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Get Started Free →Comprehensive prioritization framework expert covering 9 methods with scoring tools and decision guidance for product managers.
.claude/skills/borghei-prioritization-frameworks/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 66% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 21% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 32% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 92% | 0% |
| case-18 | ✗→✓ | ▲ Improved | 82% | 0% |
A comprehensive reference to 9 prioritization frameworks with automated scoring, ranking, and guidance on which framework to use in which situation. The core principle: prioritize problems (opportunities), not features. Features are solutions to problems. If you prioritize features directly, you skip the step of understanding whether the problem is worth solving.
references/frameworks-catalog.md).prioritization_scorer.py ranks items for RICE, ICE, Opportunity, MoSCoW, and Weighted Decision Matrix.Before scoring, confirm these inputs. If any is unknown or vague, ASK — do not assume:
Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
| Tool | Purpose | Command | |------|---------|---------| | prioritization_scorer.py | Score and rank items | python scripts/prioritization_scorer.py --input items.json --framework rice | | prioritization_scorer.py | Demo with sample data | python scripts/prioritization_scorer.py --demo --framework rice |
Supported frameworks: rice, ice, opportunity, moscow, weighted. See references/tool-and-troubleshooting.md for input JSON schemas and flags.
Load the reference that matches the task — keep this file lean and pull detail on demand:
prioritization_scorer.py flags, per-framework input JSON schemas, troubleshooting table, and success criteria. Read when running the tool or diagnosing scoring problems.In Scope:
Out of Scope:
senior-pm/ skill for SAFe portfolio prioritization)product-team/ skills)senior-pm/ skill)Important Caveats:
| Integration | Direction | Description | |------------|-----------|-------------| | execution/outcome-roadmap/ | Feeds into | Prioritized items inform Now/Next/Later horizon placement | | execution/create-prd/ | Feeds into | Top-priority items become PRD candidates with P0/P1/P2 feature labels | | execution/brainstorm-okrs/ | Complements | Prioritized initiatives inform which OKR theme to focus on this quarter | | discovery/identify-assumptions/ | Receives from | Assumption risk scores inform item confidence ratings in RICE/ICE | | scrum-master/ | Feeds into | Prioritized backlog items feed sprint planning commitment decisions | | senior-pm/ | Receives from | Portfolio-level WSJF or strategic priorities constrain team-level prioritization |
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 12,242 | 12,459 | +2% | 1 | 1 | 0% | 1,970 | 3,271 | +66% | 0 | 0 | — |
case-02 | fail→fail | 7,196 | 7,443 | +3% | 1 | 1 | 0% | 1,623 | 2,888 | +78% | 0 | 0 | — |
case-03 | fail→fail | 14,303 | 18,464 | +29% | 1 | 1 | 0% | 2,320 | 4,593 | +98% | 0 | 0 | — |
case-04 | fail→fail | 22,816 | 30,342 | +33% | 1 | 1 | 0% | 4,800 | 7,564 | +58% | 0 | 0 | — |
case-05 | fail→pass | 18,716 | 15,634 | -16% | 1 | 1 | 0% | 3,205 | 3,869 | +21% | 0 | 0 | — |
case-06 | fail→fail | 27,906 | 19,707 | -29% | 1 | 1 | 0% | 4,186 | 4,522 | +8% | 0 | 0 | — |
case-07 | fail→pass | 9,047 | 3,605 | -60% | 1 | 1 | 0% | 1,517 | 1,997 | +32% | 0 | 0 | — |
case-08 | pass→pass | 8,487 | 1,524 | -82% | 1 | 1 | 0% | 1,274 | 1,595 | +25% | 0 | 0 | — |
case-15 | fail→fail | 8,221 | 6,554 | -20% | 1 | 1 | 0% | 1,369 | 2,342 | +71% | 0 | 0 | — |
case-09 | pass→pass | 12,960 | 12,652 | -2% | 1 | 1 | 0% | 1,932 | 3,273 | +69% | 0 | 0 | — |
case-10 | fail→pass | 10,649 | 11,075 | +4% | 1 | 1 | 0% | 1,592 | 3,058 | +92% | 0 | 0 | — |
case-11 | pass→pass | 4,909 | 5,706 | +16% | 1 | 1 | 0% | 743 | 2,165 | +191% | 0 | 0 | — |
case-12 | pass→pass | 5,072 | 8,204 | +62% | 1 | 1 | 0% | 871 | 2,646 | +204% | 0 | 0 | — |
case-13 | pass→pass | 4,502 | 6,409 | +42% | 1 | 1 | 0% | 647 | 2,377 | +267% | 0 | 0 | — |
case-14 | pass→pass | 12,715 | 13,133 | +3% | 1 | 1 | 0% | 2,088 | 3,745 | +79% | 0 | 0 | — |
case-16 | pass→pass | 11,363 | 10,022 | -12% | 1 | 1 | 0% | 1,761 | 2,881 | +64% | 0 | 0 | — |
case-17 | pass→pass | 12,956 | 6,283 | -52% | 1 | 1 | 0% | 1,816 | 2,313 | +27% | 0 | 0 | — |
case-18 | fail→pass | 7,226 | 3,527 | -51% | 1 | 1 | 0% | 1,044 | 1,899 | +82% | 0 | 0 | — |
case-19 | fail→pass | 9,514 | 2,947 | -69% | 1 | 1 | 0% | 1,683 | 1,790 | +6% | 0 | 0 | — |
case-20 | fail→pass | 11,119 | 2,623 | -76% | 1 | 1 | 0% | 1,770 | 1,734 | -2% | 0 | 0 | — |
case-21 | pass→pass | 17,894 | 16,128 | -10% | 1 | 1 | 0% | 2,529 | 3,743 | +48% | 0 | 0 | — |
case-22 | fail→pass | 10,005 | 9,251 | -8% | 1 | 1 | 0% | 1,504 | 2,694 | +79% | 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 +36 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.