feature-prioritization
Installation
SKILL.md
Prioritize with math, not opinions. RICE scoring forces explicit tradeoffs. The enabler/blocker lens from Linear ensures you're not just building fun things while adoption barriers remain.
RICE Scoring
Score every candidate feature on four dimensions:
- Reach: How many users/accounts will this affect in a set time period? Use real numbers from analytics, not gut feel. "500 users/quarter" not "a lot."
- Impact: How much will this move the target metric per user? Score 0.25 (minimal), 0.5 (low), 1 (medium), 2 (high), 3 (massive). Be honest — most features are a 1.
- Confidence: How sure are you about Reach and Impact? 100% = hard data. 80% = strong evidence. 50% = gut feel. NEVER score 100% without quantitative data.
- Effort: Person-weeks of work. Include design, engineering, QA, and any cross-team coordination. Round up.
RICE = (Reach x Impact x Confidence) / Effort
Example: SSO — Reach: 500 users/qtr, Impact: 2 (high — unlocks enterprise deals), Confidence: 80%, Effort: 4 person-weeks. RICE = (500 x 2 x 0.8) / 4 = 200. Tag: Blocker.
Rank by score. The math won't be perfect, but it forces you to justify each dimension.