Rice Scoring
@webframp/rice-scoringv2026.06.15.1
01README
RICE scoring methodology as an agent-guided framework. Accepts items, conducts structured interviews to derive Reach, Impact, Confidence, and Effort values, produces ranked versioned scorecards.
Configurable scales via globalArguments allow teams to anchor scoring in their own context.
02Models
@webframp/rice-scoringv2026.06.05.1rice-scoring/mod.ts
Global Arguments
| Argument | Type | Description |
|---|---|---|
| reachDefinition | string | What 'reach' means for this team — e.g. users per quarter, requests per month, teams affected |
| reachScale | string | Anchor points for reach scoring so values are consistent across scorers |
| impactScale | string | Impact multiplier scale — how much this moves the needle per user reached |
| effortUnit | string | Unit for effort estimation — person-days, person-weeks, story points, etc. |
| confidenceGuidance | string | Guide for setting confidence — what evidence corresponds to each level |
| scoringContext | string | Optional team/product context to anchor relative comparisons |
fn score(items: array)
Score items using the RICE methodology. This method is agent-guided:
the calling agent conducts a structured interview with the user to derive
values for each dimension before invoking this method with final numbers.
AGENT GUIDANCE FOR CONDUCTING THE INTERVIEW:
1. Read globalArgs to understand the team's configured scales:
- reachDefinition: what "reach" means (users/quarter, requests/month, etc.)
- reachScale: anchor points for numeric reach values
- impactScale: what each impact mul
| Argument | Type | Description |
|---|---|---|
| items | array | Items to score with their RICE dimension values |
fn rank()
Read the latest scored items and display them ranked by RICE score descending.
Resources
scores(infinite)— Versioned RICE scorecards with ranked items
03Previous Versions
2026.06.05.1Jun 5, 2026
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