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Synthesize user research into actionable insights using atomic research methods. Use when asked to synthesize research, organize user feedback, find patterns in interviews, summarize customer discovery, or turn raw notes into insights and recommendations.
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SOC 職業分類に基づく
Evaluate product bets and shape pitches using Shape Up's appetite model and Bezos's Type 1/Type 2 decision framework. Use when asked to assess a product bet, evaluate initiative risk, decide resource allocation, or shape a pitch for a new feature or project.
Analyze competitive landscape with feature matrices, positioning maps, and strategic gap analysis. Use when asked to analyze competitors, map the competitive landscape, find differentiation, or evaluate alternatives to a product.
Design hypothesis-driven experiments and A/B tests with proper methodology. Use when asked to design an A/B test, validate a hypothesis, plan an experiment, or set up a test for a product change. Covers hypothesis writing, sample size, and common mistakes.
Prioritize features and backlog items using RICE scoring and Linear's enablers vs blockers lens. Use when asked to rank features, prioritize a backlog, decide what to build next, or evaluate feature requests against each other.
Analyze customer motivations using Jobs-to-be-Done and Forces of Progress. Use when asked about jobs-to-be-done, why customers switch products, what job a product is hired for, or when analyzing the forces that drive or resist product adoption.
Plan product launches with the right tier and coordinated checklists. Use when asked to plan a launch, coordinate a release, prepare for go-to-market, or figure out how to announce a new feature or product. Covers silent, soft, and big-bang launch tiers.
| name | research-synthesis |
| description | Synthesize user research into actionable insights using atomic research methods. Use when asked to synthesize research, organize user feedback, find patterns in interviews, summarize customer discovery, or turn raw notes into insights and recommendations. |
Turn raw research into atomic insights that drive decisions. Good synthesis surfaces patterns, bad synthesis creates narrative fiction. The goal is structured evidence, not a compelling story that cherry-picks quotes.
Break research into four levels, bottom-up:
Individual observations from a single source. Each nugget is:
Example: "[P3, Jan 12] 'I spend 30 minutes after every customer call just trying to remember what they said.'"
Group nuggets that point to the same phenomenon. A pattern requires evidence from 3+ independent sources.
Example: "5 of 7 PMs report spending 20-45 minutes on post-call documentation. All describe it as tedious and low-value."
What the pattern means for the product. An insight connects a pattern to a product opportunity or risk.
Example: "Post-call documentation is a high-frequency pain point (daily for active PMs) with no satisfying solution. Current workarounds (voice memos, bullet lists) lose context and emotional nuance."
Specific product actions justified by insights. Each recommendation traces back through the chain: recommendation ← insight ← pattern ← nuggets.
Rate every pattern and insight:
| Strength | Criteria |
|---|---|
| Strong | 5+ sources, consistent behavior observed, corroborated by data |
| Moderate | 3-4 sources, mostly consistent, some data support |
| Emerging | 2 sources, needs more evidence before acting |
| Weak | Single source or contradictory evidence |
NEVER make product recommendations based on Weak or Emerging evidence. Flag them for further research.
Built on the Atomic Research method (Daniel Pidcock) and Continuous Discovery Habits (Teresa Torres). Skills from productskills.
