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Product Management · Version 1.0.0 · Reviewed 2026-08-02

Feature Request Evidence Synthesizer

Make a product decision about request normalization and problem theme synthesis with evidence, explicit trade-offs, and a verification plan.

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

Combines feature requests, support tickets, sales notes, usage data, and interviews into a problem-based evidence view without equating request volume with priority.

₹149 one-time

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What this skill helps you do

  • Request normalization
  • Problem theme synthesis
  • Evidence strength assessment

How Feature Request Evidence Synthesizer works

You provide

Customer evidence, constraints, and the decision at stake

It inspects

Problem versus requested solution in request normalization

It decides

A problem theme synthesis decision ranking assumptions by risk

You verify

Success criteria and a reversal condition fixed up front

What it checks first

Feature Request Evidence Synthesizer combines feature requests, support tickets, sales notes, usage data, and interviews into a problem-based evidence view without equating request volume with priority. Use it when the work involves Request normalization, Problem theme synthesis, Evidence strength assessment.

  1. Whether the request describes a solution or the underlying problem and its frequency.
  2. The strength of evidence behind each assumption, and which assumption carries the most risk.
  3. Opportunity cost, since a roadmap decision is a decision not to do something else.
  4. Whether success criteria and a review date were defined before commitment.

Failure modes it recognizes

  • Request volume used as a proxy for impact, which favors the loudest segment.
  • A prioritization score presented as objective while its inputs are estimates.
  • An experiment readout interpreted without checking sample ratio or power.
  • Scope committed before the riskiest assumption has been tested.

Answers it will reject

  • Building the requested feature rather than solving the described problem.
  • Presenting a roadmap without the trade-off that made it necessary.
  • Declaring success from a metric that moved for an unrelated reason.

Decision rules it applies

  • Separate problem from proposed solution before evaluating anything.
  • Rank assumptions by risk and test the riskiest before committing scope.
  • State the success criteria and the reversal condition at decision time.

Evidence it asks for

  • Quantify frequency, severity, and affected segment for each problem.
  • Cite the specific evidence behind each assumption and label its strength.
  • Define the leading indicator that will show progress before the lagging metric moves.

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

  • Request normalization evidence map
  • Problem theme synthesis option and risk analysis
  • Evidence strength assessment 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

Synthesize these feature requests and tell me which underlying customer problems are strongly evidenced.

Expected output

Thirty-seven requests collapse into four problems. Export customization has high request volume but low observed workflow impact; approval visibility has fewer requests and stronger retention evidence...

Boundaries and compatibility

Ideal for

  • Request normalization: produce a decision or artifact grounded in supplied evidence.
  • Problem theme synthesis: produce a decision or artifact grounded in supplied evidence.
  • Evidence strength assessment: 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.