smithery.ai

rice

RICE prioritization scoring initiatives by Reach, Impact, Confidence, and Effort. Use for feature prioritization, roadmap planning, or when comparing initiatives objectively.

First seen Mar 24, 2026

Installation

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Package contents

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  • skill md SKILL.md 2,395 B
  • docs SUMMARY.md 186 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 1 installs

SKILL.md

RICE Prioritization Scoring

Score and rank initiatives using Reach, Impact, Confidence, and Effort to make prioritization decisions more objective.

Instructions

For each initiative, estimate the four RICE factors, calculate the score, and rank them. Be explicit about assumptions behind each estimate.

Output Format

Context What are we prioritizing? What's the time horizon for Reach?

Factor Definitions

  • Reach: [Define for this context, e.g., "users affected per quarter"]
  • Impact: [Define for this context, e.g., "effect on conversion rate"]
  • Effort: [Define unit, e.g., "engineer-weeks"]

Scoring Table

Initiative Reach Impact Confidence Effort RICE Score
[Name A] [#] [0.25-3] [%] [#] [calculated]
[Name B] [#] [0.25-3] [%] [#] [calculated]
[Name C] [#] [0.25-3] [%] [#] [calculated]

Ranked Results

  1. [Highest score] — RICE: X
  2. [Second] — RICE: X
  3. [Third] — RICE: X

Detailed Breakdown

For each initiative:

[Initiative Name]

  • Reach: [X] — [Assumption: how did you estimate this?]
  • Impact: [X] — [Reasoning for impact level]
  • Confidence: [X%] — [What would increase confidence?]
  • Effort: [X] — [What's included in this estimate?]
  • RICE Score: (R × I × C) / E = [score]

Sensitivity Analysis Which scores would change significantly if assumptions are wrong?

Recommendation Based on the scores and analysis:

[What to prioritize and why, including any caveats]

What RICE Doesn't Capture

  • Strategic alignment
  • Dependencies
  • Team capability gaps
  • Technical risk

Scoring Guide

Impact Scale

Score Meaning
3 Massive — core value prop
2 High — significant improvement
1 Medium — noticeable improvement
0.5 Low — minor enhancement
0.25 Minimal — nice to have

Confidence Scale

Score Meaning
100% High — have data
80% Medium — reasonable estimate
50% Low — mostly guessing

$ARGUMENTS