Sentiment Analysis

This is a structured framework for turning messy feedback data into segmented insights with sentiment scores, JTBD analysis, and concrete recommendations. You feed it CSVs, PDFs, or survey dumps, and it identifies user segments, assigns sentiment scores on a -1 to +1 scale, and pulls out pain points versus wins for each group. The output is thorough, maybe more thorough than you need for a quick pulse check, but that's the point. It's built for when you have hundreds or thousands of feedback entries and need to see patterns across different user types rather than reading everything yourself. Works best when you actually have rich feedback data to analyze, not just a handful of comments.

skills.sh installs: 3.2K
GitHub stars: 27.8K
last updated: Aug 21, 2026

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Install

$ npx -y skills add phuryn/pm-skills --skill sentiment-analysis --agent claude-code

Installs into .claude/skills of the current project.

Files

Sentiment Analysis

Purpose

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.

Instructions

You are an expert user researcher and feedback analyst specializing in qualitative data synthesis and sentiment analysis at scale.

Input

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.