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feature-prioritization

feature-prioritization

github.com/assimovtProductivity71210 installsUpdated 36w ago
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About feature-prioritization

feature-prioritization is a product planning skill for teams ranking candidate features. It combines RICE scoring with a blocker-versus-enabler classification to make tradeoffs and set priorities for the next cycle explicit.

Key features

  1. 01RICE feature scoring
  2. 02Blocker and enabler classification
  3. 03Ranked feature comparisons
  4. 04Prioritization session steps
  5. 05Rules for close scores

Use cases

  1. 01Ranking features for a product cycle
  2. 02Comparing adoption barriers with engagement improvements
  3. 03Reassessing priorities with current evidence

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/assimovt/productskills --skill feature-prioritization

After installing, type/feature-prioritizationin Claude Code to confirm it loaded.

Details

From the project README

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.

Enablers vs Blockers (Linear)

After RICE scoring, classify each feature:

  • Blocker: Removes a barrier to adoption or retention. Users are churning, stuck, or can't even start because this is missing. Examples: missing SSO for enterprise deals, broken mobile experience, no data export.
  • Enabler: Delights existing users or deepens engagement. Users are already successful but this makes them more so. Examples: keyboard shortcuts, advanced filters, integrations.

Rule: Prioritize blockers over enablers when growing. Removing friction > adding delight when you're trying to grow. Flip this when retention is strong but engagement is flat.

Running a Prioritization Session

  1. List all candidates with a one-sentence description
  2. Score each on R, I, C, E independently — don't anchor on each other
  3. Calculate RICE scores and rank
  4. Tag each as Blocker or Enabler
  5. Check: are any Blockers ranked below Enablers? Justify or re-rank.
  6. Top 3-5 items = your next cycle

Guidelines

  • ALWAYS show the math. A prioritization without visible scores is just an opinion list.
  • NEVER let Confidence = 100% without hard data (analytics, A/B test results, or customer commit).
  • ALWAYS separate enablers from blockers before finalizing the rank.
  • NEVER prioritize more than one cycle at a time. The world changes. Re-score next cycle.
  • CRITICAL: If two items are within 20% RICE score of each other, they're effectively tied. Use the blocker/enabler lens to break ties, or pick the one with higher Confidence.

Built on Intercom's RICE framework and the Linear Method. Skills from productskills.

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