What problem does it solve? User research work spans questionnaire design, interview planning, open-ended response coding, and survey data analysis, and doing each manually is slow and inconsistent. This Skill covers the full research workflow with structured processes, quality gates, and deliverable documents in Word or Feishu format. ## Core Features & Use Cases - Questionnaire Design (M1): Builds structured questionnaires from research goals and target audiences, with simulated test-fills by multiple user personas, option/scale self-checks, and delivery as Word or Feishu docs. - Interview Outline Generation (M2): Produces regular (5-8 questions) or in-depth (10-15+ questions) interview outlines with structured follow-up annotations covering rounds, directions, and trigger conditions, always delivered as a Feishu doc. - Verbatim Tagging (M3): Builds a label taxonomy from open-ended responses through a five-step workflow with sampling quality checks, second-batch validation, and full-corpus annotation. - Quantitative Survey Analysis (M4): Takes only raw response data (CSV/Excel/Feishu sheet), auto-detects question types, cleans data, runs descriptive and cross-tab analysis, and outputs a report with key findings, personas, and real verbatim quotes. - Use Case: Upload a CSV of 500 survey responses and ask for an analysis report; the Skill identifies question types, cleans low-quality responses, computes NPS and cross-tabs with unweighted N annotations, and delivers a Feishu report with evidence-backed findings. ## Quick Start Ask the assistant to design a satisfaction survey for new users who registered in the last 30 days but never placed an order, delivered as a Feishu document.
