product-research

原文涵盖的主题

• product-research
• Purpose
• When to use
• Workflow
• Scripts
• Onboarding & customization
• Optimize with autoresearch (opt-in)
• References

Product / user research as an operational discipline: choosing the right method, sizing it honestly, and synthesizing findings into governed insights. The core rule: method must match the goal, and an insight requires recurrence across independent participants — a single quote is an anecdote. Product researchers, ResearchOps teams, and PMs running discovery need method rigor and an insight repository they can trust. This skill structures three decisions: Three deterministic tools: 1. studydesigner.py — Maps (research goal × product stage) to an appropriate method and emits a method-matched plan skeleton (objective, participant criteria, guide structure, success criteria). Redirects live A/B to product-team/experiment-designer. 2. saturationplanner.py — Method-based sample guidance with an explicit confidence label: Nielsen problem-discovery (5/segment), Guest et al. thematic saturation (12), and evaluative coverage. Never claims a prevalence rate from a small-n usability test. 3. insightsynthesizer.py — Clusters coded observations by tag, counts distinct participants, ranks by cross-participant recurrence, and flags any candidate below the source threshold as an ANECDOTE, never pro

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