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Get Started Free →Applies prioritization frameworks including RICE, ICE, Value vs Effort, and Kano model. Use when prioritizing features, managing backlog, making tradeoffs, or saying no gracefully. Based on Shreyas Doshi and Intercom frameworks.
.claude/skills/bilal140202-prioritization-craft/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 46% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 17% | 0% |
| case-07 | ✓→✓ | ＝ Same ✓ | 0% | 0% |
| case-01 | ✓→✓ | ＝ Same ✓ | 7% | 0% |
| case-03 | ✓→✓ | ＝ Same ✓ | 41% | 0% |
Claude uses this skill when:
Formula:
RICE Score = (Reach × Impact × Confidence) / Effort
Reach: How many users affected (per quarter)
Impact: How much impact (0.25, 0.5, 1, 2, 3)
Confidence: How confident (%, use 80% if unsure)
Effort: How much work (person-months)Example:
markdownFeature: In-app notifications Reach: 1000 users/quarter Impact: 2 (high impact) Confidence: 80% Effort: 1 person-month RICE = (1000 × 2 × 0.8) / 1 = 1600
High Value, Low Effort → DO FIRST (quick wins)
High Value, High Effort → DO NEXT (strategic)
Low Value, Low Effort → DO LATER (fill-ins)
Low Value, High Effort → DON'T DO (money pit)markdown# Feature Prioritization ## Feature 1: [Name] - Reach: [X users/quarter] - Impact: [0.25/0.5/1/2/3] - Confidence: [X]% - Effort: [X person-months] - **RICE Score:** [calculate] ## Feature 2: [Name] - Reach: [X users/quarter] - Impact: [0.25/0.5/1/2/3] - Confidence: [X]% - Effort: [X person-months] - **RICE Score:** [calculate] ## Priority Order 1. [Feature with highest RICE] 2. [Feature with second RICE] 3. [Feature with third RICE]
Score Each Feature:
Prioritize:
Shreyas Doshi: > "Saying no is the most underrated PM skill."
Intercom: > "Without prioritization, everything feels urgent and nothing gets done well."
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-07 | pass→pass | 8,059 | 5,133 | -36% | 1 | 1 | 0% | 1,471 | 1,470 | -0% | 0 | 0 | — |
case-01 | pass→pass | 4,915 | 2,433 | -50% | 1 | 1 | 0% | 1,016 | 1,092 | +7% | 0 | 0 | — |
case-02 | fail→pass | 4,094 | 3,063 | -25% | 1 | 1 | 0% | 848 | 1,236 | +46% | 0 | 0 | — |
case-03 | pass→pass | 4,447 | 3,705 | -17% | 1 | 1 | 0% | 972 | 1,369 | +41% | 0 | 0 | — |
case-04 | pass→pass | 2,811 | 1,693 | -40% | 1 | 1 | 0% | 583 | 925 | +59% | 0 | 0 | — |
case-05 | fail→pass | 9,078 | 7,538 | -17% | 1 | 1 | 0% | 1,604 | 1,883 | +17% | 0 | 0 | — |
case-06 | pass→pass | 8,137 | 3,337 | -59% | 1 | 1 | 0% | 1,292 | 1,241 | -4% | 0 | 0 | — |
case-08 | pass→pass | 2,074 | 1,819 | -12% | 1 | 1 | 0% | 341 | 951 | +179% | 0 | 0 | — |
case-09 | pass→pass | 1,843 | 2,475 | +34% | 1 | 1 | 0% | 344 | 1,089 | +217% | 0 | 0 | — |
case-10 | pass→pass | 3,661 | 2,982 | -19% | 1 | 1 | 0% | 844 | 1,366 | +62% | 0 | 0 | — |
case-11 | pass→pass | 4,823 | 2,655 | -45% | 1 | 1 | 0% | 984 | 1,195 | +21% | 0 | 0 | — |
case-12 | pass→pass | 4,843 | 2,294 | -53% | 1 | 1 | 0% | 840 | 990 | +18% | 0 | 0 | — |
case-13 | pass→pass | 5,105 | 2,577 | -50% | 1 | 1 | 0% | 939 | 1,025 | +9% | 0 | 0 | — |
case-14 | pass→pass | 4,210 | 2,394 | -43% | 1 | 1 | 0% | 683 | 1,026 | +50% | 0 | 0 | — |
case-15 | pass→pass | 5,553 | 3,528 | -36% | 1 | 1 | 0% | 1,261 | 1,388 | +10% | 0 | 0 | — |
case-16 | pass→pass | 3,324 | 2,940 | -12% | 1 | 1 | 0% | 527 | 1,081 | +105% | 0 | 0 | — |
case-17 | pass→pass | 2,491 | 2,145 | -14% | 1 | 1 | 0% | 367 | 959 | +161% | 0 | 0 | — |
case-18 | pass→pass | 12,371 | 7,371 | -40% | 1 | 1 | 0% | 1,911 | 1,743 | -9% | 0 | 0 | — |
case-19 | pass→pass | 7,716 | 3,223 | -58% | 1 | 1 | 0% | 1,038 | 1,257 | +21% | 0 | 0 | — |
case-20 | pass→pass | 11,188 | 8,014 | -28% | 1 | 1 | 0% | 2,199 | 2,075 | -6% | 0 | 0 | — |
case-21 | pass→pass | 8,911 | 7,182 | -19% | 1 | 1 | 0% | 1,459 | 1,719 | +18% | 0 | 0 | — |
case-22 | pass→pass | 12,592 | 8,355 | -34% | 1 | 1 | 0% | 1,997 | 1,917 | -4% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +9 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.