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Get Started Free →Generate professional Product Requirement Documents (PRD) from multiple sources — Figma designs, briefs, user research, existing code. Includes risk analysis, RICE prioritization, Gherkin stories and launch plan. Activate when user mentions "PRD", "product requirements", "requirement document", "product spec", "figma to PRD", "generate PRD".
.claude/skills/bilal140202-doncheli-prd/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 18% | 0% |
| case-04 | ✗→✓ | ▲ Improved | -3% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 0% | 0% |
| case-10 | ✗→✓ | ▲ Improved | -31% | 0% |
| case-11 | ✗→✓ | ▲ Improved | -31% | 0% |
markdown# PRD: [Product/Feature Name] ## Metadata | Field | Value | |-------|-------| | Version | 1.0 | | Author | [name] | | Status | Draft / In Review / Approved | | Date | [date] | ## 1. Executive Summary ... (continue with all 13 sections)
Save output to:
.dc/prd/prd-v1.0.md — Full PRD.dc/prd/prd-risks.md — Expanded risk matrix.dc/prd/prd-figma-analysis.md — Figma analysis (if applicable)After generating the PRD, suggest:
/dc:specify → Convert user stories into Gherkin specs
/dc:tech-plan → Generate blueprint from the PRD
/dc:breakdown → Create TDD tasks from the storiesSelect based on product type:
saas — B2B platforms, dashboards, multi-tenantmobile — Native iOS/Android, PWAecommerce — Online stores, marketplacesapi — Public APIs, SDKs, developer platformsinternal — Internal team toolslanding — Landing pages, conversion funnels[NEEDS CLARIFICATION]/dc:quick instead/dc:specify directly| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 35,695 | 32,181 | -10% | 1 | 1 | 0% | 6,126 | 6,336 | +3% | 0 | 0 | — |
case-02 | fail→pass | 38,373 | 38,236 | -0% | 1 | 1 | 0% | 6,212 | 7,310 | +18% | 0 | 0 | — |
case-03 | fail→fail | 32,379 | 38,210 | +18% | 1 | 1 | 0% | 5,254 | 7,309 | +39% | 0 | 0 | — |
case-04 | fail→pass | 12,383 | 5,318 | -57% | 1 | 1 | 0% | 1,909 | 1,855 | -3% | 0 | 0 | — |
case-05 | pass→pass | 23,519 | 16,728 | -29% | 1 | 1 | 0% | 4,606 | 4,173 | -9% | 0 | 0 | — |
case-06 | pass→pass | 7,187 | 2,228 | -69% | 1 | 1 | 0% | 1,147 | 1,480 | +29% | 0 | 0 | — |
case-07 | pass→pass | 15,059 | 32,148 | +113% | 1 | 1 | 0% | 2,529 | 6,660 | +163% | 0 | 0 | — |
case-08 | pass→pass | 26,269 | 21,235 | -19% | 1 | 1 | 0% | 2,749 | 4,411 | +60% | 0 | 0 | — |
case-09 | fail→pass | 13,458 | 7,786 | -42% | 1 | 1 | 0% | 2,305 | 2,304 | -0% | 0 | 0 | — |
case-10 | fail→pass | 17,191 | 5,266 | -69% | 1 | 1 | 0% | 2,869 | 1,984 | -31% | 0 | 0 | — |
case-11 | fail→pass | 13,292 | 2,244 | -83% | 1 | 1 | 0% | 2,016 | 1,400 | -31% | 0 | 0 | — |
case-12 | pass→pass | 12,987 | 4,760 | -63% | 1 | 1 | 0% | 2,126 | 1,797 | -15% | 0 | 0 | — |
case-13 | pass→pass | 12,249 | 3,695 | -70% | 1 | 1 | 0% | 1,949 | 1,615 | -17% | 0 | 0 | — |
case-14 | pass→pass | 12,400 | 3,499 | -72% | 1 | 1 | 0% | 1,930 | 1,640 | -15% | 0 | 0 | — |
case-15 | pass→pass | 13,506 | 12,453 | -8% | 1 | 1 | 0% | 2,014 | 2,915 | +45% | 0 | 0 | — |
case-16 | pass→pass | 12,702 | 10,967 | -14% | 1 | 1 | 0% | 2,397 | 3,170 | +32% | 0 | 0 | — |
case-17 | pass→pass | 9,339 | 1,983 | -79% | 1 | 1 | 0% | 1,498 | 1,385 | -8% | 0 | 0 | — |
case-18 | fail→pass | 8,985 | 2,505 | -72% | 1 | 1 | 0% | 1,290 | 1,487 | +15% | 0 | 0 | — |
case-19 | pass→pass | 6,972 | 6,185 | -11% | 1 | 1 | 0% | 1,165 | 2,030 | +74% | 0 | 0 | — |
case-20 | pass→pass | 7,002 | 7,933 | +13% | 1 | 1 | 0% | 1,212 | 2,412 | +99% | 0 | 0 | — |
case-21 | fail→fail | 17,817 | 9,846 | -45% | 1 | 1 | 0% | 2,619 | 2,638 | +1% | 0 | 0 | — |
case-22 | fail→pass | 12,157 | 6,381 | -48% | 1 | 1 | 0% | 1,915 | 2,187 | +14% | 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 +32 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.