SkillVaultskills Browse all 500 skills

Product Management · Version 1.1.0 · Reviewed 2026-08-02

User Onboarding Flow Reviewer

Make a product decision about time to value and friction placement with evidence, explicit trade-offs, and a verification plan.

4 method steps 5 documented failure modes 5 diagnostic checks 7 quality gates

Reviews activation flows for time-to-value, friction placement, and drop-off that is actually recoverable.

₹149 one-time

Get this skill archive

What this skill helps you do

  • Time to value
  • Friction placement
  • Drop-off recovery

How User Onboarding Flow Reviewer works

You provide

Financial data, prior periods, and stated assumptions

It inspects

Volume, rate, mix, and timing behind time to value

It decides

A friction placement view presented as a range

You verify

Bottom-up and top-down builds reconciled, gap explained

What it checks first

User Onboarding Flow Reviewer reviews activation flows for time-to-value, friction placement, and drop-off that is actually recoverable. Use it when the work involves Time to value, Friction placement, Drop-off recovery.

  1. Whether a variance is driven by volume, rate, mix, or timing, because each implies a different response.
  2. Which assumptions carry the most sensitivity, since a forecast is only as good as its two or three dominant drivers.
  3. Whether the number is an accrual or a cash figure, which changes the conclusion entirely.
  4. One-time versus recurring components inside a trend.
  5. Whether the comparison baseline is like-for-like across periods and entity changes.

Failure modes it recognizes

  • A forecast that compounds a growth rate without a capacity or market constraint, producing an impossible outturn.
  • Mix shift making a blended margin look stable while both underlying segments deteriorate.
  • Timing differences presented as performance changes, reversing in the following period.
  • Double counting between a bottom-up and a top-down build.
  • Currency translation effects reported as operational performance.

Answers it will reject

  • Presenting a single-point forecast without a range, which conceals the uncertainty being decided on.
  • Explaining variance by restating the number rather than attributing it to a driver.
  • Changing the definition of a metric between periods without disclosure.
  • Building precision into a model whose inputs are estimates, implying false confidence.

Decision rules it applies

  • Decompose every variance into volume, rate, mix, and timing before explaining it.
  • Show a range with the assumptions that produce each bound.
  • Label each assumption as observed, derived, or estimated, and name its owner.
  • Reconcile bottom-up and top-down builds and explain the gap rather than averaging it.

Evidence it asks for

  • Build a bridge from prior period to current with each driver quantified and summing to the total.
  • Run sensitivity on the top three assumptions and report the resulting range.
  • State the review trigger: which observation would invalidate the forecast.

The method inside

  1. Separate the customer problem from requested solutions
  2. Inventory assumptions and strength of evidence
  3. Compare options using impact, confidence, risk, effort, and reversibility
  4. Define success, guardrails, and the decision after new evidence

Deliverables

  • Time to value evidence map
  • Friction placement option and risk analysis
  • Drop-off recovery decision memo

Evidence requirements

  • Customer research, usage, support, and commercial evidence
  • Strategy, constraints, dependencies, and opportunity cost
  • Experiment design, roadmap options, or requirements artifact

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 onboarding has seven steps and 60 percent of users never finish. Which steps should we remove?

Expected output

Removing steps blindly can remove the ones that create value. Identify the step after which retention diverges, protect that one, and defer everything that can be collected later; friction before value is the expensive kind...

Boundaries and compatibility

Ideal for

  • Time to value: produce a decision or artifact grounded in supplied evidence.
  • Friction placement: produce a decision or artifact grounded in supplied evidence.
  • Drop-off recovery: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Using request volume as a substitute for impact
  • Presenting a prioritization score as objective truth

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.