Prioritization Framework Facilitator

Pro v1.0.0 1 view

Guide product teams through objective, evidence-based feature prioritization using RICE, ICE, or Value/Effort frameworks with built-in bias detection and score calibration.

What You Get

Transform subjective feature debates into defensible, data-backed prioritization decisions with automated bias detection, calibrated scoring, and stakeholder-ready documentation.

The Problem

Product teams struggle with objective feature prioritization due to scoring bias, confidence overestimation, effort underestimation, and political pressure. Score inflation ('everything is high priority') and inconsistent scoring lead to poor roadmap decisions, stakeholder misalignment, and unpredictable delivery. Product managers need a structured process that challenges inflated estimates, detects bias patterns, and produces defensible prioritization decisions backed by evidence.

The Solution

This skill guides product teams through a structured prioritization process that recommends the optimal framework (RICE, ICE, Value/Effort, or Weighted), facilitates evidence-based scoring with probing questions, detects common bias patterns (inflation, overconfidence, inconsistencies), calibrates scores using relative comparison and historical data, calculates framework-specific priority scores, and generates comprehensive prioritization documentation with evidence, rationale, and stakeholder communication. Unlike manual prioritization, this skill actively catches scoring bias and ensures consistency across features.

How It Works

  1. 1 Assess team context and recommend optimal prioritization framework based on stage, data availability, and planning horizon
  2. 2 Guide evidence-based scoring for each feature with probing questions that challenge vague estimates and push for specific data
  3. 3 Detect scoring bias patterns including score inflation, confidence overestimation, effort underestimation, and internal inconsistencies
  4. 4 Challenge and calibrate scores using scale anchors, force ranking, relative comparison, and historical data
  5. 5 Calculate final priority scores using framework-specific formulas (RICE, ICE, Value/Effort, or Weighted)
  6. 6 Generate prioritization document with ranked features, tier assignments, evidence summary, and stakeholder communication

What You'll Need

  • List of 5-30 features to prioritize with brief descriptions
  • Team context: size, engineering resources, customer base
  • Available data: usage metrics, customer requests, support tickets (if available)
  • Planning timeframe: sprint, quarter, or year
  • Framework preference (optional - skill will recommend if unsure)

Get This Skill

Requires Pro subscription ($9/month)

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Examples

B2B SaaS RICE Prioritization with Bias Detection

Demonstrates RICE framework for a growth-stage B2B SaaS with 800 customers, scoring 12 features with quantified evidence, detecting bias patterns, and generating stakeholder-ready documentation with capacity planning.

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Early-Stage Startup Bias Calibration

Demonstrates framework recommendation for an early-stage startup with minimal data, detecting severe score inflation (80% high-priority), and providing a complete calibration framework with workshop agenda and force ranking techniques.

View example

Small Team ICE Prioritization

Demonstrates ICE framework selection for a small team with limited customer data, scoring 6 features with evidence-based calibration and generating a tiered roadmap with stakeholder communication.

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