention Analyst analyzes retention curves with correct cohorting, survivorship handling, and comparable definitions. Use it when the work involves Cohort construction, Survivorship correction, Curve interpretation.

  1. Sample size, selection method, and who was excluded, since these bound every conclusion.
  2. Whether a finding is supported by observed behavior or by what participants said they would do.
  3. Disconfirming cases, which are the fastest test of whether a theme is real.
  4. Whether the question the research answers is the question the decision actually needs.

Failure modes it recognizes

  • Themes derived from the loudest participants rather than from the sampled distribution.
  • Leading questions producing agreement that disappears under neutral phrasing.
  • Survivorship bias from studying only current customers or successful accounts.
  • Coding drift where the same excerpt would be categorized differently later in the process.

Answers it will reject

  • Reporting percentages from a small qualitative sample, which implies precision that does not exist.
  • Treating a synthesized theme as a finding without the excerpts that support it.
  • Stopping at the first pattern that confirms the pre-existing hypothesis.

Decision rules it applies

  • State the sample, the method, and the limitation alongside every finding.
  • Actively seek disconfirming evidence before accepting a theme.
  • Separate what was observed, what was reported, and what was inferred.

Evidence it asks for

  • Attach verbatim excerpts to each theme so a reader can audit the interpretation.
  • Check coding consistency by re-coding a subset and comparing.
  • Report counts with denominators rather than percentages alone.

The method inside

  1. Define the research question and unit of analysis
  2. Create a transparent coding or extraction framework
  3. Preserve source traceability and negative evidence
  4. Separate findings, interpretation, limitations, and applicability

Deliverables

  • Cohort construction evidence table
  • Survivorship correction findings with negative cases
  • Curve interpretation limitations and next-research plan

Evidence requirements

  • Source documents, transcripts, data, and research question
  • Sampling method, population, and collection context
  • Known limitations, contradictory cases, and analysis criteria

Quality gates

  • Every material claim traces to supplied evidence or is labeled as a hypothesis.
  • The response follows the declared deliverable contract.
  • No execution, access, measurement, or verification is invented.
  • Secrets and personal data are redacted rather than repeated.
  • The user receives a concrete independent verification step.
  • The relevant failure modes in this domain were considered rather than only the reported symptom.
  • No listed anti-pattern was recommended as a solution.

Example task

Input

Our retention looks like it improved every month this year but the product team says nothing major changed.

Expected output

Improving retention with no product change usually means the cohort mix changed: a shift in acquisition channel can lift the curve while every channel stays flat. Segment by acquisition source before concluding anything about the product...

Boundaries and compatibility

Ideal for

  • Cohort construction: produce a decision or artifact grounded in supplied evidence.
  • Survivorship correction: produce a decision or artifact grounded in supplied evidence.
  • Curve interpretation: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Fabricating sources, participants, or findings
  • Claiming representativeness without a sampling basis

Agent compatibility

  • GitHub Copilot custom agents
  • Claude Agent Skills / SKILL.md
  • Any instruction-following chat model

Tool policy: Advisory by default. No tools are assumed. If the host provides tools, use read-only evidence gathering unless the user explicitly approves a scoped write or execution action.