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Get Started Free →When the user has raw customer interview transcripts, survey responses, support tickets, or other qualitative data and needs to extract actionable insights.
.claude/skills/mkurman-user-research-synthesis/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-03 | ✗→✓ | ▲ Improved | 23% | 0% |
| case-01 | ✗→✓ | ▲ Improved | 23% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 96% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 28% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 187% | 0% |
|---|---| | When situation], I want to motivation], so I can outcome] | X of N sources | Segment names | High/Med/Low |
Numbered list of insight statements using the format: "We learned that finding] which means implication] so we should recommendation]."
What the data did NOT answer and recommended next research steps.
prd-writing -- Chain research synthesis directly into the Background and Market Segments sections of a PRD.competitive-analysis -- Combine customer insights with competitive data to identify underserved jobs where competitors fall short.feedback-synthesis -- Chain when you have a mix of structured feedback data (tickets, NPS) alongside interview transcripts.User: "Here's a transcript from our discovery interview with a logistics manager. Summarize it."
Good output excerpt: > Metadata: March 10, 2026 | Sarah Chen, Logistics Manager at MidCo (150 employees) > > Current solutions: Uses a combination of Excel spreadsheets and email chains to coordinate shipments. Satisfaction: 3/10. "It works but I lose about 5 hours a week just keeping everything in sync." > > Problems identified: > - No single source of truth for shipment status (mentioned 4 times) > - Cannot see driver availability in real time; relies on phone calls > - Reporting to management requires manual data compilation every Friday > > Key discovery: Sarah's team has built an informal Slack channel as a workaround for real-time updates. This was not anticipated in our research plan and suggests messaging integration may be higher priority than dashboard features.
User: "I just finished 8 customer interviews for our B2B scheduling tool. Here are the transcripts. What are the key takeaways?"
Good output excerpt: > JTBD #1 (7/8 interviews, High confidence): "When I'm coordinating meetings across 3+ time zones, I want to see everyone's availability in one view, so I can book a slot without 6 back-and-forth emails." > > Insight: We learned that multi-timezone scheduling is the primary job, not calendar management broadly. This means our positioning should lead with "global team coordination" rather than "smart calendar." We should prioritize the timezone overlay feature in the next sprint. > > Open question: None of the 8 participants were solo users. We still do not know whether the product has value for individuals without teams.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-03 | fail→pass | 21,666 | 20,537 | -5% | 1 | 1 | 0% | 3,360 | 4,134 | +23% | 0 | 0 | — |
case-01 | fail→pass | 17,021 | 16,210 | -5% | 1 | 1 | 0% | 2,806 | 3,447 | +23% | 0 | 0 | — |
case-02 | fail→pass | 12,117 | 17,400 | +44% | 1 | 1 | 0% | 1,808 | 3,536 | +96% | 0 | 0 | — |
case-04 | pass→fail | 30,899 | 31,832 | +3% | 1 | 1 | 0% | 5,397 | 6,262 | +16% | 0 | 0 | — |
case-05 | pass→pass | 20,887 | 18,741 | -10% | 1 | 1 | 0% | 3,166 | 3,671 | +16% | 0 | 0 | — |
case-06 | pass→fail | 14,627 | 18,644 | +27% | 1 | 1 | 0% | 2,281 | 3,796 | +66% | 0 | 0 | — |
case-07 | fail→pass | 17,134 | 15,310 | -11% | 1 | 1 | 0% | 2,582 | 3,314 | +28% | 0 | 0 | — |
case-08 | fail→pass | 5,641 | 9,315 | +65% | 1 | 1 | 0% | 886 | 2,540 | +187% | 0 | 0 | — |
case-09 | fail→fail | 3,274 | 3,700 | +13% | 1 | 1 | 0% | 560 | 1,474 | +163% | 0 | 0 | — |
case-10 | pass→pass | 11,409 | 16,029 | +40% | 1 | 1 | 0% | 1,586 | 3,317 | +109% | 0 | 0 | — |
case-11 | pass→pass | 13,453 | 8,296 | -38% | 1 | 1 | 0% | 1,924 | 2,228 | +16% | 0 | 0 | — |
case-12 | pass→pass | 13,025 | 9,418 | -28% | 1 | 1 | 0% | 1,887 | 2,337 | +24% | 0 | 0 | — |
case-13 | pass→pass | 12,238 | 8,672 | -29% | 1 | 1 | 0% | 1,703 | 2,270 | +33% | 0 | 0 | — |
case-14 | pass→pass | 8,098 | 4,709 | -42% | 1 | 1 | 0% | 1,395 | 1,744 | +25% | 0 | 0 | — |
case-15 | fail→pass | 7,860 | 2,870 | -63% | 1 | 1 | 0% | 1,124 | 1,387 | +23% | 0 | 0 | — |
case-16 | pass→pass | 18,783 | 16,303 | -13% | 1 | 1 | 0% | 2,669 | 3,328 | +25% | 0 | 0 | — |
case-17 | fail→fail | 7,308 | 14,948 | +105% | 1 | 1 | 0% | 1,125 | 3,235 | +188% | 0 | 0 | — |
case-18 | pass→fail | 8,074 | 15,686 | +94% | 1 | 1 | 0% | 1,276 | 3,430 | +169% | 0 | 0 | — |
case-19 | pass→pass | 16,451 | 14,770 | -10% | 1 | 1 | 0% | 2,398 | 3,109 | +30% | 0 | 0 | — |
case-20 | fail→pass | 14,994 | 18,484 | +23% | 1 | 1 | 0% | 2,036 | 3,742 | +84% | 0 | 0 | — |
case-21 | fail→pass | 9,878 | 6,453 | -35% | 1 | 1 | 0% | 1,438 | 1,871 | +30% | 0 | 0 | — |
case-22 | fail→fail | 15,809 | 18,747 | +19% | 1 | 1 | 0% | 2,266 | 3,954 | +74% | 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 +23 percentage points is the difference between those two pass rates over the 22 comparable cases. 3 cases got worse with the skill loaded, and they are included in that figure.
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.