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Get Started Free →Synthesises user interview transcripts into structured research findings. Use when asked to analyse interview notes, synthesise qualitative research, identify themes from interviews, or turn raw interview data into actionable product insights. Produces a themed synthesis with supporting quotes per theme, 'so what' implications, and recommended next steps. For mixed sources beyond interviews (surveys, tickets, feedback) use user-research-synthesis instead.
.claude/skills/mohitagw15856-user-interview-synthesis/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -11% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 71% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 93% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 63% | 0% |
| case-21 | ✗→✓ | ▲ Improved | 93% | 0% |
Transform raw interview transcripts into a structured synthesis document that surfaces themes, pain points, and actionable insights.
Ask the user for these if not provided:
Participants: n] Date Range: dates] Research Questions: list]
Repeat for each theme]
Findings worth tracking but not acting on yet — note what further research would confirm or deny]
Specific, actionable recommendations based on findings]
This skill ships with support files — use them when they are available:
references/coding-transcripts.md — Coding Interview Transcripts Without Losing the Signal. Apply it while producing the output; it carries the calibration and judgment calls the method summary above compresses.templates/per-session-capture.md — a fill-in version of the deliverable with the quality gates inline. Offer it when the user wants to work the document themselves rather than have it generated.Score any output of this skill before handing it over; 32+ is ship-quality.
| Dimension | 0 | 5 | 10 | |---|---|---|---| | Evidence traceability | Themes asserted with no quotes or participant attribution | Most themes carry quotes, but some rest on 1–2 participants or unattributed paraphrase | Every theme carries verbatim quotes from ≥3 distinct participants, with frequency counts ("6 of 9") consistent with the roster | | Implication actionability | Implications restate the observation ("users find X frustrating") | Implications gesture at direction but name no decision, owner, or change | Every implication enables a specific product decision someone could act on this quarter | | Contradiction honesty | All findings conveniently support the sponsor's hypothesis; inconvenient data absent | Contradictory evidence present but buried or softened; both-ways quotes trimmed to the helpful half | Findings that contradict the hypothesis are surfaced prominently, and ambiguous quotes are kept whole with the tension flagged | | Signal separation & question coverage | Single-source anecdotes mixed into main themes; research questions ignored | Low-confidence signals segregated but with no follow-up path, or one research question left unaddressed | Every 1–2-participant signal sits in its own section with the cheap test that would confirm it, and every research question gets an explicit answer — including "inconclusive" |
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-14 | pass→pass | 25,778 | 26,621 | +3% | 1 | 1 | 0% | 2,723 | 4,408 | +62% | 0 | 0 | — |
case-01 | fail→pass | 45,782 | 31,290 | -32% | 1 | 1 | 0% | 5,677 | 5,040 | -11% | 0 | 0 | — |
case-02 | fail→fail | 29,610 | 14,990 | -49% | 1 | 1 | 0% | 4,127 | 2,682 | -35% | 0 | 0 | — |
case-08 | fail→pass | 20,953 | 22,838 | +9% | 1 | 1 | 0% | 2,157 | 3,688 | +71% | 0 | 0 | — |
case-03 | fail→fail | 39,001 | 12,233 | -69% | 1 | 1 | 0% | 4,453 | 2,202 | -51% | 0 | 0 | — |
case-04 | pass→pass | 23,086 | 21,221 | -8% | 1 | 1 | 0% | 2,879 | 3,832 | +33% | 0 | 0 | — |
case-05 | fail→fail | 9,714 | 14,833 | +53% | 1 | 1 | 0% | 584 | 2,196 | +276% | 0 | 0 | — |
case-06 | pass→pass | 20,839 | 26,981 | +29% | 1 | 1 | 0% | 2,167 | 3,685 | +70% | 0 | 0 | — |
case-07 | fail→pass | 17,693 | 23,983 | +36% | 1 | 1 | 0% | 2,159 | 4,157 | +93% | 0 | 0 | — |
case-09 | fail→pass | 24,487 | 26,787 | +9% | 1 | 1 | 0% | 2,556 | 4,156 | +63% | 0 | 0 | — |
case-10 | pass→pass | 24,835 | 14,703 | -41% | 1 | 1 | 0% | 2,896 | 2,451 | -15% | 0 | 0 | — |
case-11 | pass→pass | 21,098 | 32,791 | +55% | 1 | 1 | 0% | 2,321 | 5,017 | +116% | 0 | 0 | — |
case-12 | fail→fail | 7,406 | 9,142 | +23% | 1 | 1 | 0% | 1,057 | 1,590 | +50% | 0 | 0 | — |
case-13 | fail→fail | 11,383 | 9,829 | -14% | 1 | 1 | 0% | 1,096 | 1,645 | +50% | 0 | 0 | — |
case-15 | pass→pass | 25,144 | 25,019 | -0% | 1 | 1 | 0% | 2,350 | 4,165 | +77% | 0 | 0 | — |
case-16 | pass→pass | 19,491 | 23,179 | +19% | 1 | 1 | 0% | 2,133 | 3,687 | +73% | 0 | 0 | — |
case-17 | pass→pass | 14,988 | 27,452 | +83% | 1 | 1 | 0% | 2,008 | 4,091 | +104% | 0 | 0 | — |
case-18 | pass→pass | 15,311 | 47,392 | +210% | 1 | 1 | 0% | 2,351 | 4,093 | +74% | 0 | 0 | — |
case-19 | pass→pass | 21,041 | 29,474 | +40% | 1 | 1 | 0% | 2,192 | 4,749 | +117% | 0 | 0 | — |
case-20 | fail→fail | 12,547 | 12,622 | +1% | 1 | 1 | 0% | 1,101 | 2,160 | +96% | 0 | 0 | — |
case-21 | fail→pass | 18,501 | 28,741 | +55% | 1 | 1 | 0% | 2,197 | 4,246 | +93% | 0 | 0 | — |
case-22 | fail→pass | 18,003 | 37,734 | +110% | 1 | 1 | 0% | 1,943 | 3,565 | +83% | 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 +27 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.