Qualitative Research Synthesiser
The failure mode in AI-assisted research synthesis is confidence without accuracy: the AI produces a polished list of themes that has lost the friction, contradiction, and uncertainty that made the research valuable. This skill keeps AI in the role of categoriser and retriever, and keeps you in the role of interpreter.
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Context
What this skill handles: Interview transcripts, survey open-text responses, support tickets, NPS/CSAT comments, session recording notes, usability test observations, mixed sources. The core rule: AI clusters and retrieves. You interpret. Never ask AI to tell you what findings mean — ask it to show you the evidence and you decide.---
Step 1 — Receive and inventory the research
Gather: research objective, sources included, time period, and known context.
Step 2 — Prepare the data
For transcripts: extract participant speech only, label with codes (P1, P2). For surveys: treat each response as a unit. For tickets: extract core need, strip identifying info. Mixed: label every piece with source type.
Step 3 — First pass: tag without clustering
Tag every meaningful unit with descriptive, specific, data-derived topic tags. No themes yet — only tags.
Step 4 — Second pass: cluster into themes
Group by tag co-occurrence. For each theme: label (descriptive, not a conclusion), tag cluster, evidence count, sources, 3–5 supporting quotes (exact), and contradicting evidence.
Theme naming rules: observations not conclusions, never name as a solution.
Step 5 — Third pass: weight and rank
Assess each theme on Frequency (High >50% / Medium 25-50% / Low <25%) and Intensity (emotional language and emphasis level). Produce ranked priority table.
Step 6 — Fourth pass: identify contradictions and gaps
Document contradictions with opposing quotes, possible explanations, and implications. Document gaps with what's unknown, why it matters, and how to fill it.
Step 7 — Produce the synthesis report
Full report with: priority themes (evidence + interpretation clearly separated), weak signals, contradictions, research gaps, recommended next steps, and what the research does NOT tell us.