alexe-ev Generate Insights
Purpose
Help teams transform raw research observations — quotes, behaviors, patterns — into well-formed product insights that are non-obvious, specific, and decision-relevant.
Skill type
Conceptual skill
Use this skill when
- Research has been conducted but the team isn't sure what it means
- Observations have been collected but haven't been turned into insights
- A synthesis session needs structure to move from data to meaning
- Multiple research sources need to be combined into a unified insight set
Do not use this skill when
- The goal is structuring observations into opportunity statements (use frame-insight-opportunity)
- The goal is presenting findings to stakeholders (this skill generates insights; presentation is downstream)
Required inputs
- Research observations, notes, or synthesized themes
Optional inputs
- Prior beliefs or assumptions to contrast with findings
- Multiple research sources to triangulate
- Segment or context breakdown of data
Upstream context
Works best when:
- User interviews or other qualitative research has been completed
- Observations have been collected and organized
If upstream context is missing
Cannot generate insights without data. Return to synthesize-qualitative-research or run-user-interviews first.
Downstream handoff
Output can feed:
- frame-insight-opportunity (insights → opportunity statements)
- develop-persona-segment (insights about different user types → personas)
- formulate-experiment-hypothesis (insights → testable hypotheses)
Instructions
- Review all observations and group related ones into themes.
- For each theme, identify the underlying pattern — what does this tell us about user behavior or needs?
- Test each candidate insight: is it non-obvious? Is it specific? Is it supported by multiple data points?
- Write each insight as a declarative statement (not a question, not a recommendation).
- Rate evidence quality: single data point / pattern / strongly supported.
- Identify contradictory data points — don't ignore them.
- Separate insights from implications and recommendations.
Output
Provide:
- Insight set (3–10 insights, each as a clear declarative statement)
- Supporting evidence for each insight
- Evidence quality rating
- Contradictory or complicating data
- What this changes relative to prior assumptions
- Gaps: what we still don't know
Risks / caveats
- "Users want X" is an observation or a recommendation, not an insight
- An insight with only one supporting data point is a hypothesis
- Don't filter out uncomfortable insights — they're often the most valuable