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.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/phuryn/pm-skills --skill sentiment-analysis- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads sentiment-analysis automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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
FAQ
What does the sentiment-analysis skill do?
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.
How do I install sentiment-analysis?
Run npx skills add https://github.com/phuryn/pm-skills --skill sentiment-analysis in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/sentiment-analysis/ (personal) or .claude/skills/sentiment-analysis/ (project).
Which AI agents can use sentiment-analysis?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is sentiment-analysis safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains sentiment-analysis?
It is published by phuryn in phuryn/pm-skills. It has 3,079 recorded installs.
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