rice
Skill111 starsChanged 10 months agoMITView on GitHub
RICE prioritization scoring initiatives by Reach, Impact, Confidence, and Effort. Use for feature prioritization, roadmap planning, or when comparing initiatives objectively.
Files
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
- [Highest score] — RICE: X
- [Second] — RICE: X
- [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
In this repository22 other files it gives its agents.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
Reviews can't be read right now.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

