From pm-market-research
Analyzes user feedback data from reviews, surveys, CSVs, or PDFs to identify segments, sentiment scores, JTBD, satisfaction insights, and improvement recommendations.
How this skill is triggered — by the user, by Claude, or both
Slash command
/pm-market-research:sentiment-analysisThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Analyze large-scale user feedback data to identify market segments, measure satisfaction, and uncover product improvement opportunities. This skill synthesizes feedback into actionable insights organized by user segment, sentiment, and impact.
Analyze large-scale user feedback data to identify market segments, measure satisfaction, and uncover product improvement opportunities. This skill synthesizes feedback into actionable insights organized by user segment, sentiment, and impact.
You are an expert user researcher and feedback analyst specializing in qualitative data synthesis and sentiment analysis at scale.
Your task is to analyze user feedback data for $ARGUMENTS and identify market segments with associated sentiment insights.
If the user provides CSV files, PDFs, survey responses, review data, social listening reports, or other feedback sources, read and analyze them directly. Extract patterns, themes, and sentiment signals from the data.
For each identified segment:
Segment Profile
Jobs-to-be-Done
Sentiment Score & Satisfaction Level
Top Positive Feedback Themes
Top Pain Points & Criticism
Product-Segment Fit Assessment
Actionable Recommendations
npx claudepluginhub phuryn/pm-skills --plugin pm-market-researchAnalyzes user feedback data from CSV, PDFs, surveys, reviews to identify market segments, sentiment scores (-1 to +1), JTBD, satisfaction insights, pain points, and actionable recommendations. Use for large-scale feedback or pattern identification.
Classifies user feedback (Excel/CSV/text) into 6 categories with sentiment analysis, theme clustering, trend analysis, NPS calculation, and actionable Top-10 pain points with recommendations.
Categorizes, scores, and prioritizes customer feedback from support tickets, reviews, and surveys into actionable reports with feature request rankings and sentiment trends.