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opportunity-sizing

Estimate affected-user ranges, per-user impact, and RICE scores for product opportunities.

70|34|Updated Apr 7, 2026
Search Tags:#prioritization#retention#rice#opportunity#sizing#opportunity-sizing#impact-estimation
One-click install
npx skills add https://github.com/Productfculty-aipm/PM-Copilot-by-Product-Faculty --skill opportunity-sizing-productfculty-aipm
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: opportunity-sizing
Source: https://github.com/Productfculty-aipm/PM-Copilot-by-Product-Faculty/tree/main/skills/opportunity-sizing
Command: npx skills add https://github.com/Productfculty-aipm/PM-Copilot-by-Product-Faculty --skill opportunity-sizing-productfculty-aipm

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps product teams quantify the realistic user and business impact of solving a specific product problem within an existing product context, avoiding oversized TAM estimates and focusing on actionable feature-level value.

Core Features & Use Cases

  • Context-aware sizing: Loads product memory and analytics baselines to ground estimates in real metrics and roadmap context.
  • Population & impact modelling: Walks through total base, segment, frequency, and severity filters to produce low/mid/high affected-user ranges and per-user impact estimates.
  • Prioritization output: Calculates a RICE score (Reach, Impact, Confidence, Effort) and surfaces key assumptions and recommended next steps for prioritization decisions.
  • Use Case: Prioritize whether to build a new onboarding flow by estimating how many users are blocked today, the retention uplift if fixed, and whether the resulting RICE justifies investment.

Quick Start

Use the opportunity-sizing skill to estimate affected users, translate that to retention/engagement/revenue impact, and produce a RICE score using your product memory and analytics baseline.

Frequently Asked Questions about opportunity-sizing

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I estimate the user impact of a new product feature?▼

Opportunity sizing calculates the realistic user and business impact of a product feature by filtering your total user base through segment, frequency, and severity models to produce low, mid, and high affected-user ranges.

How do I calculate a RICE score for feature prioritization?▼

RICE scoring for prioritization requires estimating Reach, Impact, Confidence, and Effort. This Skill calculates your RICE score by grounding those variables in your analytics baselines and product memory to produce a confident ROI trade-off.

What is the best way to size product opportunities without overestimating the TAM?▼

Feature-level opportunity sizing avoids oversized TAM estimates by applying severity and frequency filters to your analytics baseline, translating blocked users into actionable per-user retention, engagement, and revenue impact estimates.

Can I use analytics baselines to estimate retention uplift for a roadmap decision?▼

Yes, you can estimate retention uplift by combining your analytics baselines with product memory. The Skill models per-user impact to determine if the resulting engagement improvements justify the development investment.

What data do I need to estimate the ROI of fixing a blocked onboarding flow?▼

Estimating ROI for an onboarding flow requires product memory and analytics baselines to determine how many users are blocked today, the expected retention uplift if fixed, and the RICE score to justify the investment.

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