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Get Started Free →Create a structured UX research plan for any product question or feature. Use when asked to write a research plan, design a user study, create a discussion guide, write screener questions, or plan usability testing. Produces a full research plan with objectives, methodology, screener, discussion guide, and synthesis framework.
.claude/skills/mohitagw15856-ux-research-plan/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | -6% | 0% |
| case-01 | ✗→✓ | ▲ Improved | 27% | 0% |
| case-02 | ✗→✓ | ▲ Improved | -15% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 21% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 86% | 0% |
This skill creates a complete, ready-to-execute UX research plan. Output covers everything from research objectives to screener questions, discussion guide, and synthesis framework.
Ask the user for these if not provided:
Product area: Area] Research type: Type] Date: Timeline] Researcher: Leave for user]
State 2–4 clear research objectives. Each objective should map to a decision that will be made differently depending on what you find.
Objective N]: Understand specific thing] so we can decision this informs].
5–8 questions — the actual questions you want research to answer. These are not the interview questions; they're the knowledge gaps. Organised under each objective.]
Objective 1:
Method chosen: e.g. Semi-structured interviews / Usability testing / Concept testing]
Why this method: 2–3 sentences. Match method to research type. If evaluative: usability testing. If generative: contextual inquiry or interviews. If testing comprehension: 5-second test or concept test.]
What this method will and won't tell us:
Sample size: Recommended number of sessions and why — e.g. "5–6 moderated interviews for generative research; 5–8 usability sessions to identify top issues"]
Recruitment criteria:
| Criterion | Must Have / Nice to Have | Disqualify if | |---|---|---| | e.g. Uses project management software daily] | Must Have | Never uses any PM tool] | | e.g. Works in a team of 5+] | Must Have | — | | e.g. B2B industry] | Nice to Have | — |
Screener questions (5–8 questions):
Q1] Screening question — clear, not leading]
Q2] ...
Incentive recommendation: Amount and format — e.g. "£50 gift voucher for a 60-min session is standard in the UK for professional participants"]
Structure the session:
Section A]: Topic] (~X min)
Section B]: Topic] (~X min) Continue with 2–3 questions per section]
Usability tasks (if applicable): > "I'm going to ask you to try a few things with this prototype. Please think aloud as you go."
After sessions, use this framework to synthesise findings:
Step 1: Session notes → Key observations For each session: 3–5 specific observations (behaviours, quotes, reactions — not interpretations yet)
Step 2: Affinity mapping Group observations by theme across all sessions. Aim for 4–7 clusters.
Step 3: Insight statements For each cluster: "When context], users behaviour/experience], because underlying need or mental model]."
Step 4: Implications For each insight: "This means we should design/product implication]" or "This challenges our assumption that assumption]."
Step 5: Research report structure:
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-04 | fail→pass | 42,955 | 28,389 | -34% | 1 | 1 | 0% | 5,535 | 5,188 | -6% | 0 | 0 | — |
case-01 | fail→pass | 42,962 | 41,611 | -3% | 1 | 1 | 0% | 5,782 | 7,367 | +27% | 0 | 0 | — |
case-02 | fail→pass | 65,328 | 44,795 | -31% | 1 | 1 | 0% | 8,173 | 6,914 | -15% | 0 | 0 | — |
case-03 | fail→pass | 96,388 | 41,806 | -57% | 1 | 1 | 0% | 5,421 | 6,560 | +21% | 0 | 0 | — |
case-05 | fail→pass | 24,391 | 31,437 | +29% | 1 | 1 | 0% | 3,090 | 5,733 | +86% | 0 | 0 | — |
case-06 | fail→pass | 40,859 | 32,680 | -20% | 1 | 1 | 0% | 5,333 | 5,975 | +12% | 0 | 0 | — |
case-07 | fail→pass | 25,764 | 31,702 | +23% | 1 | 1 | 0% | 3,611 | 5,427 | +50% | 0 | 0 | — |
case-08 | fail→pass | 34,738 | 26,469 | -24% | 1 | 1 | 0% | 3,296 | 4,922 | +49% | 0 | 0 | — |
case-09 | fail→pass | 21,871 | 40,354 | +85% | 1 | 1 | 0% | 2,925 | 5,543 | +90% | 0 | 0 | — |
case-10 | pass→pass | 38,896 | 32,840 | -16% | 1 | 1 | 0% | 5,842 | 5,714 | -2% | 0 | 0 | — |
case-11 | fail→pass | 25,634 | 35,058 | +37% | 1 | 1 | 0% | 3,290 | 5,430 | +65% | 0 | 0 | — |
case-12 | pass→pass | 28,883 | 31,966 | +11% | 1 | 1 | 0% | 2,915 | 6,111 | +110% | 0 | 0 | — |
case-13 | fail→pass | 27,159 | 30,772 | +13% | 1 | 1 | 0% | 3,399 | 5,762 | +70% | 0 | 0 | — |
case-14 | fail→pass | 20,763 | 35,186 | +69% | 1 | 1 | 0% | 2,624 | 5,632 | +115% | 0 | 0 | — |
case-15 | fail→pass | 29,256 | 34,652 | +18% | 1 | 1 | 0% | 3,560 | 5,422 | +52% | 0 | 0 | — |
case-16 | pass→pass | 24,074 | 38,196 | +59% | 1 | 1 | 0% | 4,017 | 6,704 | +67% | 0 | 0 | — |
case-17 | fail→pass | 19,420 | 20,996 | +8% | 1 | 1 | 0% | 2,932 | 4,902 | +67% | 0 | 0 | — |
case-18 | fail→fail | 24,499 | 29,104 | +19% | 1 | 1 | 0% | 3,318 | 5,094 | +54% | 0 | 0 | — |
case-19 | pass→pass | 26,643 | 34,406 | +29% | 1 | 1 | 0% | 3,158 | 6,267 | +98% | 0 | 0 | — |
case-20 | fail→fail | 17,100 | 30,032 | +76% | 1 | 1 | 0% | 2,642 | 5,319 | +101% | 0 | 0 | — |
case-21 | fail→fail | 21,841 | 28,839 | +32% | 1 | 1 | 0% | 2,716 | 5,194 | +91% | 0 | 0 | — |
case-22 | pass→fail | 20,157 | 29,662 | +47% | 1 | 1 | 0% | 2,472 | 5,477 | +122% | 0 | 0 | — |
case-23 | pass→pass | 20,104 | 20,216 | +1% | 1 | 1 | 0% | 2,192 | 3,583 | +63% | 0 | 0 | — |
case-24 | pass→pass | 8,775 | 17,355 | +98% | 1 | 1 | 0% | 1,555 | 3,474 | +123% | 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. 24 cases were attempted. The headline lift of +54 percentage points is the difference between those two pass rates over the 24 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.