neurofoo

rice

35
5
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npx skills add neurofoo/agent-skills --skill "rice"

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# Description

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

# SKILL.md


name: rice
description: RICE prioritization scoring initiatives by Reach, Impact, Confidence, and Effort. Use for feature prioritization, roadmap planning, or when comparing initiatives objectively.
user-invocable: true


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

# Supported AI Coding Agents

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