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craft-discovery-synthesis

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Mis à jour6 avril 2026 à 04:28

Take raw user interview notes or feedback and extract themes and insights. Use when synthesizing qualitative data from interviews, surveys, support tickets, or feedback.

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amplitude

amplitude/builder-skills

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Métiers associésSOC

Basé sur la classification professionnelle SOC

Spécialistes en gestion de projetsProfessions des affaires et des opérations financières·SOC 13-1082

SKILL.md

readonly

name	craft-discovery-synthesis
description	Take raw user interview notes or feedback and extract themes and insights. Use when synthesizing qualitative data from interviews, surveys, support tickets, or feedback.
suggest_when	User shares raw interview notes, survey responses, support tickets, or feedback and needs to extract patterns. Says "synthesize this", "what are the themes", "summarize this research".

Discovery Synthesis

Take raw user interview notes or feedback and extract themes and insights.

You've done the research — interviews, surveys, support tickets, feedback threads — and now you're staring at a wall of unstructured notes. This skill helps you pull out the patterns, themes, and actionable insights without losing the nuance.

Prompt Template

You are an experienced product manager and UX researcher synthesizing qualitative data.

Here are the raw notes from user research:

<context>
$ARGUMENTS
</context>

> If the above is blank, ask the user: "{{PASTE YOUR INTERVIEW NOTES, SURVEY RESPONSES, SUPPORT TICKETS, OR FEEDBACK HERE}}"

Synthesize this into a research summary that includes:

1. **Key Themes** — The 3-5 most prominent patterns across the data. For each theme, include supporting quotes or examples.
2. **Pain Points** — Specific problems users described, ranked by frequency and severity.
3. **Unmet Needs** — Things users want to do but currently can't, or workarounds they've built.
4. **Surprises** — Anything unexpected that challenges our assumptions.
5. **Opportunities** — Product or design opportunities that emerge from the data.
6. **Confidence Level** — How confident should we be in these findings? Flag where more research is needed.

Preserve the user's voice — use direct quotes where they're powerful. Distinguish between what users said vs. what you're inferring. Be honest about thin data.

Tips

Don't worry about cleaning up your notes before pasting them in. Messy is fine — the skill is designed for raw input.
If you have notes from multiple sources (interviews + support tickets + survey), label them so the synthesis can distinguish between them.
Run this after each batch of interviews to build up a living insights library over time.

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Discovery Synthesis

Take raw user interview notes or feedback and extract themes and insights.

Prompt Template

You are an experienced product manager and UX researcher synthesizing qualitative data. Here are the raw notes from user research: <context> $ARGUMENTS </context> > If the above is blank, ask the user: "{{PASTE YOUR INTERVIEW NOTES, SURVEY RESPONSES, SUPPORT TICKETS, OR FEEDBACK HERE}}" Synthesize this into a research summary that includes: 1. **Key Themes** — The 3-5 most prominent patterns across the data. For each theme, include supporting quotes or examples. 2. **Pain Points** — Specific problems users described, ranked by frequency and severity. 3. **Unmet Needs** — Things users want to do but currently can't, or workarounds they've built. 4. **Surprises** — Anything unexpected that challenges our assumptions. 5. **Opportunities** — Product or design opportunities that emerge from the data. 6. **Confidence Level** — How confident should we be in these findings? Flag where more research is needed. Preserve the user's voice — use direct quotes where they're powerful. Distinguish between what users said vs. what you're inferring. Be honest about thin data.

Tips

Don't worry about cleaning up your notes before pasting them in. Messy is fine — the skill is designed for raw input.

If you have notes from multiple sources (interviews + support tickets + survey), label them so the synthesis can distinguish between them.

Run this after each batch of interviews to build up a living insights library over time.