mcpbeat

Sentiment Analysis

phuryn/sentiment-analysis

Analyze user feedback data to identify segments with sentiment scores, JTBD, and product satisfaction insights. Use when analyzing user feedback at scale, running sentiment analysis on reviews or surveys, or identifying satisfaction patterns.

875 tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
24819
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository 
npx skills add https://github.com/phuryn/pm-skills --skill sentiment-analysis

The instruction itself

8 sections, as written by the author

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.

Analysis Steps (Think Step by Step)

  • Data Ingestion: Read all feedback sources and create a working inventory
  • Segment Identification: Identify at least 3 distinct user segments or personas from the feedback
  • Thematic Analysis: Extract recurring themes, pain points, and positive feedback per segment
  • Sentiment Scoring: Assign sentiment scores (-1 to +1) for overall satisfaction per segment
  • Impact Assessment: Prioritize insights by frequency, severity, and business impact
  • Synthesis: Create segment profiles with consolidated insights

Output Structure

For each identified segment:

Segment Profile

  • Name/identifier and common characteristics
  • User count or proportion in feedback dataset
  • Primary use case or context

Jobs-to-be-Done

  • Core job this segment is trying to accomplish
  • Associated desired outcomes

Sentiment Score & Satisfaction Level

  • Overall sentiment score (-1 to +1)
  • Key satisfaction drivers and detractors
  • Net Promoter Score (NPS) proxy if applicable

Top Positive Feedback Themes

  • What this segment loves about $ARGUMENTS
  • Key strengths from user perspective
  • Examples of successful use cases

Top Pain Points & Criticism

  • Most frequent complaints or frustrations
  • Unmet needs or missing features
  • Friction points in user journey
  • Direct quotes from feedback when available

Product-Segment Fit Assessment

  • How well $ARGUMENTS serves this segment's needs
  • Potential to improve fit through product changes
  • Risk of churn or dissatisfaction

Actionable Recommendations

  • 2-3 highest-impact improvements per segment
  • Quick wins vs. strategic initiatives
  • Segments to prioritize or de-prioritize

Best Practices

  • Ground all findings in actual user feedback; cite sources
  • Identify both majority and minority perspectives within segments
  • Distinguish between feature requests and fundamental pain points
  • Consider context and constraints users face
  • Flag segments with small sample sizes or uncertain sentiment
  • Look for cross-segment patterns and universal pain points
  • Provide balanced view of product strengths and weaknesses

Further Reading

How to use it

Copy the folder

Take phuryn/sentiment-analysis from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.