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Get Started Free →Plan and execute user research including research planning, recruiting, interview design, qualitative synthesis, and translating findings into product decisions. Use this skill whenever the user wants to plan user research, design interviews, recruit participants, conduct discovery, run formative research, or synthesize qualitative findings. Triggers on user research, UX research, user interviews, discovery research, generative research, formative research, qualitative research, user insights, r
.claude/skills/rampstackco-ux-research/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 65% | 0% |
| case-18 | ✗→✓ | ▲ Improved | 111% | 0% |
| case-22 | ✗→✓ | ▲ Improved | 53% | 0% |
| case-03 | ✓→✗ | ▼ Worse | 36% | 0% |
| case-02 | ✓→✓ | = Same ✓ | 79% | 0% |
Plan and execute user research that produces decisions, not just decks. Stack-agnostic. Tool-agnostic.
This skill is for generative and discovery research. For testing existing designs, use usability-testing. For mapping the full customer experience, use journey-mapping.
usability-testing)journey-mapping)analytics-strategy)cro-optimization)Bad questions produce bad research. Spend disproportionate time on framing.
Good research questions:
Examples:
| Weak question | Better question | |---|---| | "Do users like our onboarding?" | "Where in onboarding do new users feel uncertain about whether to continue?" | | "What features should we build?" | "What unmet needs do current users have when specific job]?" | | "Why is conversion low?" | "What's the user mental model when they reach the pricing page, and where does it diverge from our intent?" |
The method follows the question.
Generative methods (what's true?):
Validation methods (is this hypothesis right?):
(For testing usability of working designs, see usability-testing.)
The recruit makes or breaks the research.
Recruit criteria:
Recruit channels:
Incentive: Pay participants. Standard rates: $50 to $150 for 60 minutes, more for executives or specialized professions.
Recruit volume: Plan for 20 to 30 percent no-show. Recruit 7 to schedule 5.
The interview or session itself.
Pre-interview:
During the interview:
Anti-patterns:
Notes don't become insights automatically.
The synthesis process:
Heuristics for strong insights:
Findings die in slide decks. Plan distribution.
Outputs that work:
Outputs that fail:
Default outputs:
research-plan-[topic].mdinterview-guide-[topic].mdresearch-findings-[topic].mdFindings document structure:
markdown# [Topic] research findings ## Question we set out to answer [Specific question] ## Method [Approach, sample size, dates] ## Top insights 1. [Insight, stated in one sentence] - Supporting evidence: [Quotes, behaviors, or state the gap per the data-availability rule] - Implication: [What this means for product/strategy] 2. [Insight 2] ... ## Themes (less prominent than top insights, still worth noting) [List] ## Outliers worth investigating [Single-participant observations that may be signal in disguise] ## Recommended next steps [Specific actions]
This skill's output depends on data, measurements, or tool results it cannot generate on its own. When a required input, tool, or data source is unavailable or unverifiable, the sanctioned output is the deliverable with the gap stated: what was needed, what was actually obtained or verified, and which parts of the output are affected. Fabricating, estimating, or interpolating a required number to complete the deliverable is never sanctioned. A stated gap is a complete answer.
references/interview-guide-template.md - Structured interview guide template with example openings, probes, and closes.| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-03 | pass→fail | 21,788 | 17,179 | -21% | 1 | 1 | 0% | 3,740 | 5,078 | +36% | 0 | 0 | — |
case-01 | fail→fail | 32,855 | 26,833 | -18% | 1 | 1 | 0% | 5,206 | 6,583 | +26% | 0 | 0 | — |
case-02 | pass→pass | 18,421 | 18,971 | +3% | 1 | 1 | 0% | 3,000 | 5,371 | +79% | 0 | 0 | — |
case-04 | fail→pass | 16,338 | 14,147 | -13% | 1 | 1 | 0% | 2,827 | 4,660 | +65% | 0 | 0 | — |
case-05 | pass→pass | 12,728 | 9,946 | -22% | 1 | 1 | 0% | 2,084 | 3,921 | +88% | 0 | 0 | — |
case-06 | pass→pass | 8,985 | 7,715 | -14% | 1 | 1 | 0% | 1,520 | 3,582 | +136% | 0 | 0 | — |
case-07 | pass→pass | 10,062 | 7,854 | -22% | 1 | 1 | 0% | 1,674 | 3,496 | +109% | 0 | 0 | — |
case-08 | pass→pass | 9,135 | 7,284 | -20% | 1 | 1 | 0% | 1,547 | 3,549 | +129% | 0 | 0 | — |
case-09 | pass→pass | 9,082 | 6,315 | -30% | 1 | 1 | 0% | 1,584 | 3,433 | +117% | 0 | 0 | — |
case-10 | pass→pass | 13,171 | 11,600 | -12% | 1 | 1 | 0% | 2,074 | 4,063 | +96% | 0 | 0 | — |
case-11 | pass→pass | 9,393 | 4,679 | -50% | 1 | 1 | 0% | 1,618 | 3,088 | +91% | 0 | 0 | — |
case-12 | pass→pass | 11,585 | 8,468 | -27% | 1 | 1 | 0% | 1,898 | 3,680 | +94% | 0 | 0 | — |
case-13 | pass→pass | 7,972 | 5,809 | -27% | 1 | 1 | 0% | 1,363 | 3,315 | +143% | 0 | 0 | — |
case-14 | pass→pass | 10,295 | 7,685 | -25% | 1 | 1 | 0% | 1,590 | 3,540 | +123% | 0 | 0 | — |
case-15 | pass→pass | 14,015 | 12,206 | -13% | 1 | 1 | 0% | 2,039 | 4,162 | +104% | 0 | 0 | — |
case-16 | pass→pass | 14,035 | 15,242 | +9% | 1 | 1 | 0% | 2,032 | 4,505 | +122% | 0 | 0 | — |
case-17 | pass→pass | 13,951 | 14,054 | +1% | 1 | 1 | 0% | 2,117 | 4,421 | +109% | 0 | 0 | — |
case-18 | fail→pass | 12,311 | 10,727 | -13% | 1 | 1 | 0% | 1,812 | 3,829 | +111% | 0 | 0 | — |
case-19 | pass→pass | 13,087 | 13,291 | +2% | 1 | 1 | 0% | 1,905 | 4,301 | +126% | 0 | 0 | — |
case-20 | pass→pass | 12,087 | 11,902 | -2% | 1 | 1 | 0% | 1,924 | 4,120 | +114% | 0 | 0 | — |
case-21 | pass→pass | 13,094 | 11,599 | -11% | 1 | 1 | 0% | 1,981 | 4,101 | +107% | 0 | 0 | — |
case-22 | fail→pass | 20,671 | 15,696 | -24% | 1 | 1 | 0% | 3,143 | 4,809 | +53% | 0 | 0 | — |
case-23 | pass→pass | 14,454 | 10,650 | -26% | 1 | 1 | 0% | 2,090 | 3,860 | +85% | 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. 23 cases were attempted. The headline lift of +9 percentage points is the difference between those two pass rates over the 23 comparable cases. 1 case got worse with the skill loaded, and it is 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.