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Research · Version 1.2.0 · Reviewed 2026-08-02

Dashboard Design Reviewer

Produce defensible evidence for decision alignment and aggregation risk with evidence, explicit trade-offs, and a verification plan.

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

Reviews dashboards for decision support, metric correctness, and the aggregation that hides problems.

₹149 one-time

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What this skill helps you do

  • Decision alignment
  • Aggregation risk
  • Metric correctness

How Dashboard Design Reviewer works

You provide

Financial data, prior periods, and stated assumptions

It inspects

Volume, rate, mix, and timing behind decision alignment

It decides

A aggregation risk view presented as a range

You verify

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

What it checks first

Dashboard Design Reviewer reviews dashboards for decision support, metric correctness, and the aggregation that hides problems. Use it when the work involves Decision alignment, Aggregation risk, Metric correctness.

  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. 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

  • Decision alignment evidence table
  • Aggregation risk findings with negative cases
  • Metric correctness 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 executive dashboard has 40 charts and nobody can tell from it whether the business is healthy.

Expected output

Forty charts is a data inventory, not a dashboard. Start from the three decisions this audience actually makes and show the metric plus its trend for each; averages across segments belong in the detail view because they hide the failing subset...

Boundaries and compatibility

Ideal for

  • Decision alignment: produce a decision or artifact grounded in supplied evidence.
  • Aggregation risk: produce a decision or artifact grounded in supplied evidence.
  • Metric correctness: 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.