Feature Prioritizer (RICE)

Name: Feature Prioritizer (RICE)
Price: 8.99 USD
Availability: InStock
Author: Agensi

Apply RICE scoring that surfaces honest numbers instead of advocacy — with calibrated estimates and the reasoning made explicit.

$8.99

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See it in action

You say

Apply RICE to our new product features: In-app chat (500 users, high impact, 2 weeks) and Dark Mode (all users, low impact, 2 days). Identify the weakest assumptions.

Your agent does

In-app Chat: Score 45. Critical assumption: 50% increase in retention (Confidence ceiling 50% - anecdote only).
Dark Mode: Score 120. Data-backed Reach (100% of users) provides higher confidence (90%). Recommendation: Verify retention impact for Chat before committing.

What you get

Debias feature requests by enforcing evidence-based confidence ceilings.Identify sensitivity flips where a minor estimate change shifts priority.Generate critical questions to challenge advocacy in roadmap meetings.Standardize impact definitions across different product teams.

About this skill

RICE fails in practice for one reason: the numbers are advocacy. Reach is inflated because the team is excited, effort is underestimated because no one wants to kill their feature, and confidence is whatever makes the score win. This skill applies RICE in a way that surfaces the biases: it asks for the reasoning behind each estimate rather than just the number, flags when estimates are inconsistent with historical data, separates the team's confidence from the model's implied confidence, and produces a prioritized stack with the key assumptions that would flip each ranking. It also generates the questions to ask in the prioritization meeting — the ones that make advocacy visible before it's baked into the roadmap. Bring your feature list and context; get back a scored stack you can defend and the list of assumptions worth stress-testing.

business-operations

Changelog

v1.1LatestJun 26, 2026
Rewrote the skill body with real, expert content. Every section now carries concrete RICE-with-calibration method (scale calibration first, Reach from real measured data, Impact anchored to reference cases, Confidence ceilings by evidence type, Effort from the builders, scoring-session facilitation to cut bias, sensitivity & flip analysis, handling strategic overrides explicitly, communicating the ranking, and a re-scoring cadence). Replaced the generic placeholder sections.
v1.0Jun 11, 2026
Initial release