Feature Prioritizer (RICE)
Apply RICE scoring that surfaces honest numbers instead of advocacy — with calibrated estimates and the reasoning made explicit.
- 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.
$8.99
· or 45 creditsSecure checkout via Stripe
Included in download
- Debias feature requests by enforcing evidence-based confidence ceilings.
- Identify sensitivity flips where a minor estimate change shifts priority.
Sample input
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.
Sample output
- 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.
Apply RICE scoring that surfaces honest numbers instead of advocacy — with calibrated estimates and the reasoning made explicit.
$8.99
· or 45 creditsSecure checkout via Stripe
Included in download
- Debias feature requests by enforcing evidence-based confidence ceilings.
- Identify sensitivity flips where a minor estimate change shifts priority.
- Instant install
Sample input
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.
Sample output
- 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.
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.
Use Cases
- 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.
Known Limitations
- Requires external user data for Reach accuracy.
- Cannot automate engineering effort estimates.
- Best for strategic planning, not real-time task sorting.
How to Install
mkdir -p ~/.claude/skills && curl -sL https://www.agensi.io/api/install/feature-prioritizer-rice -o /tmp/feature-prioritizer-rice.zip && unzip -o /tmp/feature-prioritizer-rice.zip -d ~/.claude/skills && rm /tmp/feature-prioritizer-rice.zipFree skills install directly. Paid skills require purchase - use the download button above after buying.
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Compatible with SKILL.md-compatible agents skipping vendor-specific hooks.
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